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Streamlining the Chatbot Development Life Cycle with AI Integration

In today’s fast-paced digital world, chatbots have become essential for businesses, enhancing customer engagement and streamlining operations. Integrating AI into the chatbot development life cycle can significantly improve their functionality, making them smarter, more responsive, and efficient. This guide will explore the stages of the chatbot development life cycle, the tools and technologies involved, and provide insights into creating an AI-powered chatbot.

Understanding the Chatbot Development Life Cycle

Approximately 1.4 billion individuals currently use chatbots. The chatbot development life cycle encompasses several key stages, each crucial for building an effective chatbot.

Requirement Analysis and Planning

The journey begins with understanding the needs and expectations of the users. Imagine a company that wants a chatbot to handle customer service inquiries. They need to:

  • Identify the chatbot’s purpose.
  • Define the target audience.
  • Set clear objectives and goals.
  • Outline key functionalities and features.

For instance, they might decide the chatbot should answer common questions, guide users through the website, and handle basic troubleshooting.

Designing the Chatbot

Designing a chatbot involves creating the conversational flow and user interface. Picture a creative team brainstorming the chatbot’s personality. They decide it will be friendly and helpful, reflecting the company’s brand. They then craft the conversation script, design the user interface, and create a personality for the chatbot. The result is a chatbot that feels like a natural extension of the company’s customer service team.

Choosing the Right Tools and Platforms

Selecting the right tools and platforms is crucial. Here are some popular choices:

  • Dialogflow: Powered by Google, it’s great for complex conversational flows with robust natural language processing (NLP) capabilities.
  • Microsoft Bot Framework: A comprehensive framework supporting multiple channels like Skype, Slack, and Facebook Messenger.
  • Rasa: An open-source framework that allows for highly customizable chatbots with advanced NLP capabilities.

Each platform has its strengths. Dialogflow excels in ease of use, Microsoft Bot Framework is perfect for extensive integrations, and Rasa offers high customization. For businesses seeking tailored solutions, MOCG generative AI consulting can further enhance the capabilities of these platforms, providing specialized expertise to meet unique requirements.

Development and Integration

Now comes the actual building of the chatbot. Imagine a team working together, coding, and integrating the chatbot with various platforms. They might choose Rasa for its flexibility. The team sets up the environment, trains the model, and starts running the chatbot. They then integrate it with Slack, allowing users to interact with the chatbot directly within their preferred communication tool.

Testing and Debugging

Testing is a critical phase. Think of a meticulous QA team putting the chatbot through its paces. They conduct:

  • Unit Testing: Ensuring each part of the chatbot works individually.
  • Integration Testing: Making sure all parts work together seamlessly.
  • User Acceptance Testing (UAT): Gathering feedback from real users.

The goal is to iron out any kinks and ensure the chatbot provides a smooth user experience.

Deployment

Deploying the chatbot involves making it available to users on chosen platforms. Picture the excitement as the team finally releases their creation to the world. They might use cloud services like AWS or Google Cloud, or opt for containerization with Docker or Kubernetes, ensuring the chatbot is scalable and robust.

Maintenance and Improvement

The work doesn’t stop after deployment. Continuous monitoring and improvement are essential. Imagine a dedicated team analyzing user interactions, fixing bugs, and adding new features. They use tools like Google Analytics to track performance and Mixpanel to understand user behavior. This ongoing process ensures the chatbot remains relevant and effective.

AI Integration in Chatbot Development

Integrating AI into the chatbot development life cycle enhances its capabilities in several ways:

Natural Language Processing (NLP)

AI-powered NLP allows chatbots to understand and process human language more effectively. For instance, a customer might ask, “Can you help me track my order?” The chatbot recognizes the intent (tracking an order) and the entity (the order itself), providing a precise response.

Machine Learning

Machine learning algorithms enable chatbots to learn from interactions and improve over time. Imagine a chatbot that gets better at answering questions as it interacts with more users, thanks to supervised learning and reinforcement learning techniques.

Sentiment Analysis

AI-powered sentiment analysis helps chatbots understand the emotions behind user input, enabling more empathetic responses. For example, if a user expresses frustration, the chatbot can offer a soothing and helpful response, improving customer satisfaction.

Case Study: Building an AI-Powered Customer Support Chatbot

Let’s walk through a practical example of building an AI-powered customer support chatbot.

Step 1: Creating the Concept

Picture a company deciding they need a chatbot to handle customer support inquiries. They outline the chatbot’s purpose, target audience, and key functionalities.

Step 2: Designing the Experience

The creative team designs a friendly, helpful chatbot that aligns with the company’s brand. They draft conversation scripts, design the user interface, and give the chatbot a personality that resonates with users.

Step 3: Choosing the Platform

The team opts for Dialogflow due to its robust NLP capabilities and ease of integration with their existing systems.

Step 4: Building the Chatbot

The development team sets up Dialogflow, creates intents for common customer inquiries, and integrates the chatbot with the company’s website. They work diligently, ensuring the chatbot understands and responds accurately to user queries.

Step 5: Testing and Refining

The QA team rigorously tests the chatbot, ensuring it handles various scenarios smoothly. They gather feedback from beta users and make necessary adjustments to improve the user experience.

Step 6: Going Live

With everything in place, the team deploys the chatbot on the company’s website. Customers can now interact with the chatbot, receiving instant support for their inquiries.

Step 7: Continuous Improvement

Post-launch, the team continuously monitors the chatbot’s performance, making updates and improvements based on user feedback and interaction data. This ongoing process ensures the chatbot remains a valuable tool for customer support.

Trends in AI-Powered Chatbot Development

Based on Research by SlashData – 15% of ML/DS developers use machine learning to build new ML or AI-based products, applications or chatbots. The integration of AI in the chatbot development life cycle is continuously evolving. Here are some trends to watch:

Conversational AI

Conversational AI aims to create more human-like interactions by understanding context and generating natural responses. This means chatbots are becoming more sophisticated, capable of engaging in more complex and meaningful conversations.

Multilingual Chatbots

With global businesses, there is a growing demand for chatbots that can converse in multiple languages. This trend is driven by the need to provide support to a diverse customer base.

Voice-Enabled Chatbots

Voice-enabled chatbots are gaining popularity, providing users with a more intuitive and hands-free experience. As voice technology improves, we can expect to see more businesses adopting voice-enabled chatbots.

Hyper-Personalization

AI enables chatbots to provide highly personalized responses based on user data and behavior. This trend is leading to more tailored and relevant interactions, enhancing the overall user experience.

Future-Proofing with AI

As AI technology advances, the chatbot development life cycle will become more streamlined, with AI handling more complex tasks and reducing development time. Businesses that embrace these advancements will be better positioned to provide superior customer experiences and stay ahead of the competition.

Conclusion

Integrating AI into the chatbot development life cycle significantly enhances the capabilities and efficiency of chatbots. By following a structured approach and leveraging the right tools and technologies, businesses can create powerful, intelligent chatbots that drive engagement and deliver exceptional value to users. This journey, from planning to continuous improvement, highlights the dynamic and transformative nature of AI in chatbot development.

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AIoT- Bringing Together The Powers Of AI And IoT Technology

AIoT

It’s unimaginable to think machines mimic user behavior and make intelligent decisions faster than human beings. Thanks to Internet of Things (IoT) technology, which can sense and collect data quickly, and Artificial Intelligence (AI) technology, which can analyze data in less than a second. Incredible technologies are changing the world with innovative use cases.

