Author: Julie Martel

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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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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.