ML in Production: From Data Scientist to ML Engineer

I’ve been working in this field for over eight years and it’s rare to meet other ML engineers who hold themselves to the same high standards I do. In this course, I’ve included everything you need to stand out from most developers, using the newest tools to create excellent ML microservices with clean and structured codebase.

This comprehensive course is designed to equip you with the essential skills and knowledge required to transform Machine Learning (ML) models developed in Jupyter notebooks into fully operational, production-ready microservices. As a student of this course, you’ll delve deep into the intricacies of taking an ML model from a mere concept in a notebook to a scalable and efficient microservice that thrives in a real-world production environment.

Note: the course is still in progress (no Module 4 yet), but you can buy it now for a lower price.

Throughout the course, you will learn to bridge the gap between data science and software engineering, elevating your ML capabilities from theoretical models to practical applications.

> In Module 1, we will begin by training and tuning a simple ML model. This initial exercise serves as a practical example for the course. Later on, you will have the flexibility to apply the concepts and techniques learned to any ML model of your choice.

> In Module 2, you will be introduced to the fundamentals of production codebase design, where you will learn how to transition your code from Jupyter notebooks into a robust application. This module covers:

1. Project Structure: Organizing your codebase into logical components such as modules, packages, and directories to maintain clarity and scalability.

2. Parametrization Techniques: Techniques for parameterizing your ML model to enhance adaptability across different scenarios and configurations.

3. Logging Setup: Implementing industry best standards logging mechanisms to capture relevant information and facilitate effective debugging processes.

4. Database Implementation: Setting up a database layer to manage data persistence for your ML model, ensuring efficient data storage and retrieval.

5. Clean Code Practices: Exploring best practices for writing clean code that emphasizes readability, maintainability, and scalability, enhancing the longevity and usability of your application.

6. Linting and Formatting: Applying linting tools and code formatting practices to maintain code quality standards and ensure consistency across your codebase.

7. Automation with Makefiles: Using makefiles to automate repetitive tasks and streamline development workflows, improving efficiency and reducing errors.

8. CI/CD Pipelines with GitHub Actions: Setting up continuous integration and continuous deployment (CI/CD) pipelines using GitHub Actions to automate testing, build, and other processes, enabling faster and more reliable software delivery.

By the end of module 2, you will have a solid foundation in transforming your ML models into well-structured applications, equipped with best practices in code design, automation, and deployment strategies.

> In Module 3, you will be guided through the process of designing and implementing robust APIs that facilitate seamless communication for your ML models with other applications. This module covers:

1. API Design and Implementation: You will learn how to design APIs for various purposes, including model maintenance APIs and APIs for model inference. This involves structuring endpoints to handle specific tasks such as model training updates, parameter tuning, and real-time predictions.

2. Combining APIs: Understanding how to integrate different APIs within your application architecture to create cohesive workflows that leverage your ML models effectively.

3. Practical Training on Handling Requests and Responses: Hands-on exercises to manage incoming API requests, process them efficiently, and deliver appropriate responses. This includes parsing input data, executing model predictions, and formatting output responses.

4. Ensuring Security: Best practices for securing your APIs against potential threats and unauthorized access. This includes authentication mechanisms, data encryption, and implementing secure communication protocols.

5. Scalability Considerations: Techniques for scaling your APIs to handle increased traffic and growing demands. This involves strategies like load balancing, caching, and optimizing resource usage.

6. Asynchronous API Operations: Understanding the benefits of asynchronous processing in APIs to improve responsiveness and handle concurrent requests more efficiently. This includes utilizing asynchronous frameworks and handling long-running tasks effectively.

By the end of module 3, you will have the skills and knowledge to design, implement, and manage APIs that empower your ML models to integrate seamlessly into production environments, ensuring both reliability and performance across various operational scenarios.

> In Module 4, you will delve into the essential skill of containerizing your ML models using Docker, which is crucial in modern software development. This module includes:

1. Creating Docker Images: Hands-on training on packaging your ML models, along with their dependencies and environment configurations, into Docker images. This ensures consistency and portability across different environments.

2. Managing Containers: Learning how to effectively manage Docker containers, including starting, stopping, and monitoring their performance. Understanding container lifecycle management is key to maintaining the availability and reliability of your ML applications.

3. Container Orchestration Principles: Introduction to the principles of container orchestration, such as Kubernetes, which automates the deployment, scaling, and management of containerized applications. You will gain insights into deploying multi-container applications and managing them at scale.

4. Deployment Strategies: Practical guidance on deploying containerized ML models in real-world scenarios. This includes strategies for blue-green deployments, canary releases, and rolling updates to ensure minimal downtime and smooth transitions.

5. Continuous Delivery Pipelines: Setting up continuous delivery pipelines (CI/CD) to automate the deployment process of your Dockerized ML models. This involves integrating version control, automated testing, and deployment steps to streamline the release cycle.

6. Version Control and Monitoring: Best practices for versioning your Docker images and managing changes effectively using version control systems like Git. Additionally, you will learn about monitoring tools and practices to ensure the health and performance of deployed ML models.

By the end of Module 4, you will be equipped with the knowledge and practical skills to containerize your ML models, deploy them using Docker in a scalable manner, and implement robust deployment strategies that align with industry best practices. This prepares you to manage and maintain production-grade ML applications efficiently in diverse operational environments.

By the end of this course, you will have a well-rounded understanding of the full lifecycle of ML model development and deployment. You will be able to confidently take any ML model from a Jupyter notebook and turn it into a production-ready service, ready to deliver value in real-world applications. This course is an invaluable opportunity for anyone looking to enhance their career in data science, machine learning, or software engineering.