The blend of these two technologies, AIoT (Artificial Intelligence of Things), revolutionizes every industry vertical with unique benefits. Turning Sci-fi into reality, AIoT applications and extensive benefits take connected world concepts beyond imagination, which we will discuss in detail in the blog. Let’s dive in!

What is AIoT?

AIoT unites two advanced technologies, AI and IoT, enabling systems to collect data and drive insights at scale. Leveraging machine learning, deep learning, and neural networks, AIoT manages and interprets data to identify patterns, anomalies, and new trends that are impossible for the human brain in a matter of seconds. Hence, connecting with one of the top AI companies enables AIoT systems to be made more responsive and efficient.

AIoT works with AI integration in infrastructure that is connected with IoT networks. It works in two modes- cloud-based AIoT and edge-based AIoT. Cloud-based AIoT manages and processes data collected from IoT devices through cloud platforms. On the contrary, edge-based AIoT involves data collected from IoT devices moving into processing at the edge, which reduces excessive data movement.

Either way, AIoT is deployed, the immense potential of AIoT increased its market size to $9.53 billion in 2023 and is projected to grow to $46.39 billion by 2030 at a CAGR of 9.53%—the continuous advancements bring abundant business opportunities to the table.

What are the benefits of AIoT?

The blend of transformative forces is delivering an array of advantages spanning across various industry verticals. The benefits that businesses will reap with AIoT use cases are as follow.

Improve operational efficiency

IoT devices generate massive operational data that ML tools analyze using computational prowess. The real-time analysis optimizes resource allocation with operational insights and issues identification and enables task automation of repetitive jobs.

This way, businesses can provide better services as AIoT handle repetitive jobs. Also, vision-based quality control of automated tasks ensures operations are optimized according to government norms, thereby guaranteeing operational efficiency.

Minimize expenses and maximize savings

AIoT acts like a crystal ball for machines with its predictive maintenance. Continuous monitoring ensures the machines or equipment get required maintenance ahead of breakdown, which reduces downtime. Also, no human involvement and remote monitoring help avoid costly repairs, which translate into huge savings.

Additionally, AIoT ensures efficient utilization of resources that eliminates unnecessary expenses. Smart buildings with light and temperature auto-control based on occupancy help save resources and increase savings.

Foster informed decision-making

As AIoT collects data using IoT and analyzes it using AI technology, it uncovers valuable insights hidden in plain sight. Thereby, businesses can make data-driven decisions that further enhance competitiveness. For example, the healthcare industry uses AIoT systems to analyze patient’s health histories before prescribing treatment plans.

Another example in the retail sector is that inventory data collection by AIoT systems determines which products are selling fast and which aisles need to be restocked immediately. The data-backed decisions eliminate out-of-stock inventory that delivers the best customer experience.

Hyper-personalization to achieve perfection

As personalized experience has become a need of the hour for every B2C or B2B business, AIoT implementation is gaining traction. AIoT devices help offer tailored experiences with intelligent, customized recommendations. Consider personalized suggestions for movies, TV shows, and others by leading streaming services such as Netflix, Disney+ Hotstar, and others. The content recommendations allow customers to get suggestions according to their preferences.

Smart homes intelligently analyze users’ preferences for temperature, and after collecting climate data, light and temperature are adjusted accordingly.

Safety is rest assured

AIoT-powered systems are performing great in surveillance or threat detection, thereby contributing to the security of the home and city. In smart homes, AIoT technology can identify actual threats and false alarms with its great sensing power. Smart buildings use technology for video surveillance that recognizes images in real-time and detects unexpected scenes.

For example, Walmart has installed AIoT-powered video surveillance cameras that utilize image recognition technology at checkouts to find out about thefts. Weapon detection or intrusion event detection is also possible with AIoT integration.

Real-world AIoT applications transforming various industry verticals

The real-life examples of AIoT help you know how businesses tap the potential of the technology and cherished grandeur success. They are:

  • Amazon Go concept is made possible with AIoT systems wherein IoT devices scan the items that are added to the cart and purchased by the users. Later, they auto-deduct the amount from their digital wallet as the users move out of the store.
  • Alibaba Cloud built an AIoT-driven ET city brain system to maximize China’s metropolitan public resources. The system collects and processes data streams to report accidents rapidly, auto-adjust traffic signal time, and reduce ambulance arrival time.
  • Tesla autonomous cars have ultrasonic sensors, external cameras, and onboard computers, which are followed by deep neural networks to analyze data. The self-driving capabilities of the vehicles will bring hands-free driving in the near future.
  • London City Airport integrated AIoT everywhere, which allows monitoring every aspect of the facility, including cabin crew checking passengers’ location, gate information updates, and other activities. Also, passengers can instantly check flight status and other information accurately.

What does the future hold for the fusion of two technologies in AIoT?

AIoT is one of the latest AI trends that is reshaping the world with IoT potential. From oil and gas to manufacturing and retail, the sectors are transforming with innovative use cases of AIoT technology. The latest developments and epic success driven by AIoT are making businesses adopt AIoT and stay ahead of the game.

Stand at the convergence of IoT and AI technology to leap forward and make your business future-proof. Also, dipping your toes in AIoT app development with expert AI developers is a good way to get started. Partner with a reliable AI development company right away to experience a paradigm shift with AIoT. 

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Automating Industries: How Robotics and AI Are Transforming Manufacturing

How Robotics and AI Are Transforming Manufacturing

The future of manufacturing is smart, intuitive, and highly efficient, all thanks to robotics and AI. Automated factories are nothing new, as digital upgrades have been occurring for quite some time. However, with recent technological advances leading to smart robotics, artificial intelligence, and machine learning, the industry is rapidly moving forward by leaps and bounds. 

These technologies are drastically transforming factory settings, leading to significant improvements in quality control, safety, and supply chain optimization. As this transformation continues, it highlights a future where AI and robotics drive further advancements and innovations, leading to superior efficiency and unimaginable capabilities.

What Are the Benefits?

Automation driven by robotics and AI can produce a number of benefits that can help further the manufacturing industry, including:

  • Improving worker safety
  • Lowering operational costs
  • Reducing factory lead times
  • Higher and faster ROI
  • Increased competitiveness
  • Greater consistency
  • Better planning
  • Increased output

For those companies that are looking to reduce their carbon footprint, AI and robotic automation can also help lower environmental impact. In essence, using AI and robotics helps factories run more efficiently, which means less usage of energy and resources that contribute to waste and pollution. 

How Robotics and AI Are Transforming Manufacturing

AI-powered systems run on machine learning programs that essentially tell robots, machines, and equipment what to do, when to do it, and how best to do it. In other words, they are designed to learn the best way to perform a task, especially repetitive tasks, which can help eliminate otherwise wasted time, money, and resources. This ultimately leads to overall operational optimization, which can boost efficiency and lead to greater productivity. 

Below are some of the most common ways AI and robotics are being used today to transform factory settings.

Smart Automation

Again, AI-powered systems are embedded with deep learning programs and neural networks that enable them to work optimally with little to no human intervention. This allows human workers to focus their attention on other more important tasks that do require a human touch, while the robots take care of everything else. This allows for numerous factory operations to be automated, which can reduce human error and cycle times, and speed up production processes. 

