Machine Learning Operations (MLOps) is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the process of deploying and managing machine learning models in production.
MLOps is crucial for organizations looking to scale their machine learning efforts and ensure that their models are reliable, efficient, and deliver consistent value over time.
By learning MLOps, you can gain valuable skills in deploying, monitoring, and managing machine learning models, enabling you to build robust and scalable AI systems.
Finding a comprehensive and well-structured MLOps course can be challenging, given the rapidly evolving nature of the field.
You’re likely searching for a course that covers the entire MLOps lifecycle, from model development to deployment and monitoring, while providing practical experience with industry-standard tools and techniques.
Based on our research and analysis, we believe that the Machine Learning Engineering for Production (MLOps) Specialization on Coursera is the best MLOps course overall.
This specialization offers a deep dive into MLOps, covering topics such as model development, deployment, monitoring, and continuous integration/continuous delivery (CI/CD).
It also includes hands-on projects and real-world case studies, allowing you to gain practical experience with MLOps tools and techniques.
However, there are other excellent MLOps courses available that may be a better fit for your specific needs and learning style.
We’ve compiled a list of the best MLOps courses across various platforms, catering to different levels of experience and areas of focus.
Continue reading to explore our top recommendations and find the perfect MLOps course to advance your machine learning journey.
Machine Learning Engineering for Production (MLOps) Specialization
Provider: Coursera
This specialization takes you deep into Machine Learning Operations (MLOps), guiding you from the beginning stages of creating an ML production system all the way to keeping it running smoothly.
You discover how to pick the right models and train them well, making sure they perform well in real-world situations.
You also learn about concept drift, which is how a model’s accuracy can decrease over time as data changes, and how to create a system that adapts to these changes.
Next, you’ll dive into the world of data management.
You learn how to collect, clean, and validate data, using tools like TensorFlow Extended (TFX) to transform and prepare data for analysis.
You will discover data provenance, which ensures that you can track the origins and history of your data throughout the system.
Then, you’ll learn the important skill of building effective machine learning models.
You’ll discover techniques to manage your resources wisely, handling both large batches of data and real-time requests.
This specialization equips you to use analytics tools to make sure your models are fair, explainable, and free from performance bottlenecks.
You even get to explore Explainable AI, making your models easier to understand and trust.
Finally, you’ll learn how to take your models and make them available to users in the real world.
You learn how to deploy and serve these models, building systems that can handle a large number of requests.
You learn about workflow automation and how to deliver updates in a streamlined way.
You’ll discover how to monitor your system’s performance, identify any issues, and ensure it’s always up and running.
MLOps Fundamentals - Learn MLOps Concepts with Azure demo
Provider: Udemy
This course begins with a deep dive into MLOps, taking you from the basics to building a real-world pipeline.
You will explore the traditional machine learning lifecycle and the common obstacles in deploying models.
You will then discover how MLOps provides a robust solution, outlining its principles, benefits, and different maturity levels.
You will gain hands-on experience by constructing a CI/CD MLOps pipeline using Azure DevOps and Azure Machine Learning.
The course guides you through using essential tools like Jupyter notebooks for data exploration and analysis (EDA), Azure Machine Learning Studio, PyTorch, and TensorFlow.
You will learn to write code for model training, evaluation, and deployment, understanding how to connect Azure DevOps with Azure ML to orchestrate your pipeline.
Through practical examples and the construction of a complete MLOps project, you will grasp the concepts of continuous integration (CI) and continuous deployment (CD).
You will work with real-world scenarios, starting from data preparation and model training, and progress to deployment and monitoring.
By the course’s end, you will have a strong foundation in MLOps principles and be able to build and manage your own MLOps pipelines, which can lead you to more efficient and scalable machine learning projects.
MLOps | Machine Learning Operations Specialization
Provider: Coursera
This specialization guides you through the essentials of MLOps, beginning with the core concepts of Python programming and advancing to the deployment of sophisticated machine learning models using cloud technology.
You start by grasping the fundamentals of Python, including data types, functions, and modules.
You then learn to test your code’s reliability using Pytest and discover how to interact with APIs and SDKs.
This knowledge equips you to build command-line tools that can automate a variety of machine learning tasks.
The specialization then delves into the practical applications of MLOps.
You learn to leverage DevOps, DataOps, and MLOps principles to create and deploy machine learning solutions.
Using technologies like GitHub Copilot, Gradio, and Click, you’ll streamline your workflow and build containers using Docker to package and deploy your models in cloud environments.
The curriculum also introduces Rust, a programming language gaining traction in building high-performance ML applications.
You will explore prominent cloud platforms like Amazon Web Services (AWS) and Microsoft Azure, and work with platforms like Amazon SageMaker and Azure ML to build, train, and deploy machine learning models in real-world settings.
This covers data engineering tools, data preparation techniques, and selecting the most suitable models for different needs.
You also learn to deploy and manage these models effectively within these cloud platforms.
Finally, you’ll explore open-source platforms, MLflow and Hugging Face.
You’ll discover how to use MLflow for managing machine learning projects and models, while with Hugging Face, you’ll build APIs and even deploy them to the cloud.
This specialization equips you with the knowledge and skills to develop, deploy, and manage machine learning solutions in a production setting.
Azure Machine Learning & MLOps : Beginner to Advance
Provider: Udemy
This course guides you through the complex world of Azure Machine Learning and MLOps, starting from the basics and taking you to an advanced level.
You begin with the Azure Machine Learning Service, learning how to build, manage, and deploy your machine learning models using this powerful cloud platform.
You then explore Azure DevOps, a crucial toolset for setting up your infrastructure as code and creating efficient CI/CD pipelines to automate your workflow.
