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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.

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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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TechGig is a technology platform to Attract, Engage and Hire top tech talent. Companies can source talent from a growing community of 4 million developers and leverage the power of automated real-time assessments and data-driven decision support.

Deciding Tech Stack is the most important decision you need to take while creating any Tech Product. The important criterion we have used while deciding technology stack are developer ecosystem, agility, scalability, speed and performance. Every product has unique requirements and you need to select technology stack matching your requirements. Blindly copying others’ technology decisions is not the right way to go. With the growth of users and features sometimes, you might need to replace old tech pieces and adopt new ones. While designing your architecture you should design in such a way that replacing some part of your older stack should not lead to a major rewrite.

TechGig engineering team believes in Open Source technologies. Our complete stack is made up of open-source software. For different layers, we have used different technologies specific to the requirement.

Front End

Our Front End is served by LAMP stack and client-side is written in jQuery and AngularJS which is most commonly used stack and is appreciated by the developer community for its agility and scalability.

Full-Text Search

Full-text search is an important requirement for TechGig. For this, we are using Solr which supports real-time indexing of content, faceted search, dynamic clustering and scalability and fault tolerance.

Containerization

TechGig supports 54+ languages in its code evaluation engine. To scale it in real-time and support these many languages and environments we are heavily using containers. Docker is the container technology which provides lightweight virtualization with almost no overhead.

Data Store

A lot of data within the TechGig ecosystem is not relational in nature like metadata of various types of evaluations, events and analytics data. To support these kinds of data we are using MongoDB as NoSQL technology. MongoDB is easy to use, highly available, highly scalable and high-performance document-oriented database which fits well within our requirements for evolving data and real-time analytics.

Messaging

TechGig application follows loosely coupled architecture to manage complexity, modularity and stability of code. Messaging is an important aspect of loosely coupled architecture. TechGig uses Kafka as the message queue for inter-communication between different modules of application like code evaluation front end, code evaluation engine, content indexing, real-time analytics, recommendation engine etc.

Caching Tech Stack Requirements

Caching is an important requirement for any application to enhance performance and response time. TechGig uses Redis to support data caching and offloading database load. TechGig serves user profile data, session data, stats and a lot of long-lived data from Redis caching layer. Other static data like JavaScript, CSS and images are served from Akamai CDN.

Backend and Analytics

A lot of heavy lifting components like face detection, face recognition recommendation services, plagiarism detection, content classification, bulk mailing services etc. are written in java and python using OpenCV, NLTK and TensorFlow frameworks.

Analytics is very important for better user experience and informed decision making. TechGig uses ELK stack for data ingestion and exploration and visualization. All event logs and behavioural data is ingested and visualized using ELK stack.

Development and Deployment

Apart from the technology stack, the selection of Software development tools is also very critical. This includes IDE, build tools, source control, requirement and bug tracking, CI/CD pipeline and testing tools. TechGig team uses, VSCode as IDE as of this lightweight and very fast. For source control TechGig uses BitBucket and for the requirement and Bug tracking, Atlassian Jira is used. Bitbucket and Jira go along so well, and both are Atlassian tools. TechGig uses Jenkins for its CI/CD pipeline as it is very easy to manage, requires very little maintenance and has a rich plugin ecosystem. For automated testing we use Selenium as it is open source, supports multiple browser testing and parallel test executions.

Site Reliability

After all Site Reliability monitoring is also very important. If some code deployed in production misbehaves or breaks, the SRE team should get alert and immediate rectification should be done. TechGig team has its own set of inhouse homegrown tools for monitoring and alert.

Application Security

Bad actors consistently try to steal the user’s personal, financial data. To protect users and data, we have a comprehensive application security program in which from requirements gathering to production deployment in all steps security is embedded. We use Static Code analysis to identify security gaps within the code. To identify security gaps in production live application we use Dynamic security assessments. Apart from this to identify business logic vulnerabilities extensive manual security assessments are done periodically.

Apart from the above-mentioned pieces, there are a lot of other components which contribute to TechGig tech stack. The technologies are subject to change depending on new requirements and challenges. What makes our work interesting is the problem statement we are solving i.e. help the developer community to learn to compete and grow.

About the author

Ram Awasthi is the Head-Technology of TechGig & TimesJobs and VP- Technology, Times Internet.