Quality Control

Consistency is key to success in the manufacturing industry. This means machines and equipment that are consistently working as they should to help produce consistent and high-quality products. This is where AI steps in. 

AI-powered computer systems can monitor input from various factory robots and equipment, keeping an eye out for any anomalies or issues that could impact production. For example, many AI-powered systems in manufacturing settings now use analysis for predictive maintenance, which helps avoid breakdowns and malfunctions that could lead to delays or shutdowns.  

Layout Optimization

The way the factory floor is laid out and organized can also play a role in production output and efficiency. If things are not laid out as optimally as possible, for example, it can make it harder for a worker to do their job. 

AI-powered tools and sensors, however, can analyze layouts and suggest a better plan to improve efficiency and reduce issues with safety, space, and materials. If a short-run project arises, these systems can also suggest a temporary reconfiguration to better suit the needs of the project. 

Even after the factory is set up, sensors can continuously monitor and make suggestions to improve process flow based on how things are running. 

Generative Design

Generative design using AI allows engineers to input a set of requirements for a product, such as the parameters and design goals, and then the AI uses that information to test out hundreds, even thousands, of different iterations until the best option is found. This is something that could normally take months or years for a person to do themselves, but AI can do it much faster, which allows companies to create the most optimally designed product in a shorter amount of time. 

Current Case Studies for AI and Robotics

Already, hundreds of companies around the globe are using AI and robotics to improve manufacturing processes. 

Siemens’ (SIEGY) has adopted Microsoft’s OpenAI Service to harness generative AI design to help a number of industrial companies drive efficiency and innovation across their manufacturing processes.  

DHL is deploying Boston Dynamic’s Stretch Robot to optimize its supply chain, using it to help grab and move packages. 

Rockwell Automation’s Smart Manufacturing software uses AI to equip factories with the insights needed to optimize risk management, quality, productivity, and sustainability. 

Walmart recently built a 1.4 million-square-foot facility in Florida, their first automated distribution center powered by AI supply chain technology from the company, Symbiotic. 

NVIDIA provides a number of AI solutions to the industrial sector, including IGX Origin, a platform that provides predictive maintenance, robotics, and industrial inspection solutions. 

In the United States alone, manufacturing companies have heavily invested in smart automation installations in recent years, including 44,303 robotic units in 2023. These numbers are expected to continue growing as more companies look to these technologies to upgrade their factories and improve efficiencies. 

Challenges to Consider

While smart automation technologies have the power to positively transform the manufacturing industry, using AI and robotics still comes with challenges. The ethical implications of AI are important to consider, for example. 

In any setting where you have human-robot collaborations or human-AI collaborations, it’s necessary to understand the ethical challenges, such as safety, communication, and job displacement. 

While robots are often used to make factory settings safer, if workers do not understand how to properly interact with these systems, it can end up causing more safety problems. This is where thorough training plays an important role. If companies intend to integrate robots, they must first make sure their workers fully understand how to use and interact with these advanced systems. 

For example, many robots have user interfaces that workers can use to access and adjust settings, however, if workers don’t have any programming knowledge, they could struggle without proper training. Additionally, factory robots can also sometimes be controlled using voice commands, but if workers don’t adapt to these voice controls, it will make it difficult for them to work in the same area as the robots.  

Another challenge that can arise is low morale due to fear of job displacement. When integrating AI-powered systems and robots, it’s crucial that companies alleviate these fears by assuring their workers that these systems are meant to help them do their jobs better as opposed to replacing them entirely. 

Cybersecurity Concerns

Another challenge worth noting is potential cybersecurity issues. As processes and systems become more digitally connected and intertwined, it makes them more susceptible to cyberattacks. For example, while rare, industrial robots can be hacked as can other AI-powered systems. 

To avoid these risks, manufacturers must take steps to increase cybersecurity awareness among workers and implement advanced cybersecurity protocols. This means training workers on how to use these systems without putting any sensitive data at risk and even limiting who has access to controls. It also means using device-hardening protocols and end-to-end encryption to protect data. 

Keeping up with the latest software updates and firmware patches is also important to reduce system attacks, as well as conducting regular cybersecurity risk assessments. If manufacturers don’t want to handle this themselves, they can hire vendors who can monitor cybersecurity threats for them and distribute updates and patches as needed.   

Final Thoughts

While there are risks associated with the adoption of any new advanced system, the pros generally outweigh the cons. So long as companies are smart about how they integrate AI and robotic systems, these technologies have the power to lead to greater efficiency and production output. It will also help companies stay competitive in an evolving digital landscape. 

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Developing AI-Assisted Software with TensorFlow and Keras

AI-Assisted Software with TensorFlow and Keras

In very simple words, AI-assisted software uses artificial intelligence to perform tasks that normally require human intelligence. You might be wondering what human skills can be replaced with AI, so here is the answer to your questions. AI-assisted software can recognize speech, make decisions really fast, and understand natural language.

From all this, you can guess why it’s such an important innovation. It fundamentally changes how we operate in different fields, making our work smooth and more efficient. AI also improves accuracy and decision-making in various fields. For example, one can apply it in healthcare for diagnosing diseases, in finance for fraud detection, and in customer service for chatbots.

You Main Helpers: TensorFlow and Keras

If you want to dig deeper into the topic, then check out two powerful tools for AI development: TensorFlow and Keras. We’ll talk about them below, but for starters TensorFlow is an open-source platform for machine learning and  Keras is a user-friendly neural networks API that runs on top of TensorFlow. What’s so great about them for developers? These two frameworks make it easier for tech specialists to build and train AI models, which are so helpful.

Getting Started with TensorFlow and Keras

Before you reap all the benefits of these tools, you have to understand how to install them properly. We are not going to leave you alone with it, let us guide through this complicated process. 

1. Install Python

You might not understand what’s the point of this step but it’s very important. Here is why: TensorFlow and Keras are libraries built for Python, which is a programming language commonly used for AI and machine learning. That’s why you can’t use these great tools without installing Python on your computer first. Basically, it is an essential component to run these libraries and write your AI programs.

2. Install pip

The next step is also related to Python. You have to make sure that you have pip on your laptop as it simplifies the process of managing and installing Python packages. Thanks to pip, you can be calm knowing that you have the latest versions of essential packages and all the required dependencies. So, don’t skip this stage even if you feel tempted to do it.

3. Create a Virtual Environment

Unlike the previous necessary step, this one is optional. You can skip it if you want even though we don’t recommend doing it. To give you a deeper insight into this stage, let’s define a virtual environment. In short, it’s a space where you can install Python packages without affecting your system-wide Python setup. Why is this so important? 

Because this helps to manage dependencies and avoid conflicts between different projects. If it sounds too theoretical for you, let’s consider an example. Imagine that you have multiple projects at the same time and each of them requires  different versions of the same library. Virtual environment can come in handy in this case it ensures each of your projects has its own dependencies and versions.

4. Install TensorFlow

Now, let’s move to the most interesting part and the essence of this section, which is installing TensorFlow. Among the benefits of this tool, you get access to its powerful features for building and training machine learning models needed for your software. If you don’t want to handle all the  heavy lifting of complex mathematical computations, then go for this solution. All the experts say that it provides an extensive library of pre-built models and algorithms needed for their professional purposes. 