The course then introduces you to the Azure ML SDK V2, a powerful toolset designed to standardize and streamline your machine learning lifecycle.
You’ll explore highly sought-after capabilities of Azure Machine Learning, including responsible AI.
You’ll learn how to ensure your models are fair, explainable, and easy to analyze for errors.
The course also dives into using Azure Machine Learning pipelines for seamless management, orchestration, and scheduling of your machine learning tasks.
You’ll gain hands-on experience with powerful tools like Feast, Ray, and Dask.
You’ll discover how to leverage Feast as your feature store, allowing you to build, manage, and share features efficiently.
The course teaches you how to use Ray and Dask within Azure Machine Learning, harnessing their power for distributed and parallelized processing.
You’ll learn how to implement AutoML for computer vision and natural language processing (NLP) tasks.
The course goes beyond the basics, covering topics such as differential privacy for working with sensitive data and deploying models to your on-premises Kubernetes clusters using Azure Arc.
You’ll explore how to integrate Azure Machine Learning with other essential tools in your data science toolkit.
This includes Power BI for consuming your trained models, Power Apps for creating user interfaces for your deployed models, and Azure Databricks for managing big data machine learning tasks.
You’ll even learn how to deploy multi-model endpoints, implement a blue-green deployment strategy for smooth model updates, and leverage open-source tools like MLflow for managing your entire machine learning lifecycle.
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Provider: Coursera
You’ll start with the fundamentals of big data and machine learning on Google Cloud, learning how to design streaming pipelines using tools like Dataflow and Pub/Sub.
You’ll gain a thorough understanding of the data-to-AI lifecycle and explore various machine learning solutions offered by Google Cloud, including Vertex AI and AutoML.
The specialization then dives into the practical application of these concepts using TensorFlow, where you’ll learn to build and train your own machine learning models.
You’ll gain hands-on experience with Keras, a user-friendly API for building TensorFlow models, and explore feature engineering techniques to improve the accuracy of your models.
The program emphasizes best practices for implementing machine learning, including responsible AI principles.
As you progress, the specialization covers advanced MLOps concepts, providing a deep dive into deploying, evaluating, monitoring, and automating machine learning systems in production.
You’ll learn about CI/CD practices specifically for ML systems, configure Google Cloud architectures for reliable MLOps, and work with tools like TensorFlow Extended (TFX), Cloud Composer, and MLflow.
This practical approach equips you with the skills to manage the entire machine learning lifecycle, from data preparation to model deployment and monitoring.
Machine Learning Deep Learning model deployment
Provider: Udemy
This Udemy course on deploying machine learning and deep learning models will take you from the fundamentals to advanced techniques.
You’ll begin with a crash course on Python libraries like NumPy, Pandas, and Matplotlib, essential for working with data.
You’ll then learn how to build, evaluate, and save classification models, a common type of machine learning model.
Next, you’ll dive into deploying these models in different environments.
You’ll discover how to use your model locally, then transition to deploying it to cloud platforms like Google Colab.
You’ll gain hands-on experience creating REST APIs using Flask, a popular Python framework, to serve your models effectively.
The course also explores serverless machine learning using Cloud Functions, allowing you to run your models without managing servers.
The course doesn’t shy away from deep learning either.
You’ll learn how to build and deploy models using powerful frameworks like PyTorch and TensorFlow.
You’ll explore using Docker containers for deployment and even discover how to convert models between PyTorch and TensorFlow formats using ONNX.
The course uses the practical example of Twitter sentiment analysis to teach you how to create and deploy NLP models.
You’ll work with techniques like bag-of-words and tf-idf to process text data, create a Twitter developer account, and deploy your model for real-world use.
Finally, you’ll delve into the world of MLOps, learning how to manage the entire lifecycle of a machine learning project.
You’ll become familiar with MLflow, a platform for tracking experiments, managing models, and deploying them to production.
This course equips you with the tools and knowledge to take your machine learning models from development to deployment, a crucial skill in today’s data-driven world.
Complete MLOps Bootcamp | From Zero to Hero in Python 2022
Provider: Udemy
This “Complete MLOps Bootcamp” course takes you on a journey from understanding the basics of machine learning to becoming skilled in using MLOps to seamlessly deploy models in real-world applications.
You’ll explore how MLOps solves common challenges in managing and deploying machine learning models, giving you the tools to streamline your workflow.
The course introduces you to essential tools like Python, Jupyter Notebook, and Docker, providing a solid foundation for your MLOps projects.
You’ll dive into popular libraries like Pycaret, MLFlow, and DVC, gaining hands-on experience in building, versioning, and managing your machine learning models and datasets.
You’ll master techniques for structuring your projects efficiently using tools like Cookiecutter, Poetry, and Makefile, ensuring your code is clean and well-organized.
The course doesn’t just stop at building models; it equips you with the skills to deploy them effectively.
You’ll learn to create APIs using FastAPI, build interactive web applications with Streamlit and Gradio, and even delve into backend development with Flask.
You’ll learn how to containerize your applications using Docker, making them portable and easy to deploy across different environments.
Plus, you’ll explore cloud deployment options using platforms like Azure and Heroku, giving you the flexibility to choose the best fit for your projects.
The course takes your skills further by introducing you to continuous integration and delivery (CI/CD) using GitHub Actions and CML.
You’ll learn how to automate the testing and deployment process, saving you time and reducing errors.
Finally, you’ll discover how to monitor your deployed models and services using Evidently AI.
This ensures your models continue performing as expected in a real-world setting, catching any potential issues before they impact your users.
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