articles, July 9, 2024

Overview of AI Tech Stack: Components, AI Frameworks, MLOps & IDEs

Overview of AI Tech Stack

Ever wondered what makes some companies stand out in the AI space while others struggle to keep up? The secret often lies in their AI tech stack. Understanding the core components and best practices can set you on the path to success.

But first things first: what exactly is an AI tech stack? Simply put, it’s the collection of tools, AI frameworks, and platforms that you use to develop AI applications. Think of it as the foundation of a skyscraper; without a solid base, the whole structure is at risk.

The 2024 McKinsey Global Survey reveals a significant increase in AI adoption, rising from around 50% over the past six years to 72% this year, highlighting a growing global interest and implementation. As more organizations embrace AI, having a robust tech stack becomes essential for staying competitive, ensuring efficient development, and maintaining high performance in AI applications.

Components of AI Tech Stack

Building an AI system involves a multifaceted tech stack, with each layer playing a critical role in the development, deployment, and maintenance of robust AI solutions. Let’s break down the core components.

ai tech stack components

AI Infrastructure

Whether it’s CPUs, GPUs, TPUs, or specialized hardware, the backbone of any AI infrastructure lies in robust compute resources. Ensuring swift and efficient processing, these powerhouses enable the training and inference tasks vital for model development and deployment.

Effective storage solutions, such as SSDs and distributed storage systems, house large datasets and model artifacts. They guarantee quick access and retrieval capabilities for seamless data and model management.

High-speed networking infrastructure paves the way for efficient data flow and coordination among disparate components, fostering a cohesive and responsive AI ecosystem.

Data Collection and Storage

Any AI project begins with raw data. Data may be collected from databases, APIs, sensors, and web scraping, among others. These sources provide the necessary data to train and evaluate AI models.

To collect, aggregate, and preprocess data from any source, we use data ingestion tools. For example, solutions like AWS Kinesis, AWS Glue, Azure Data Factory, and Databricks enable seamless data ingestion and ensure data is consistently fed into the system for analysis and model training.

Efficiently storing and managing large volumes of structured and unstructured data is crucial. There are several scalable storage solutions available for AI applications, including Amazon S3, Google Cloud Storage, and Azure Blob Storage.

For instance, in developing the AI-driven speech analytics solution, we extensively leveraged Azure computing services for their robust data storing, processing capabilities, and scalable infrastructure. Azure services were particularly instrumental in efficiently managing large volumes of data, so the resulting solution completely fulfilled client requirements in terms of functionality and usability.

Data Preparation and Feature Engineering

Once raw data is collected and stored, the next phase is data preparation and feature engineering. It involves:

  • data cleansing, which removes noise, handles missing values, and validates and standardizes the data.
  • feature engineering, the process of creating or transforming features to improve model performance.
  • data transformation techniques such as normalization, scaling, and encoding.

These steps are vital to ensure the data is clean, relevant, standardized, and ready for effective AI model training.

Modeling and Training

With prepared data in hand, the next phase is modeling and training, where the real magic of AI happens. This stage involves selecting appropriate algorithms, training the models, and fine-tuning them to achieve optimal performance.

Choosing the right algorithm is crucial and depends on the problem domain. Supervised learning algorithms, such as regression and classification, work best with labeled data, helping predict outcomes based on input features. In contrast, unsupervised learning algorithms, like clustering, excel with unlabeled data, identifying patterns and groupings. Reinforcement learning is ideal for tasks requiring sequential decision-making, where a user learns optimal actions through trial and error.

For complex tasks such as image recognition and natural language processing, we use deep learning frameworks like PyTorch, Tensorflow, and Keras. These frameworks provide the tools necessary to build and train neural networks, enabling models to learn from vast amounts of data.

Optimizing hyperparameters is essential for boosting model performance. Automated and manual techniques like grid search, random search, and Bayesian optimization come to the rescue. They aid in pinpointing the most effective hyperparameters and ensure a model excels when faced with unseen data.

When it comes to evaluating model performance, we employ metrics such as accuracy, precision, recall, and F1-score for supervised learning models. For unsupervised models, silhouette scores or clustering metrics are utilized.

Deployment and Serving

When a model is trained and evaluated, what’s next? Here comes deploying it into production environments and making it accessible for real-world use.

