Hands-on Docker for Data Science & AI Projects

Are you a data scientist, AI practitioner, or machine learning engineer grappling with environment inconsistencies, reproducibility nightmares, or the infamous "it works on my machine" syndrome? Do you want to streamline your development workflows, collaborate more effectively, and deploy your AI models with greater ease and confidence? If so, Hands-on Docker for Data Science & AI Projects is your definitive guide to mastering containerization for your specific needs.

In the rapidly evolving landscape of data science and artificial intelligence, managing complex dependencies, ensuring consistent environments across different machines, and simplifying deployment are paramount. Docker has emerged as an indispensable tool to address these challenges, and this book is designed to equip you with the practical skills to leverage its full potential for your projects.

We begin with a Beginner-Friendly Introduction to Docker for Data Science Projects, demystifying core concepts like containers versus images and highlighting why Docker is crucial for data scientists. You'll quickly get Docker installed and dive straight into Dockerizing your first Machine Learning Application, learning how to define an environment, write a Dockerfile, and build your image.

Next, we explore a curated list of 11 Essential Docker Container Images for Generative AI & ML Projects. This chapter serves as a practical toolkit, showcasing pre-built environments for Python, Jupyter, Hugging Face Transformers, NVIDIA CUDA, TensorFlow, PyTorch, Ollama, Qdrant, Airflow, MLflow, and Kubeflow Notebooks, saving you valuable setup time.

Building on this foundation, Chapter 3 guides you through Building your first comprehensive Docker container for Machine Learning Applications, reinforcing your understanding with practical steps. To ensure your Docker practices are efficient and professional, Chapter 4 delves into Best Practices, covering crucial topics like minimizing layers, using official images, optimizing with multi-stage builds, persisting data with volumes, and effectively organizing and versioning your images.

Finally, to empower your daily workflow, Chapter 5 presents 10 Docker Commands for 90% of Containerization Tasks. This practical reference will make you proficient in commands like docker run, docker build, docker ps, and docker push, enabling you to manage your containers with confidence.

Whether you're new to Docker or looking to specifically apply it to the unique demands of data science and AI, this book provides a hands-on, step-by-step approach. By the end, you'll be adept at building, managing, and deploying containerized data science applications, paving the way for more reproducible, scalable, and collaborative projects.

Table of Contents:

1. Docker for Data Science Projects: A Beginner-Friendly Introduction
2. 11 Docker Container Images for Generative AI & ML Projects
3. Building your first Docker container for Machine Learning Applications
4. Best Practices For Using Docker for Data Science Projects
5. 10 Docker Commands for 90% of Containerization Tasks