ML in Production: From Data Scientist to ML Engineer

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Andrew Wolf,Ilya Fursov

2:41:54

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1. Getting Started

1. Course Compass Exploring the Journey Ahead.mp4

10:50

2. Course Resources Community, Git Repository, and more.html

3. Course Software Setup Jupyter, IDE and more.html

2. Model Creation in Jupyter Notebook

1.1 You can access the end result of this lecture through the following link..html

1. Data Preprocessing, Model Building, Hyperparameters Tuning & Models Management.mp4

07:13

3. Model-to-App Packaging Production Codebase Design

1. From Jupyter to Application Code Transformation.mp4

18:02

2.1 You can access the end result of this video through the following link..html

2. Python Dependency Management Setup with Poetry.mp4

17:24

3.1 You can access the end result of this video through the following link..html

3. Python Parametrization Setup with Pydantic.mp4

09:16

4.1 You can access the end result of this lecture by following this link..html

4. Python Logging Setup with Loguru.mp4

23:29

5.1 You can access the end result of this lecture by following this link..html

5. Database Setup and Python Connectivity with Sqlalchemy.mp4

14:05

6.1 You can access the end result of this lecture by following this link..html

6. Foundations of Codebase Architecture.mp4

14:40

7.1 You can access the end result of this lecture by following this link..html

7. Clean Code Techniques for Software Excellence.mp4

46:55

Description

Turn any ML model within Jupyter Notebook to production-ready microservice.

What You'll Learn?

Transform ML models from Jupyter notebooks into production-ready microservices, focusing on deployment, dependency management, and maintainability.
Learn to create robust APIs for ML models, covering API design, request handling, and ensuring scalability and security.
Master Docker containerization for deploying ML models, including container management and best practices for ML applications.
Gain hands-on experience with real-world deployment strategies, including CI/CD pipelines, version control, MLOps frameworks and maintaining live models.

Who is this for?

Junior/Middle Data Scientists

What You Need to Know?

Knowledge and practical experience of Python syntax

Experience in model development in Python (train-test split, tuning hyperparameters, evaluating model performance, making predictions)

More details

Description
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 application that thrives in a real-world production environment.
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. The course starts with an introduction to the fundamentals of microservices architecture, setting the stage for understanding how ML models fit into larger software systems.
You will then learn how to effectively containerize your ML models using Docker, a critical skill in todayâ€™s software development landscape. This includes hands-on training on creating Docker images, managing containers, and understanding the principles of container orchestration.
API development is another cornerstone of this course. You will be guided through the process of designing and implementing robust APIs that allow your ML models to communicate seamlessly with other applications. This includes practical training on handling API requests and responses, along with ensuring the security and scalability of your APIs.
Moreover, the course covers deployment strategies, focusing on practical, real-world challenges. You will engage in setting up continuous integration and continuous delivery (CI/CD) pipelines, managing version control, and learning the best practices for monitoring and maintaining your models once they are deployed.
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.
Who this course is for:
Junior/Middle Data Scientists

User Reviews

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Hi there. My name is Andrew. I am an MLOps Engineer by day and an enthusiastic STEM tutor by night.While teaching ML to my students, it always occured to me that the materials I managed to find on the ML in Production were either designed for experienced researchers, or were sprinkled with fishy fairy tales about artificial intelligence, data-science magic, and jobs of the future. I tried to fix this problem by creating this course that makes machine learning in production both simple to understand and fun to learn about!

Ilya Fursov

Instructor's Courses

As an ML Engineer, I specialize in integrating machine learning models into scalable microservices, crafting robust APIs to facilitate seamless model interaction. My expertise lies in bridging the gap between complex ML algorithms and practical applications, ensuring models are not only accurate but also accessible. I excel in the realm of MLOps, streamlining deployment, monitoring, and management of machine learning models, thereby enabling efficient, real-time data processing and decision-making capabilities.

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