Deploying a trained model requires setting up infrastructure and software components to host the model and handle incoming requests. We opt for technologies like Kubernetes, Docker, and serverless computing services to provide scalable and efficient deployment solutions.

To guarantee seamless integration with other applications or systems, we expose the model as an API or microservice. It allows developers to interact with the model programmatically, making predictions or obtaining insights in real-time.

On top of that, we ensure the deployed models can manage diverse workloads and sustain performance across different scenarios, employing scalability strategies like load balancing, auto-scaling, and resource optimization.

Monitoring and Management

After deployment, we proceed with continuous model monitoring and management to detect any deviations from expected behavior or performance degradation. Monitoring tools such as PowerBI, Datadog, AWS QuickSight, AWS CloudWatch, Azure Monitor, Weights & Biases (W&B) track various metrics, including model accuracy, latency, and resource utilization.

To keep stakeholders informed about any anomalies (such as data and model drift) or critical events in real-time, we implement robust alerting mechanisms. Additionally, logging relevant information ensures traceability and simplifies post-mortem analysis in cases of incidents or failures.

With model versioning and management, organizations can maintain the integrity of their AI systems and guarantee traceability and reproducibility. For example, version control systems like DVC help track changes and updates to models.

Development and Collaboration Tools

As vital AI tech components, development environments like Jupyter Notebooks, PyCharm, and other IDEs provide platforms for writing, testing, and experimenting with code. These tools improve productivity and allow for interactive exploration of data and models.

To track changes and manage the codebase, we leverage version control systems like Git. They allow us to maintain the integrity of the AI development project and facilitate collaboration among team members.


Clusters are crucial in the deployment and scalability of machine learning models, particularly for high-throughput and real-time inference use cases. Establishing a cluster-based architecture can give organizations significant benefits, such as enhanced scalability and high availability. Clusters ensure optimal resource utilization, allowing for efficient handling of varying workloads. They offer flexibility and portability, making adapting to different environments and requirements easier.

ai development tech stack

Data in AI Tech Stack

Data stands as the cornerstone of AI development, shaping the trajectory of machine learning models. It serves as the raw material from which models glean insights, discern patterns, and make predictions. Moreover, the quality of data directly impacts model performance and aids in addressing biases to foster equitable and precise decision-making.

The GIGO Concept

GIGO, “garbage in, garbage out,” stands as a vital concept in machine learning and data-driven systems. It underscores the necessity of high-quality, relevant, and accurate input data to guarantee the reliability and effectiveness of a model’s predictions or outputs.

At Coherent Solutions, we adhere strictly to this rule, recognizing that the quality of input data directly impacts the performance of our models. By prioritizing data integrity, we ensure that our machine learning solutions deliver precise and valuable insights. This commitment to GIGO helps maintain the trust and satisfaction of our clients.

Types of Data Used in AI Projects

1. Structured data

Ever wondered what makes structured data so valuable? Well, it’s all about organization. Structured data adheres to a predefined schema and is commonly housed in databases or spreadsheets. Its organized nature renders it easily searchable and analyzable, making it ideal for traditional statistical analysis and machine learning algorithms alike.

2. Unstructured data

Everyone who encountered unstructured data knows it’s like navigating a maze without a map. Unstructured data, comprising text, images, videos, and audio files, lacks a predefined structure. Analyzing it demands advanced techniques such as natural language processing and computer vision to extract meaningful insights from this diverse array of information.

3. Semi-structured data

A blend of order and chaos. That’s what semi-structured data is. It exhibits some organizational properties but doesn’t adhere to a rigid schema. Examples include JSON and XML files. This type of data offers flexibility in storage and retrieval and is commonly employed in web applications and NoSQL databases for its adaptable nature.

4. Temporal data

Ever considered the significance of temporal data? It’s all about the timestamps. Temporal data comprises time-stamped information like stock prices, sensor readings, and event logs. Analyzing this data entails deciphering trends, patterns, and correlations over time, indispensable for tasks like forecasting and predictive modeling.

5. Spatial data

Exploring the world through spatial data is akin to having a digital globe at your fingertips. Spatial data encompasses geographic information such as maps, satellite images, and GPS coordinates. Analyzing spatial data requires spatial indexing, geocoding, and spatial analysis techniques to unveil spatial relationships and patterns. This data is instrumental in applications like urban planning and environmental monitoring, offering insights into geographical phenomena that shape our world.