5. Install Keras

Now it’s time for Keras. This is a high-level neural networks API that runs on top of TensorFlow and simplifies processes. The point of Keras is that it builds and trains neural network models with  an intuitive and user-friendly interface. Obviously, this is one of the main things when it comes to user experience, so you can greatly benefit from Keras, no matter whether you are an experienced developer or a beginner. 

6. Verify the Installation

Verifying the installation ensures that TensorFlow and Keras have been installed correctly and are ready to use. By importing these libraries and checking their versions, you confirm that your setup is complete and functional. This step helps to catch any installation issues early on, ensuring a smooth start to your AI development journey.

Building Your First AI Model with TensorFlow and Keras

You must be scared to even think about such a big task ahead. But trust us, it’s going to be a fun and rewarding journey. And remember, there is only one way to eat an elephant: one bite at a time.

1. Setting Up Your Environment

Logically, you’ll need to set up your environment first. You should check if TensorFlow and Keras are installed properly and ready to use. 

2. Understanding Key Concepts

If you don’t understand basic concepts, you’ll be helpless. So let’s make sure you know what we are talking about. 

– Layers: Layers can be perceived as building blocks of your neural network. To dig deeper into the structure, each layer has neurons (or nodes) that process the input data and pass it along. If it’s still not clear enough, you can think of layers like a conveyor belt in a factory, where each station does something different with the product (your data) as it passes through.

– Neurons: If we continue to expand this metaphor, neurons are the workers at each station on our conveyor belt. As you may guess, their function is to receive input, apply a weight to it, and then pass it through an activation function to produce output.

– Activation Functions: Here is where things become more complicated. These kinds of functions introduce some complexity to our model because it’s their job to decide which neurons to activate based on the input they receive. Common ones are ReLU, sigmoid, and tanh. 

– Training: Please, don’t be fooled by the familiarity of this word because now, this is where the magic happens. Training is when your model learns from the data. It works the following way: you feed the data into the model, it calculates the error (the difference between what it predicted and the actual answer), and then it adjusts its weights to get better. This process is repeated many times (called epochs) until the model performs well enough to be functional.

3. Building the Model

Alright, now let’s end with terms and build our model. As a first step in this exciting process, you need to start by defining the structure of your neural network. And now it must become clear why we mentioned Keras. By using it, you stack layers and specify the type and number of neurons in each layer. If we were to provide a metaphor again, it’s like sketching out a blueprint for a building.

4. Compiling the Model

The next step brings you much closer to a ready software. Here, you have to compile the model so that it’s ready for training. To add details, this is where you specify:

  • the loss function (to measure error)
  • the optimizer (to adjust weights)
  • metrics (to evaluate performance).

5. Training the Model

Now it’s time to train the model you’ve carefully created in the previous steps! You’ll use your dataset, splitting it into training and validation sets. What is happening during training? In simple words, the model adjusts its weights to minimize the loss function. You might think that it’s the end of your developing journey, but it’s not true. There is one more important step left.

6. Evaluating the Model

Finally, you evaluate your model’s performance using test data. Otherwise, you never know if the model generalizes well and performs accurately on new, unseen data. 

Conclusion

AI-assisted software development with TensorFlow and Keras can deeply transform human experiences. This way, we can create intuitive, engaging, and accessible applications that truly resonate with users. As you venture into AI development, remember that you’re making the digital world a more connected and inclusive place. So, dive in with enthusiasm!

Author’s BIO

Dan Mathews is a seasoned AI developer and technology enthusiast. His main talent is to combine technical expertise with a deep understanding of user behavior. When he’s not coding, Dan offers dissertation writing services as he strives to help students articulate complex ideas. His dedicated work continues to inspire and empower others to create meaningful technological solutions.

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Everything You Need To Know About AI Tech Stack

AI Tech Stack
AI Tech Stack

AI Tech Stack: Explained In Detail

Over a narrow span, AI technology experienced a paradigm shift from novelty to an all-imperative aspect for businesses. With exponential growth in AI solution development, businesses are trying to maintain a pace with evolving AI tech stack, ensuring the adoption of the latest AI trends.

Before stepping in it’s essential to understand the AI tech stack, the technical breakdown of the AI tech stack, the stages of AI tech stack development, and how AI development companies select the best one. Let’s walk through all of them to ensure AI solutions are built using the advanced AI tech stack.

A brief overview of the AI tech stack

The AI tech stack is a structural framework that’s created with a layered approach and comprises components such as APIs, ML algorithms, data processing, data storage, visual data recognition, and data ingestion. The three layers- application layer, model layer, and infrastructure layer act as a foundation of the AI tech stack.

AI tech stack architecture includes multifaceted frameworks that provide programming paradigms that easily adapt AI technology evolutions. Vertex AI, LangChain, Fixie, and Semantic Kernel are the popular frameworks leveraged by AI engineers to build AI solutions quickly.

Technical breakdown of AI tech stack

The overview of the AI tech stack determines the importance of every component and element, which enables the creation of the best AI tech stack. Here’s the breakdown:

·        Machine learning frameworks: ML frameworks such as Keras, TensorFlow, and PyTorch provide a range of tools and APIs enabling ML model creation that are necessary for AI training and interference.

·        Programming languages: Python, R, and Julia are widely used programming languages for creating complex functionalities such as high-performance computational tasks, statistical analysis, etc. that are highly accessible.

·        Cloud services: Cloud services such as AWS, Azure, GCP, or other integrations provide ML platforms and configurable resources. Scalability ensures AI solutions perform to the notch despite variations in workload.

·        Data manipulation utilities: Data normalization, encoding, and preprocessing are important, and they are enabled using Hadoop, an Apache-like data manipulation utility. It helps to manage huge datasets and to analyze data to uncover valuable insights.

Different phases of building AI tech stack

For effective development and deployment of AI solutions, the layered AI tech stack is divided into two phases followed by multiple stages, which we will discuss in detail.

Phase 1: Data management

As data is the crux of ML algorithms and impacts decision-making, data handling is vital. Data management involves data acquisition, transformation, storage, processing, and monitoring.

Stage 1: Data acquisition

·        Data aggregation: Data collection involves moving through databases and writing queries to extract data. The data is further analyzed to gain actionable insights.

·        Data annotation: Manual labelling or auto-labelling using tools like- ImgLabs or V7Labs helps with data labelling so that ML solutions can identify the relationships among data in a supervised environment.

·        Synthetic data generation: When the data is not available for specific use cases, the data is generated using different libraries (SymPy and Pydbgen) and tools (Tensorflow and OpenCV) supporting data generation from images, texts, tables, and others.

Stage 2: Data transformation and storage

·        Data transformational mechanism: Data transformation is enabled in two types- ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). The former is a traditional method that uses data processing as a priority, and the latter is preferred when data preservation and faster processing are required.

·        Storage modalities: Three types of data storage facilities are available based on data volume, interaction frequency, and data structure. Data lakes store unstructured data and organize them in a flexible format, while data warehouses store and process structured data across multiple touchpoints. Databases store and process structured, filtered data, which is good for interactions.

Stage 3: Data processing

·        Analysis: This stage converts raw data into meaningful data that Machine Learning models consume. NumPy, Pandas, and Apache Spark are the popular libraries used for data analysis at speed. Business intelligence tools provide business insights that are useful during stakeholder interactions.

