The 5 Best MLOps Courses and Online Training for 2023

The editors at Solutions Review have compiled this list of the best MLOps courses and online training to consider.

MLOps (short for machine learning operations) is the process of taking a model developed in an experimental environment and putting it into a production web system. When an application is ready to be launched, MLOps is coordinated between data science professionals, DevOps and machine learning engineers to transition the algorithm into production. A key characteristic of MLOps is to increase automation and improve the quality of production machine learning while keeping compliance requirements in check.

With this in mind, we’ve compiled this list of the best MLOps courses and online training to consider if you’re looking to grow your data science and machine learning skills for work or play. This is not an exhaustive list, but one that features the best MLOps courses and online training from trusted online platforms. We made sure to mention and link to related courses on each platform that may be worth exploring as well.

The Best MLOps Courses

TITLE: Machine Learning Monitoring Concepts

OUR TAKE: This course, instructed by NannyML co-founder and CEO Hakim Elakhrass, starts with the blueprint of where to begin monitoring in production and how to structure the processes around it. It also covers basic workflow by showing you how to detect the issues, identify root causes, and resolve them with real-world examples.

Platform: DataCamp

Description: Deploying a model in production is just the beginning of the model lifecycle. Even if it performs well during development, it can fail due to continuously changing production data. In this course, you will explore the difficulties of monitoring a model’s performance, especially when there’s no ground truth. Learn all about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

TITLE: MLOps (Machine Learning Operations) Fundamentals

OUR TAKE: Offered by Google Cloud, this 100 percent online training features flexible deadlines on intermediate-level subject matter. It takes roughly 16 hours to complete.

Platform: Coursera

Description: This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

TITLE: Applied Machine Learning: Foundations

OUR TAKE: Data scientist Derek Jedamski specializes in machine learning and shows students the Python programming language, machine learning techniques and data cleaning examples.

Platform: LinkedIn Learning

Description: In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.

TITLE: Demistifying Machine Learning Operations (MLOps)

OUR TAKE: This intermediate-level Pluralsight training is just two hours long and will teach you the main concerns and issues to consider while developing machine learning models after deployment.

Platform: Pluralsight

Description: In this course, Demystifying Machine Learning Operations (MLOps), you’ll learn to implement machine learning operations into your machine learning project. First, you’ll explore how to apply machine learning operations (MLOps) practices for your infrastructure. Next, you’ll discover how machine learning operations (MLOps) during model development. Finally, you’ll learn how to apply machine learning operations (MLOps) after model deployment.

TITLE: Become a Machine Learning Engineer for Microsoft Azure Nanodegree

OUR TAKE: Udacity’s nanodegree program takes roughly 3 months to complete (at 5-10 hours per week). Students should bring prior experience with Python, machine learning, and statistics for the greatest chance of success with this module.

Platform: Udacity

Description: In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom.

NOW READ: The Best Coursera Machine Learning Training to Consider

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