Data Collection, Preprocessing, and Augmentation Techniques

1. Data collection

In this first phase, the process kicks off by sourcing raw data from diverse channels, including databases, APIs, sensors, and web scraping. It’s crucial that the collected data is not only pertinent and precise but also substantial enough to ensure effective training and evaluation of machine learning models.

2. Data preprocessing

Once acquired, raw data embarks on a journey of preprocessing to cleanse, transform, and prepare it for analysis. This pivotal stage encompasses tasks like addressing missing values, eliminating noise, standardizing formats, and encoding categorical variables. Through meticulous preprocessing, data attains consistency, relevance, and suitability for training machine learning models.

3. Data augmentation

Data augmentation techniques serve to enrich the diversity and scale of the training dataset, warding off overfitting and bolstering model generalization. Methods encompass image manipulation like rotation, flipping, cropping, and noise addition. For textual data, strategies such as synonym substitution, word dropout, and random insertion are utilized. These augmentation processes fortify the resilience and efficacy of machine learning models, particularly beneficial in scenarios with constrained training data.

artificial intelligence tech stack

AI Development Frameworks

AI development frameworks provide essential libraries, APIs, and environments necessary for building and deploying machine learning and deep learning models efficiently. Here are the top AI development frameworks shaping artificial intelligence.


Another Artificial Intelligence framework is PyTorch, the established star of the AI world. Known for its flexibility and simplicity, PyTorch has quickly gained a cult following among developers and researchers alike. With its dynamic computational graph and intuitive interface, PyTorch makes it a breeze to build complex AI solutions as well as prototype and experiment with new ideas.
But don’t let its user-friendly facade fool you — PyTorch delivers formidable results when it comes to performance.


TensorFlow is the undisputed heavyweight champion of deep learning. Developed by Google, TensorFlow packs a punch with its robust architecture and extensive library of pre-built models. Whether you’re training neural networks or building custom algorithms from scratch, TensorFlow has got you covered.
But TensorFlow isn’t just for the pros. With its user-friendly interface and comprehensive documentation, even beginners can dive into the world of deep learning with ease. So, whether you’re a seasoned AI veteran or a curious newcomer, TensorFlow is your ticket to AI greatness.


Last but not least, we have Keras, the versatile AI framework designed with simplicity and speed in mind. Keras, developed as an open-source project, is renowned for its easy-to-use interface and ability to run on top of TensorFlow, Theano, or CNTK, making it an excellent choice for rapid prototyping and deep learning experiments.
But what sets Keras apart from the competition? It isn’t for beginners. Its modularity and extensibility make it powerful enough for advanced researchers, allowing them to build and train complex neural networks efficiently, whether you’re crafting your first model or pushing the boundaries of AI research.

Comparing and Contrasting AI Frameworks

ai development frameworks comparison

Choosing the Right AI Framework

Embarking on the quest for the perfect AI framework? Here are some key considerations to keep in mind as you navigate the labyrinth of options.

1. Tailor-made solutions

Your project is unique, so why settle for a one-size-fits-all approach? Consider the specific requirements and constraints of your project, from data type to deployment environment.

2. Feature evaluation

Not all frameworks are created equal. Evaluate each contender based on performance, scalability, ease of use, and community support. It’s like choosing the perfect tool for a master craftsman — precision is key.

3. Hands-on exploration

Don’t be afraid to roll up your sleeves and get your hands dirty. Experiment with different frameworks through prototyping and benchmarking to see which one fits like a glove.

4. Familiarity and integration

Sometimes, the best tool for the job is the one you know inside out. Take into account your familiarity with the framework as well as its integration with existing tools and workflows.

What is MLOps in AI Tech Stack?

MLOps, short for Machine Learning Operations, is a set of practices and processes that focuses on effectively integrating machine learning models into production environments. It is a crucial part of the AI tech stack and encompasses practices and tools for automating and streamlining the lifecycle of machine learning models, from development to deployment and monitoring. By implementing MLOps, organizations can ensure continuous delivery and scalable management of their AI solutions.

Key MLOps Platforms and Tools Used in the Industry

Discover the key MLOps platforms that are enhancing IT operations. These tools leverage AI to improve model quality monitoring, detect model drift, and enhance overall operational performance.

