Demystifying Machine Learning Operations (MLOps)

by Mohammed Osman

Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.

What you'll learn

Machine Learning is a robust science that can empower the business with unique competitive advantages to address several challenges, such as sales price prediction, customer segment classification, and product recommendation. 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. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations needed to manage the MLOps lifecycle of your project.

About the author

Mohammed Osman is a senior software engineer who started coding at the age of 13. Mohammed worked in various industries, including telecommunication, accounting, banking, health, and assurance. Mohammed's core skillset is a .NET ecosystem with a strong focus on C#, Azure, and Data Science. Mohammed also enjoys the soft-side of software engineering and leads scrum teams. Mohammed runs a blog with the message "Making your code smart and your career smarter." He shares tips and techniques to improve your code and valuable career pieces of advice in his blog.

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