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.
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.
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.
Course Overview Hi everyone. My name is Mohammed Osman, and welcome to my course, Demystifying Machine Learning Operations, or in short MLOps. I am a senior software developer and a data science enthusiast at SmarterCode. Machine learning is a robust science that can empower the business with a unique competitive advantage to address several challenges, such as sales price prediction, customer segment classification, and product recommendation. However, to convert a valuable machine learning experiment developed by a brilliant data scientist to a useful production model is a tough challenge that must be solved in order to cultivate machine learning benefits. That is what we call machine learning operations. In this course, we are going to see how to convert a machine learning experiment from a simple notebook to a fully functional production machine learning solution, running in the cloud and managed through the modern machine learning practices. Some of the major topics that we will cover include what's machine learning operations and its benefits, the best practices to create the infrastructure for your machine learning projects, the best practices when developing your machine learning models, and, finally, the best practices when deploying your models to production. By the end of this course, you will know how to convert a simple machine learning notebook to a modern machine learning project that utilizes MLOps practices, such as infrastructure as a code, source control, model monitoring, and many other useful techniques. You will learn to do that by making your hands dirty and implementing a real solution. Before joining the course, you should be familiar with machine learning. I hope you will join me on this journey to learn machine learning operations with the Demystifying Machine Learning Operations at Pluralsight.