1. MLFlow

MLFlow is an open-source platform designed to manage the complete machine learning lifecycle, from experimentation and reproducibility to deployment. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models.

2. DVC

Data Version Control is used for managing and versioning datasets, models, and experiments. This open-source version control system facilitates collaboration and reproducibility throughout the project lifecycle.

3. Kubeflow

Kubeflow simplifies the deployment, orchestration, and scaling of machine learning workflows on Kubernetes. The platform supports end-to-end workflows, from data preparation to training and deployment.

4. Amazon SageMaker

Amazon SageMaker is a fully managed service that provides tools to build, train, and deploy machine learning models at scale. It offers integrated Jupyter notebooks, automated model tuning, and one-click deployment.

5. Azure Machine Learning

Azure Machine Learning is a cloud-based service that enables data scientists and developers to build, train, and deploy machine learning models. It provides an end-to-end solution with automated ML, model management, and MLOps capabilities.

6. Databricks Machine Learning

Databricks Machine Learning is a collaborative platform that combines data engineering, machine learning, and analytics. It offers managed Apache Spark, automated machine learning, and collaborative notebooks for streamlined workflows.

7. Weights & Biases

Weights & Biases is a tool that helps track and visualize machine learning experiments, making it easy to compare results and understand model performance. It integrates with popular frameworks and supports collaborative research and development.

8. Datadog

Datadog provides cloud monitoring and security services that include tracking metrics, logs, and traces for machine learning models. It ensures reliable performance and helps detect and resolve issues in real-time.

IDEs in AI Tech Stack

IDEs, or Integrated Development Environments, are the wizard’s wand of AI development. AI IDEs are comprehensive software suites designed to streamline the software development lifecycle. They amalgamate essential tools like code editors, debuggers, compilers, and version control systems into a cohesive interface. IDEs facilitate coding, testing, and debugging processes, enhancing developer productivity and collaboration.

Explore the best AI IDEs that provide developers with a powerful toolkit to efficiently create, refine, and deploy software solutions.

ides in ai tech stack

Best Practices for Building AI Tech Stack

Embarking on AI development requires a strategic approach to ensure success. Follow our essential guidelines to navigate your AI project effectively.

1. Define Clear Objectives

Kickstart your AI journey with defined objectives. Clarify what you aim to achieve to steer your development efforts effectively and measure progress.

2. Choose the Right Tools

Handpick tools aligned with your goals and technical specifications. Whether it’s ML frameworks, data processing platforms, or deployment solutions, select those tailored to your project’s requirements.

3. Data Quality is Paramount

Build on a foundation of quality data. Ensure accuracy, completeness, and relevance to fuel robust AI models and reliable insights.

4. Focus on Scalability

Anticipate growth and scalability needs. Design an architecture capable of handling increasing data volumes and user demands while maintaining performance and efficiency.

5. Embrace Automation

Streamline workflows with automation. From data preprocessing to model deployment, leverage automation tools to expedite processes and minimize manual intervention.

6. Prioritize Security and Privacy

Safeguard data integrity and user privacy. Implement stringent security measures and adhere to privacy regulations to instill trust and protect sensitive information.

7. Promote Collaboration

Cultivate a collaborative culture across teams. Encourage interdisciplinary collaboration between data scientists, developers, and domain experts to foster innovation and drive successful AI initiatives.

Final Thoughts

In conclusion, a well-constructed AI tech stack is critical for successful AI application development and deployment. By understanding and implementing the key components, frameworks, and best practices, organizations can achieve efficient, scalable, and high-performing AI solutions. As AI adoption continues to rise, staying informed and leveraging the right tools and techniques will ensure a competitive edge.

Coherent Solutions: Your Go-to AI Technical Partner

Just understanding the details of the AI tech stack is not enough for success in AI software development. It requires a strategic partner with deep expertise in the field and a proven track record. Coherent Solutions excels in delivering innovative, high-quality, and custom AI solutions.

Coherent Solutions harnesses its 30-year experience in software engineering to deliver state-of-the-art AI consulting and development services. No matter your industry, we can help you enhance your business operations and achieve greater success through tailored AI solutions. Feel free to reach out to our experts to schedule a free consultation.

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