·        Features handling: Feature store solutions (Iguazio, Tecton, Feast, and Hopsworks) make invaluable contributions to feature storage, computing, management, and versioning across ML solutions.

Stage 4: Data versioning lineage

Continuously changing and updating data makes it difficult to generate results unless data is versioned optimally. DVC is a popular data versioning tool that’s language-agnostic and enables seamless integrations with data, code, files, and storage. Data lineage helps view data version evolution over time and find out the logical connections between every data touchpoint.

Stage 5: Data monitoring

Data surveillance is essential to identify whether the data passed to ML models is flawless. Automated monitoring tools such as Censius, Fiddler, etc, help monitor millions of data points to check quality issues or abnormalities. Conceptual pattern and traffic monitoring through intelligent tools ensures data is completely error-free.

Phase 2: Model architecting and performance metrics

Data management and modelling are cyclic, wherein developers move back and forth to make changes and get optimal results. Model development starts with data gathering, storage, analysis, and transformation into usable form. After that, various aspects of the process are involved, from algorithm selection to final evaluation.

·        Algorithm selection: Every ML library has its strengths and offers a range of advantages, including customization level, speed, adoption, and flexibility. Post-library selection and model-building activities are executed.

·        Integrated Development environment: IDE facilitates code, compiler, debugger, and integration of other features that are essential for software development. PyCharm, VS code, Jupyter, and MATLAB are the popular IDEs leveraged at scale.

·        Tracking: AI solution development involves experimenting with feature combinations, models, and data to find the best result. These experiments are executed multiple times and tracked using tools like MLFlow, Neptune, and Layer for faster analysis and selection.

·        Evaluation: The results of different experiments are monitored and compared using AI tools. Correlating performance evaluations helps find the root cause of issues.

Phase 3: Model Deployment

The deployment phase ensures the solution becomes available to end users and is automated so that no incompatibility issues exist.

Stage 1: Model serving

Model serving enables AI solutions to be hosted by different hosting service providers. It ensures that end users can access the application. Model serving tools such as Cortex, TensorFlow Serving, Seldon, and Torchserve have multiple options to ease production.

Stage 2: Resource virtualization

It supports the isolated environment and experiments for model training and deployment. Virtual machines and containers help best manage development and deployment activities. 

Stage 3: Model testing

Model testing helps filter all the issues across various environments and containers, ensuring the right model reaches the customers. Testing tools compatible with a range of infrastructures enable faster testing.

How do you select the best AI tech stack?

The AI tech stack is overwhelming for beginners, but connecting with one of the top AI companies helps you create the best tech stack. However, consideration of a few criteria and milestones allows businesses to select the right AI tech stack.

·        Specifications for functionality and technology: The number of features and their complexity determine programming languages, frameworks, libraries, tools, and APIs to select. Data modality, computational complexity, scalability, and execution speed must be evaluated to determine tech stack specifications.

·        Strategic selection of assets: Resource availability plays a vital role in AI tech stack selection. So, tech stack selection must be strategic and based on team expertise, resource accessibility, budget, and maintenance complexity.

·        Scalability is important to consider: Adaptability is key in AI applications, so the AI tech stack must be scalable, ensuring longevity and high performance. 

·        Security and compliance can change the game: Critical data handling and management in a secure data environment require nation-specific compliances to be followed. Data integrity, authentication mechanisms, infrastructure defence, and regulatory adherence are paramount, ensuring data remains safe forever.

Partner with the reliable AI development company

Building scalable, dynamic AI solutions rests on the shoulders of a powerful AI tech stack that further helps businesses stay current and stand out in the competition. Building a robust AI tech stack requires connecting with the top AI companies with rich expertise and experience in AI solution development, leveraging the right mix of AI tools, techniques, and libraries. Collaborate with the right partner to create futuristic AI solutions. 

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Community

From Rendering to AI: 5 Reasons Why You Can Consider an NVIDIA GPU Dedicated Server?

Consider an NVIDIA GPU Dedicated Server

Computing power is no longer a luxury, it’s a need. Whether you’re a creative professional pushing the boundaries of animation or a scientist address complex copy, having the right tools at your disposal can make all the difference. This is where NVIDIA GPU dedicated servers come in, offering a hard join of processing power and creativity that can transform your workflows.

But what exactly are NVIDIA GPU dedicated servers, and why should you consider them? Let’s explore into the world of GPU boost and explore five powerful reasons why incorporating an NVIDIA GPU dedicated server into your arms can empower your projects and move you towards success.

1.  Release the Power of Similar Processing:

At the heart of an NVIDIA GPU dedicated server lies the Graphics Processing Unit (GPU). Unlike traditional CPUs designed for sequential tasks, GPUs excel at similar processing. Imagine a highway with multiple lanes – a CPU funnels tasks through one lane at a time, while a GPU utilizes numerous lanes simultaneously, significantly accelerating computations.

This similar processing skill makes NVIDIA GPU dedicated servers ideal for applications that involve massive datasets and complex calculations. Tasks like:

  • 3D Rendering and Animation: Render hard scenes with breathtaking detail in a spot of the time compared to CPU-based systems.
  • Video Editing and Encoding: Edit high-resolution footage seamlessly and encode videos for various platforms at blazing speeds.
  • Scientific Simulations: Run complex simulations involving solution dynamics, weather patterns, or protein folding with unparalleled efficiency.
  • Deep Learning and AI: Train deep learning models faster and develop cutting-edge AI applications that leverage vast amounts of data.

2. Unmatched Performance for Demanding Workloads:

NVIDIA GPUs boast thousands of cores specifically designed for processing massive datasets and performing complex calculations. This translates to real-world benefits for users. With an NVIDIA GPU dedicated server, you can:

  • Reduce project turnaround times: Get results quicker, repeat faster, and meet deadlines with greater ease.
  • Handle larger and more complex projects: Take on hard attempt that would strain traditional CPU-based systems.
  • Increase productivity and efficiency: Focus on your core tasks while the server tackles computationally intensive workloads.
  • Unlock new creative possibilities: Experiment with innovative techniques and push the boundaries of your field without hardware limitations.

3. Scalability to Match Your Growing Needs:

As your projects become more demanding or your business scales, your computing needs will evolve. The beauty of NVIDIA GPU dedicated servers lies in their inherent scalability. Many providers offer a range of server configurations with varying numbers and types of NVIDIA GPUs. This allows you to choose a server that perfectly aligns with your current requirements, with the flexibility to upgrade seamlessly as your workload grows.

Considering Colocation vs. Cloud-Based Solutions:

While cloud-based NVIDIA GPU dedicated servers offer a convenient and scalable option, colocation server hosting presents a powerful alternative for users with specific requirements. Here’s a breakdown of the key considerations:

  • Control and Security: Colocation offers greater control over your hardware and network environment, ideal for businesses with strict security needs.
  • Customization: Colocation allows for customized server configurations tailored to your specific workload demands.
  • Latency Sensitivity: For applications where latency is critical, such as real-time simulations or high-frequency trading, colocation can provide a lower latency connection compared to cloud-based solutions.

