How to Make AI: A Guide to An AI Developer’s Tech Stack

Artificial intelligence (AI) applications are rapidly transforming the world around us, from personalized recommendations in your favorite streaming service to AI-powered virtual assistants that handle our daily tasks. With the increasing demand for AI solutions, many founders are wondering how to make AI themselves, and many developers are eager to explore and create their own AI applications.

However, building an AI application requires a robust understanding of the underlying technology stack. So today, we’ll go through the essential components and explore the various options available for each.

Let’s get into it.

How to Make AI: The Basics

Before we dive into the technicalities, let’s define the very thing itself.

AI, or artificial intelligence, refers to developing computer systems capable of performing tasks typically requiring human intelligence. These tasks encompass learning, reasoning, problem-solving, comprehending natural language, and visual perception.

How to Make AI: The Developer’s Tech Stack

Okay, so we’ve now defined AI in what it does. But how do AI developers build it? Based on a complex tech stack with a bunch of different components—i.e., ingredients. 

1. Framework and Library Selection:

Frameworks and libraries are pre-built sets of tools and functions that help developers build and train AI models efficiently. They provide a structured foundation for creating applications, reducing development time and complexity.

Think of this like choosing the tools and ingredients you need to build a house. The frameworks and libraries provide the basic building blocks for constructing your AI model.

Options: TensorFlow, PyTorch, Keras, JAX, PySyft, scikit-learn

While you won’t be the one coding, grasping the frameworks your team might use is crucial. TensorFlow and PyTorch remain popular choices, with TensorFlow boasting widespread adoption while PyTorch offers a more intuitive interface. Keras, now seamlessly integrated within TensorFlow, provides user-friendliness, and scikit-learn excels at traditional machine learning tasks. Emerging frameworks like JAX and PySyft deserve mention, offering specific advantages for interpretability and privacy-focused applications.

2. Programming Language:

Programming languages are the building blocks of software development, allowing developers to instruct computers on how to perform specific tasks.

Think of programming languages like the instructions you give to build the house. They tell the computer what to do with the tools and ingredients from the framework.

Options: Python, R, Julia

Python is the go-to language for AI development, but understanding its importance allows for effective communication with your development team. R is ideal for statistical analysis, and Julia, a rising star, focuses on high-performance computing.

3. Data Collection and Preprocessing:

Data collection and preprocessing involve gathering and cleaning data to make it suitable for training AI models.

Think of it like gathering the materials needed to build the house and preparing them for use. Data collection and preprocessing gets data from various sources and cleans and organizes it so the AI model can understand it.

Options: Apache Kafka, Apache Flink, Apache Nifi, Apache Beam, Apache Hudi

For non-technical founders, it’s crucial to understand that data forms the lifeblood of AI applications. Tools like Apache Kafka facilitate real-time data streaming, Apache Flink excels at stream processing, and Apache Nifi provides a user-friendly interface for integrating data from diverse sources. Newer technologies like Apache Beam and Apache Hudi offer unified and incremental data processing pipelines, further streamlining the data preparation process.

4. Model Training:

Model training is the process of feeding data to an AI system, enabling it to learn patterns and make predictions.

This is where the metaphor falls off a bit, but think of it like building the house and teaching it to do what you want. Model Training feeds the prepared data to the AI model so it can learn and make predictions or decisions.

Options: Cloud-based platforms (AWS SageMaker, Google AI Platform), on-premises solutions (NVIDIA DGX systems, IBM PowerAI), Horovod, Megatron-Turing NLG

Model training involves feeding data to your AI system to enable it to make predictions or decisions. Cloud platforms like AWS SageMaker offer convenience, while on-premises solutions provide more control over infrastructure. Distributed training frameworks like Horovod and Megatron-Turing NLG are gaining traction for handling large-scale models efficiently.

5. Model Deployment:

Model deployment involves making the trained AI model accessible to users or other systems.

Think of model deployment as putting the finished house on display for everyone to use—it makes your trained model accessible for real-world applications.

Options: Docker, Kubernetes, serverless platforms (AWS Lambda, Google Cloud Functions), WebAssembly (WASM)

Deploying your trained model signifies the launch of your AI application. Docker ensures consistency across various environments, Kubernetes orchestrates container deployment, and serverless platforms offer automatic scaling. A rising alternative is WebAssembly (WASM), a platform-independent technology enabling the deployment of AI models in web applications.

6. Monitoring and Optimization:

Monitoring and optimization involve tracking the performance of your AI application and making adjustments for better efficiency.

Options: TensorFlow Profiler, PyTorch Profiler, AWS CloudWatch, Grafana, LIME, SHAP

Monitoring and optimizing your AI application guarantees smooth operation. Tools like TensorFlow Profiler and PyTorch Profiler offer insights, AWS CloudWatch provides integrated monitoring, and Grafana delivers an open-source solution for customizable monitoring. Additionally, Explainable AI (XAI) tools like LIME and SHAP empower users to understand how AI models arrive at decisions, promoting transparency and trust.

Summing up how to make AI…

The journey of creating an AI application might seem complex at first, but by equipping yourself with a basic understanding of the components within an AI developer’s tech stack, you become better equipped to collaborate effectively with your technical team. Remember, the key isn’t to become a developer overnight but to grasp the fundamentals to make informed decisions and drive your AI project to success.

But, if you find yourself needing some help with all of this—reach out to your friendly neighborhood developers. We’d love to talk 🙂