#4 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 4]

Key Takeaways

you've seen the machine learning project lifecycle let's briefly go over what you learned in the rest of this course even though i presented the lifecycle going from left to right i found that for learning these materials it'll be more efficient for you to start at the end go and start from deployment and then work backwards to modeling data and then scoping so in the rest of this week starting with the next video you learn about the most important ideas in deployment next week in week 2 you learn about modeling you may have learned about how to train the machine learning model from other courses in this video i'll share some new ideas that you may not have heard before of how to systematically use a data centric approach to be more efficient in how you improve the performance of your model then in the third and final week of this course you learn about data how to define data and establish a baseline and how to label and organize your data in a way that is systematic not ad hoc not hacking around in the jupyter notebook in the hope that you stumble on the right insights but in a more systematic way that helps you be more efficient in defining the data that will help the modeling to help you get to deployment and then finally in week three we'll also have an optional section on scoping in which i hope to share with you some tips i've learned on how to define effective machine learning projects throughout this course you also learn about ml ops or machine learning operations which is an emerging discipline that comprises a set of tools and principles to support progress through the machine learning project life cycle but especially these three steps for example at landing ai where on co we used to do a lot of these steps manually which is okay but slow but after building an emma ops 2 called landing lens for computer vision applications all these steps became much quicker the key idea in ml ops is that systematic ways to think about scoping data modeling and deployment and also software tools to support the best practices so that's it in this course we're going to start at the end goal start from deployment and then work our way backwards as you already know being the deploy a system is one of the most important and valuable skills in machine learning today so let's go on to the next video where we'll dive deep into the most important lessons most important ideas needed to deploy machine learning systems i will see you in the next video