4. Cost-Effective Solution for High-Performance Computing:

Investing in high-performance computing hardware can be a significant upfront cost. NVIDIA GPU dedicated servers provide a cost-effective alternative. Here’s why:

  • Shared Infrastructure: Cloud-based providers offer dedicated servers with powerful NVIDIA GPUs, eliminating the need to purchase and maintain expensive hardware yourself.
  • Pay-as-you-Go Model: Many providers offer flexible billing options, allowing you to pay only for the resources you utilize. This is ideal for projects with fluctuating workloads or those in the initial stages of development.
  • Reduced Downtime: Cloud providers manage server infrastructure, ensuring minimal downtime and maintenance headaches.

5. A Future-Proof Investment for Emerging Technologies:

The world of figure is constantly develop, and NVIDIA GPUs are at the lead of this revolution. As AI, deep learning, and other data-intensive applications continue to gain rank, NVIDIA GPU dedicated servers will become even more crucial.

By investing in an NVIDIA GPU dedicated server today, you’re not just get processing power; you’re provide yourself with a platform that can adapt and grow beside these emerging technologies. This future-proofs your investment and ensures you have the tools necessary to stay ahead of the turn.

Conclusion

NVIDIA GPU dedicated servers offer a strong idea for anyone try to push their workflows and unlock new creative or scientific possibilities. From the unparalleled processing power to the cost-effective scalability and future-proof design, these servers empower users to achieve remarkable results.

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Community

Role Of AI in Transforming Customer Service

AI in Transforming Customer Service

Any business expert can tell you the importance of customer service. Technological transformation in work environments is inevitable for standing out in the competition. 

A study shows that due to satisfactory customer service, 42% of business-to-customer customers increased their interest in after-sales purchasing. On the other hand, 52% of them left the brand due to a bad customer service experience. 

Regardless of your industry, from taking orders to underwriting in the insurance industry, AI can revolutionize customer service and many other aspects. The insurance industry is the most beneficiary of AI-integrated customer service. It includes complex data management and fraud detection using a predictable machine-learning model. Artificial intelligence in insurance claims management can deliver tasks error-free and speedily. It would streamline the customer journey for claims, providing a friendly experience for insurers and policyholders.

How AI is Changing the Mood of Employees

Chatbots

Chatbots are AI-integrated, machine-learning operatives that can replace human customer service agents. Expanding a business requires handling customer queries hand in hand, which can be challenging. 

Customers dislike waiting more than 50 seconds to respond to their queries so that AI can give instant and specialized responses. This not only speeds up the process but also makes it accurate and efficient. You can train your chatbot according to your needs and general queries.

Virtual Assistants

Besides helping customers directly, AI can help customer service agents to help through repetitive tasks. Monotonous tasks like analyzing customer feedback data can be handled with AI. 

It would make positive and satisfactory changes in the work environment, boosting the overall mood of the employees.

Predictability

AI can predict consumer behavior based on their past interaction with your firm. This behavior can be used to upsell related products or services. AI can analyze all this by measuring and analyzing their social sharing, likes and dislikes, and common queries. 

Machine learning can analyze big data that would take several days into seconds. This would reduce the cost of human labor and the possibility of error.

How AI is Revolutionary For Customers

All-time active support

Human labor needs breaks on holidays or festivals. AI can work 24/7 for all the days of the year. You don’t have to update your human customer service area to more prominent people, as AI can handle multiple queries simultaneously without hallucinations.

From solving complex queries to ordering pizza, AI can be trained and customized according to your needs. This would also break the language barrier and improve international customer service.

Understanding Customer’s Needs

People need clarification when making big decisions, especially if they are involved in changing insurance firms or investing in stocks. AI can provide scientific and rational reasons to act in a certain way, making the custom journey smoother and more beneficial. 

It can also provide detailed comparisons of different products, analyze the consumer’s specifications, and analyze specific trends to provide customers with the best seasonal hot deals.

NLP Analysis

Natural language processing (NLP) is a critical component of customer service. Its importance can be measured by the fact that Google uses NLP to analyze the pages, position them to analyze pages, and give them positions on search engines’ result pages. 

AI can effectively measure the customer’s tone and specific keywords in voice intelligence. Sentiment analysis of customer feedback is critical in assessing what your customers think of your services or products.

Benefits of using AI in Customer Service

Cost Effectiveness

Replacing human labor with AI can significantly reduce the cost of management. According to a survey, AI chatbots can save up to $8 billion yearly in corporate expenses. You will need fewer employees, and the cost of electricity, hardware, and buildings will be lessened.

Reliability

Top-notchTop-notch customer service is a critical factor in retaining and attracting new customers. Only businesses with the highest level of customer satisfaction would survive in this competitive market. You must integrate AI into your work environment to stay on top of this competitive customer acquisition race.

Personalization

AI can remember previous chats with users to give the desired information or recommendations based on their history. Using this personalized approach will significantly increase the customer retention ratio.

Challenges in Integrating AI in Customer Service Centres

Require Investment

Transforming the work environment can be costly. Expenses include hardware, training staff, software development costs, and more. The exact price would depend on various factors, including requirements, firm size, and location. 

However, business owners should consider the ROI, which can be huge. In addition to the financial gains, it can provide an efficient, deductive, high-quality customer experience.

Training For The New Environment

If you are new to this innovation, many challenges are ahead. AI needs extensive data to train itself for specialized tasks. Moreover, replicating the existing hardware with an integrated AI will cause tech hurdles and challenges.

Data Privacy

With every technological advancement comes the responsibility for data protection from potential threats. You must comply with all the privacy laws of your state. Rules like the General Data Protection Regulation (GDPR) can result in legal action against you if not considered.

Customer Trust

Every novelty takes time to become fully accepted in society. AI is also not the exception. Customers may be skeptical of innovation and hesitate to use AI instead of human agency. They also need time to become aware of its use. 

Efficient use can only be applicable with its basic understanding. You can give a manual or video guide describing all the potential usage of AI in customer service.

Customer Experience

User experience is crucial to the success of any software or website. It should be mobile-friendly, as more than 60% of users use mobile devices. Practical strategies like using breadcrumbs can give customers real-time guidance.

Conclusion

Satisfactory customer service is crucial for a business to survive. It increases brand loyalty and retention. Beyond merely responding to queries, it can be used creatively, like giving customers personalized gifts. 

Remember that the human touch to any technological transformation can not be ignored. You will eventually need the trained staff to maintain AI-empowered technology.

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Community

Can AI exist in a sustainable way?

AI in a sustainable way

The fictional but prescient Dr. Ian Malcom noted in 1993’s Jurassic Park, “…your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should.”  Generative AI’s rapid expansion due to the increase in size of large language models has felt something akin to genetically engineering a TRex.  It is certainly fun and exciting, but what are the consequences?  

Hardware is also beefing up those CPUs, GPUs, TPUs (all the PUs really) to support the training and distribution of those models. But just as history, and our favorite science fiction movies, have showed us, there is a cost. Of course, we’re all wary that SkyNet (T)  might emerge, (and frankly time will tell), but the more pressing matter is the consumption of electricity and water.

