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In today's world where data drives decisions, it is not enough to create machine learning (ML) models. Organizations must do more than create models: they must implement, manage, and continually improve these models successfully in real-world scenarios. Picture this: You've built a super-smart system for predicting weather patterns, but unless you make sure it works every day and gets smarter with new data, it's like having a powerful tool collecting dust in a shed. That's where MLOps comes in.
If you are curious to take your MLOps skills to the next level and want to know how to turn your amazing models into real-world solutions, this article is your guide. I'll introduce you to five free courses that break down MLOps into easy-to-understand parts. Whether you're starting from scratch or already a pro at machine learning, here's a course that's perfectly suited to your needs.
Link: Python Basics for MLOps
Python Essentials for MLOps Course
This course will teach you the foundational Python skills you need to succeed in an MLOps role. Covers the basics of the Python programming language, including data types, functions, modules, and testing techniques. It also covers how to effectively work with datasets and other data science tasks with Pandas and NumPy. In this course, through a series of hands-on exercises, you will gain hands-on experience working with Python in the context of an MLOps workflow. By the end of the course, you will have the skills to write Python scripts to automate common MLOps tasks.
This course is ideal for anyone looking to enter the field of MLOps or for experienced MLOps professionals who want to improve their Python skills.
Topics covered:
- Data exploration
- Classification: spam filtering
- Classification: Priority Inbox
- Regression: predict page views
- Regularization: text regression
- Optimization: decipher codes
- PCA: creating a market index
- MDS: Visually exploring the similarity of the US senator
- kNN: recommender systems
- Social graph analysis
- Model comparison
Link: MLOps for beginners
MLOps course for beginners
So now that you've brushed up on Python, it's 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 practical exercises. By the end of the course, she will have a good understanding of the basic concepts of MLOps and will be able to apply them to her work.
Topics covered:
- MLOps Overview
- MLOps Tools and Platforms
- Creating pipes
- Automation of model training, evaluation and experimentation.
- Implementation and monitoring
- Service
- Climbing
- MLOps Best Practices
Link: Specialization in Machine Learning Engineering for Production (MLOps)
Machine Learning Engineering for Production Specialization
If you're ready to move from theoretical knowledge to real-world machine learning coding, you should take this Machine Learning Engineering for Production (MLOps) specialization course on Coursera. This comprehensive specialization, offered by ai/” rel=”noopener” target=”_blank”>deep learning.ai, is designed for programmers who previously have some experience in Tensorflow and have a passion for practical applications and hands-on coding experiences. This course is ideal for those who have a good command of Python and TensorFlow and want to jump straight into the MLOps world!
The best thing is that the course is taught by Andrew Ngthe main advocate of ai at Google, Lorenzo Moroneyand Robert Crowe of Google.
Topics covered:
- Production-Ready Machine Learning Systems
- Data pipelines and model management techniques.
- Derived from the concept
- Model training
- Cloud-based tools for MLOps
- Model tracking
- Model optimization.
- Tensorflow (TFX) production
Link: Machine Learning Operations Specialization
Machine Learning Operations Specialization
This comprehensive series of courses is designed for people with programming knowledge who are interested in learning MLOps. The courses will teach you how to use Python and Rust for MLOps tasks, GitHub Copilot to improve productivity, and leverage platforms like Amazon SageMaker, Azure ML, and MLflow. You will also learn how to tune large language models (LLM) using Hugging Face and understand the implementation of sustainable and efficient embedded binary models in the ONNX format. The courses will also prepare you for various career paths in MLOps, such as data science, machine learning engineering, architecting cloud machine learning solutions, and artificial intelligence (ai) product management.
This complete series of courses is perfect, especially for those people 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
Link: Made with MLOps
Made With ML MLOps Course
Goku Mohandas has developed an exceptional, publicly accessible course on building 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 complexities of deploying, testing, and monitoring models 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 software engineering best practices needed to succeed in an MLOps role.
Topics covered:
- Machine Learning Fundamentals
- End-to-end system development
- Implementation strategies
- Test methodologies
- Model monitoring
- Intuition behind the concepts
- Practical Project Assignments
- Software engineering best practices
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 on the world. With the help of these five free courses, you can take the first step towards becoming an MLOps expert. So, what are you waiting for? Sign up today and start learning!
If you are a beginner to machine learning and MLOps, you may want to check out our article on 5 Free Books to Master Machine Learning. But if you want to dive right into MLOps and want to take just one or two courses, I recommend taking the Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng and the Made with MLOps course.
We are curious to know which courses have played a pivotal role in your journey into machine learning. Feel free to share your thoughts in the comments below!
Kanwal Mehreen is an aspiring software developer with a strong interest in data science and ai applications in medicine. Kanwal was selected as a Google Generation Scholar 2022 for the APAC region. Kanwal loves sharing technical knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.
