Image generated by Microsoft Bing
 

Introduction

 

In today's world, where data drives decisions, simply creating machine learning (ML) models isn't enough. Organizations need to do more than build models — they need to successfully deploy, manage, and continuously improve these models in real-world scenarios. Imagine this: you've built a super-smart system to predict weather patterns, but unless you ensure it works every day and keeps getting smarter with new data, it's like having a powerful tool gathering dust in a shed. That's where MLOps steps in.

If you're curious about taking your MLOps skills to the next level and want to know how to turn your awesome models into real-world solutions, this article is your guide. I will introduce you to five free courses that break down MLOps into easy-to-understand bits. Whether you're starting fresh or you're already a pro in machine learning, there's a course here that fits just right for you.

 

Python Essentials for MLOps

 

Link: Python Essentials for MLOps

 

Python Essentials for MLOps Course
 

This course will teach you the fundamental Python skills you need to succeed in an MLOps role. It covers the basics of the Python programming language, including data types, functions, modules, and testing techniques. It also covers how to work effectively with datasets and other data science tasks with Pandas and NumPy. In this course, through a series of hands-on exercises, you will gain practical experience working with Python in the context of an MLOps workflow. By the end of the course, you will have the necessary skills to write Python scripts for automating common MLOps tasks. 

This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their Python skills.

 

Topics covered:

 

• Data Exploration
• Classification: Spam Filtering
• Ranking: Priority Inbox
• Regression: Predicting Page Views
• Regularization: Text Regression
• Optimization: Breaking Codes
• PCA: Building a Market Index
• MDS: Visually Exploring US Senator Similarity
• kNN: Recommendation Systems
• Analyzing Social Graphs
• Model Comparison

 

MLOps for Beginners

 

Link: MLOps for Beginners

 

MLOps for Beginners Course
 

So now that you have taken a refresher on Python, it is time to dig into some real stuff! The course, MLOps for Beginners, is a free tutorial on Udemy that teaches you how to provide an end-to-end machine learning development process to design, build, and manage the AI model lifecycle. 

The course is taught by Prem Naraindas, an experienced MLOps practitioner, and includes several hands-on exercises. By the end of the course, you will have a good understanding of the basics of MLOps and be able to apply it to your work.

 

Topics covered:

 

• MLOps Overview
• MLOps Tools and Platforms
• Creating pipelines
• Automating model training, evaluation, experimentation
• Deployment and monitoring
• Serving
• Scaling
• MLOps Best Practices

 

Machine Learning Engineering for Production (MLOps) Specialization

 

Link: Machine Learning Engineering for Production (MLOps) Specialisation

 

Machine Learning Engineering for Production Specialization
 

If you're ready to transition from theoretical knowledge to real-world machine learning coding, you need to take this course Machine Learning Engineering for Production (MLOps) Specialization on Coursera. This comprehensive specialization, offered by deeplearning.ai, is designed for programmers who have previously some experience in Tensorflow and possess a passion for practical applications and hands-on coding experiences. This course is ideal for those who have a good grip on Python and TensorFlow and want to jump right into the MLOps world!

The best part is that the course is taught by Andrew Ng, the leading AI advocate at Google, Lawrence Moroney, and Robert Crowe from Google. 

 

Topics covered:

 

• Production-ready Machine learning systems
• Data pipelines and model management techniques
• Concept Drift
• Model Training
• Cloud Based tools for MLOps
• Monitoring of models
• Optimization of models
• Tensorflow Production (TFX)

 

MLOps | Machine Learning Operations Specialization

 

Link: Machine Learning Operations Specialization

 

Machine Learning Operations Specialization
 

This comprehensive course series is designed for individuals with programming knowledge and who are interested in learning MLOps. The courses will teach you how to use Python and Rust for MLOps tasks, GitHub Copilot to enhance productivity, and leverage platforms like Amazon SageMaker, Azure ML, and MLflow. You will also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format. The courses will also prepare you for various career paths in MLOps, such as data science, machine learning engineering, cloud ML solutions architecture, and artificial intelligence (AI) product management.

This comprehensive course series is perfect, especially for those individuals with prior programming knowledge, such as software developers, data scientists, and researchers.

 

Topics covered:

 

• Microsoft Azure
• Big Data
• Data Analysis
• Python Programming
• Github
• Machine Learning
• Cloud Computing
• Data Management
• DevOps
• Amazon Web Services (Amazon AWS)
• Rust Programming
• MLOps

 

Made With ML MLOps Course

 

Link: Made with MLOps

 

Made With ML MLOps Course
 

Goku Mohandas has developed an exceptional and publicly accessible course on the creation of end-to-end machine learning systems. Made with ML is one of the most popular GitHub repositories with over 30,000 people enrolled in this course.

Made with ML lessons cover the fundamentals of machine learning as well as the intricacies of model deployment, testing, and monitoring in production. Goku's lessons explain the underlying ideas behind the concepts introduced, provide practical project-based assignments, and equip students with some of the best practices in software engineering necessary for success in an MLOps role.

 

Topics Covered: 

 

1. Fundamentals of Machine Learning
2. End-to-End System Development
3. Deployment Strategies
4. Testing Methodologies
5. Model Monitoring
6. Intuition behind Concepts
7. Hands-On Project Assignments
8. Software Engineering Best Practices

 

Conclusion

 

MLOps is a rapidly growing field with a high demand for skilled professionals. By mastering MLOps, you can open up new career opportunities and make a real impact in the world. With the help of these five free courses, you can take the first step toward becoming an MLOps expert. So what are you waiting for? Enroll today and start learning!

If you are a beginner in machine learning and MLOps, you might want to see our article on 5 free books to master machine learning. But if you want to dive right into MLOps and want to take one or two courses only, I recommend taking the Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng and the Made with MLOps course.

We're curious to know, which courses have played a pivotal role in your machine-learning journey. Feel free to share your thoughts in the comments below!
 
 

Kanwal Mehreen Kanwal is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook "Maximizing Productivity with ChatGPT". As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She's also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.