Addressing the AI elephant in the room

At Cisco, we’ve been baking predictive AI into our platforms for years, helping IT operations make insightful, and even proactive, decisions. Across compute, storage, and networking infrastructure, application of predictive AI and observability has been incredibly useful in helping organizations scale and optimize their actual infrastructure usage. With APIs paving the way for multi platform integration, we’re seeing wholesale Day 0 to Day N solutions that help organizations manage usage and more.

ai environment

What the research says

While these gains are exciting, the underlying machine learning technologies that support predictive AI do not have the same resource needs as Generative AI, which requires new approaches to reducing carbon footprint and overall environmental impacts

Over the last five years or so, researchers at universities like Berkeley and the University of Massachusetts saw past the horizon and started experimenting and proving methods that could be employed to lessen the energy consumption (and carbon footprint) of precursor technologies like natural language processing (NLP) to large language model (LLM). They even go as far as to prescribe both software/algorithm and hardware/infrastructure improvements to alleviate the carbon footprint created by training and using NLP and LLM. Even better, similar activities are underway to measure the impact of AI technology on water usage as well.

But, that’s not the whole story..

As of today, the true nature of AI’s impact on energy consumption is REALLY hard to actually quantify. Article after article tries to dig into the actual effect of using generative AI technologies. The challenge is that the combination of large amounts of variables (what task is being done, how is the data center setup, what processors are being used, etc. etc.) and IP secrecy (there is a LOT of money to be made here) makes reaching a true, tangible answer difficult. Not to mention, there is no way of knowing if those running LLM-based services are employing some of the proven mitigations noted above. 

The best any of the current research can come up with is energy usage comparable to an average U.S. home per year to the average mid-size country. That’s an unmanageable range which makes  understanding the actual impact and ways to mitigate difficult to identify.

So, it seems, that at least in the short term, newer AI technologies will have an increased impact on energy consumption and water usage to the possible negative detriment of the environment.

Problem solving, the developer way

 So how can AI exist in conjunction with sustainability efforts? Ah, that’s the interesting part. AI just may be the answer to its own problems. The problem that I mention above about it being difficult to figure out the impact of AI usage on energy and water consumption is being currently worked on by AI sustainability initiatives. 

In theory, the models would then be able to suggest solutions to increased water and electricity consumption. r In a slightly less sophisticated model, predictive AI elements are starting to be used to simply just turn things off. This is the simplest answer: eliminate situations where energy is generated but not actually used– and the really cool thing is AI can help us with that.

In the realm of this technological advancement, developers are bestowed with an extraordinary opportunity to make a real impact for a sustainable future.

Getting involved

Cisco’s Build for Better coding challenge, is open March 14 – April 22, 2024, and invites all Developers to harness their skills in AI, Observability, and Sustainability to make a real-world impact. Learn more and commit your code by Earth Day.

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Analysis

Machine learning developers and their data

Machine learning developers and their data

The data science (DS), machine learning (ML), and artificial intelligence (AI) field is adapting and expanding. From the ubiquity of data science in driving business insights, to AI’s facial recognition and autonomous vehicles, data is fast becoming the currency of this century.  This post will help you to learn more about this data and the profile of the developers who work with it. 

The findings shared in this post are based on our Developer Economics 20th edition survey, which ran from December 2020 to February 2021 and reached 19,000 developers. 

Before you dive into the data, our new global developer survey is live now. We have a set of questions for machine learning and AI developers. Check it out and take part for a chance to have your say about the most important development trends and win prizes.

It takes all types

The different types of ML/AI/DS data and their applications

We ask developers in ML, AI, and DS what types of data they work with. We distinguish between unstructured data — images, video, text, and audio — and structured tabular data. The latter group includes tabular data that they may simulate themselves. 

With 68% of ML/AI/DS developers using unstructured text data, it is the most common type of data these developers work with; however, developers frequently work with multiple types of data. Audio is the most frequently combined data type: 75-76% of those that use audio data also use images, video, or text. 

“Unstructured text is the most popular data type, even more popular than tabular data”

Given the most popular applications of audio data are text-to-speech generation (47%) and speech recognition (46%), the overlaps with video and text data are clear. Image data, like audio, overlaps heavily with video data: 78% of those using video data also use image data. The reverse dependence isn’t as strong: only 52% of those using image data are also video data users. The top two applications of both these data types are the same: image classification and facial recognition. These are two key application fields driving the next generation of intelligent devices: improving augmented reality in games and underpinning self-driving cars, in home robotics, home security surveillance, and medical imaging technology. 

The types of data ML/AI/DS developers work with

69% of  ML/AI/DS developers using tabular data also use unstructured text data

With 59% usage, tabular data is the second most popular type of data. 92% of the tabular data ML/DS/AI developers use is observed, while the other 8% is simulated. The two most common use-cases for this data is workforce planning — 39% of developers who use simulation do this —  and resource allocation, also at 39%.

Structured tabular data is least likely to be combined with other types of data. Although uncommon to combine this type of data with audio or video data, 69% do combine tabular data with unstructured text data. The top application of both tabular data and unstructured text is the analysis and prediction of customer behaviour. This is the sort of analysis often done on the data nuggets we leave behind when searching on retail websites — these are key inputs to algorithms for natural language and recommender systems.

Keeping it strictly professional?

The professional status of ML/AI/DS developers

The professional / hobbyist / student mix in the ML/AI/DS ecosystem

ML/AI/DS developers engage in their fields at different levels. Some are professionals, others students or hobbyists, and some are a combination of the above. The majority (53%) of all ML/DS/AI developers are professionals — although they might not be so exclusively. 

Of all the data types, audio data has the highest proportion of professional ML/DS/AI developers. 64% of ML/AI/DS developers who use this type of data classified themselves as a professional; and the majority (50%) of these professionals are applying audio data to text-to-speech generation. The high proportion of professionals in this field might be a byproduct of co-influencing variables: audio data is the data type most frequently combined with other types, and professionals are more likely to engage with many different types of data. 

Data types popular with students include image, tabular, and text data. Between 18-19% of developers who work with these types of data are students. There are many well-known datasets of these types of data freely available. With this data in hand, students also favour certain research areas. 

Image classification, for example, is popular with developers who are exclusively students: 72% of those students who use image data use it for this application, in contrast to just 68% of exclusive professionals that do. In applying unstructured text data, 38% of exclusive students are working in Natural Language Processing (NLP), while 32% of exclusive professionals are. As these students mature to professionals, they will enter industry with these specialised skills and we expect to see an increase in the practical applications of these fields, e.g. chatbots for NLP. 

“65% of students and 54% of professionals rely on one or two types of data”

Besides differences in application areas, students, hobbyists, and professionals engage with varying types of data. 65% of those who are exclusively students use one or two types of data, while 61% of exclusively hobbyists and only 54% of exclusively professionals use one or two types. Developers who are exclusively professionals are the most likely to be relying on many different types of data: 23% use four or five types of data. In contrast, 19% of exclusively hobbyists and 15% of exclusively students use four to five types. Level of experience, application, and availability of datasets all play a role in which types of data an ML/AI/DS developer uses. The size of these datasets is the topic of the next section.

Is all data ‘big’?

The size of ML/AI/DS developers’ structured and unstructured training data

The hype around big data has left many with the impression that all developers in ML/AI/DS work with extremely large datasets. We asked ML/AI/DS developers how large their structured and unstructured training datasets are. The size of structured tabular data is measured in rows, while the size of unstructured data — video, audio, text — is measured in disc size. Our research shows that very large datasets aren’t perhaps as ubiquitous as one might expect. 

“14% of ML/AI/DS developers use structured training datasets with less than 1,000 rows of data, while the same proportion of developers use data with more than 500,000 rows” 

The most common size band is 1K – 20K rows of data, with 25% of ML/AI/DS developers using structured training datasets of this size. This differs by application type. For example, 22% of those working in simulation typically work with 20K – 50K rows of data; while 21% of those working with optimisation tools work with 50K – 100K rows of data. 

Dataset size also varies by professional status. Only 11% of exclusively professional developers use structured training datasets with up to 20K rows, while 43% of exclusively hobbyists and 54% of exclusively students use these small datasets. This may have to do with access to these datasets — many companies generate large quantities of data as a byproduct or direct result of their business processes, while students and hobbyists have access to smaller, open-source datasets or those collected via their learning institutions. 

A further consideration is, who has access to the infrastructure capable of processing large datasets? For example, those who are exclusively students might not be able to afford the hardware to process large volumes of data. 

Non-tabular data is a useful measure for comparisons within categories: for example, 18% of image datasets are between 50MB-500MB, while only 8% are more than 1TB in size. The measure doesn’t, however, allow for cross-type comparisons, since different types of data take up different amounts of space. For example, 50MB of video data takes up a considerably shorter length of time than 50MB of audio data. 

The size of unstructured training data ML/AI/DS developers work with

The categorisation of the different data sizes was designed to take into account the steps in required processing power. For most ML/AI/DS developers, we expect that a 1-25GB dataset could be handled with powerful, but not specialised, hardware. Depending on the language and modelling method used, 25GB on disc relates to the approximate upper bound in memory size that this type of hardware could support. 

We see that 26% of ML/AI/DS developers using text data and 41% using video data will require specialised hardware to manage their training. The high level of specialized hardware manifests as a barrier-to-entry: data analysis on these large datasets is beyond an achievable scope without the backing of deep pockets supplying cloud-based technology support or infrastructure purchases.

Want to know more? This blog post explores where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

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Tips

Where do ML developers run their code?

In this blog post we’ll explore where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

Machine learning (ML) powers an increasing number of applications and services which we use daily. For some organisations and data scientists, it is not just about generating business insights or training predictive models anymore. Indeed, the emphasis has shifted from pure model development to real-world production scenarios that are concerned with issues such as inference performance, scaling, load balancing, training time, reproducibility, and visibility. Those require computation power, which in the past has been a huge hindrance for machine learning developers.

A shift from running code on laptop & desktop computers to cloud computing solutions

The share of ML developers who write their app or project’s code locally on laptop or desktop computers, has dropped from 61% to 56% between the mid and end of 2019. Although the five percentage points drop is significant, the majority of developers continue to run their code locally. Unsurprisingly, amateurs are more likely to do so than professional ML developers (65% vs 51%).

By contrast, in the same period, we observe a slight increase in the share of developers who deploy their code on public clouds or mainframe computers. In this survey wave, we introduced multi cloud as a new possible answer to the question: “Where does your app/project’s code run?” in order to identify developers who are using multiple public clouds for a single project.

As it turns out, 19% of ML developers use multi cloud solutions (see this multi-cloud cheat sheet here) to deploy their code. It is likely that, by introducing this new option, we underestimate the real increase in public cloud usage for running code; some respondents may have selected multi cloud in place of public cloud. That said, it has become increasingly easy and inexpensive to spin up a number of instances and run ML models on rented cloud infrastructures. In fact, most of the leading cloud hosting solutions provide free Jupyter notebook environments that require no setup and run entirely in the cloud. Google Colab, for example, comes reinstalled with most of the machine learning libraries and acts as a perfect place where you can plug and play to build machine learning solutions where dependency and compute is not an issue.

While amateurs are less likely to leverage cloud computing infrastructures than professional developers, they are as likely as professionals to run their code on hardware other than CPU. As we’ll see in more depth later, over a third of machine learning enthusiasts who train deep learning models on large datasets use hardware architectures such as GPU and TPU to run their resource intensive code.

Developers working with big data & deep learning frameworks are more likely to deploy their code on hybrid and multi clouds

Developers who do ML/AI research are more likely to run code locally on their computers (60%) than other ML developers (54%); mostly because they tend to work with smaller datasets. On the other hand, developers in charge of deploying models built by members of their team or developers who build machine learning frameworks are more likely to run code on cloud hosting solutions.

Teachers of ML/AI or data science topics are also more likely than average to use cloud solutions, more specifically hybrid or multi clouds. It should be noted that a high share of developers teaching ML/AI are also involved in a different way in data science and ML/AI. For example, 41% consume 3rd party APIs and 37% train & deploy ML algorithms in their apps or projects. They are not necessarily using hybrid and multi cloud architectures as part of their teaching activity.

The type of ML frameworks or libraries which ML developers use is another indicator of running code on cloud computing architectures. Developers who are currently using big data frameworks such as Hadoop, and particularly Apache Spark, are more likely to use public and hybrid clouds. Spark developers also make heavier use of private clouds to deploy their code (40% vs 31% of other ML developers) and on-premise servers (36% vs 30%).

Deep learning developers are more likely to run their code on cloud instances or on-premise servers than developers using other machine learning frameworks/libraries such as the popular Scikit-learn python library. 

There is, however, a clear distinction between developers using Keras and TensorFlow – the popular and most accessible deep learning libraries for python – compared to those using Torch, DeepLearning4j or Caffe. The former are less likely to run their code on anything other than their laptop or desktop computers, while the latter are significantly more likely to make use of hybrid and multi clouds, on-premise servers and mainframes. These differences stem mostly from developers’ experience in machine learning development; for example, only 19% of TensorFlow users have over 3 years of experience as compared to 25% and 35% of Torch and DeepLearning4j developers respectively. Torch is definitely best suited to ML developers who care about efficiency, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Hardware architectures are used more heavily by ML developers working with speech recognition, network security, robot locomotion and bioengineering. Those developers are also more likely to use advanced algorithms such as Generative Adversarial Networks and work on large datasets, hence the need for additional computer power. Similarly, developers who are currently using C++ machine learning libraries make heavier use of hardware architectures other than CPU (38% vs 31% of other developers) and mainframes,  presumably because they too care about performance.

Finally, there is a clear correlation between where ML developers’ code runs and which stage(s) of the machine learning/data science workflow they are involved in. ML developers involved in data ingestion are more likely to run their code on private clouds and on-premise servers, while those involved in model deployment make heavier use of public clouds to deploy their machine learning solutions. 31% of developers involved across all stages of the machine learning workflow – end to end – run code on self hosted solutions, as compared to 26% of developers who are not. They are also more likely to run their code on public and hybrid clouds. 

By contrast, developers involved in data visualisation or data exploration tend to run their code in local environments (62% and 60% respectively), even more so than ML developers involved in other stages of the data science workflow (54%).

Developer Economics 18th edition reached 17,000+ respondents from 159 countries around the world. As such, the Developer Economics series continues to be the most global independent research on mobile, desktop, industrial IoT, consumer electronics, 3rd party ecosystems, cloud, web, game, AR/VR and machine learning developers and data scientists combined ever conducted. You can read the full free report here.

If you are a Machine Learning programmer or Data Scientist, join our community and voice your opinion in our current survey to shape the next State of the Developer nation report.