Machine Learning Operations is the most hyped topic these days in the data community, but let's have a talk without much hype. Shall we? In this one, I am going to discuss stages of the machine learning project with emphasis on practical aspects of each stage. In 30 minutes, you will learn about scoping, data, modelling and deployment parts of the ML project.
Machine Learning Operations is the most hyped topic these days, but let's have a talk without much hype. Shall we? In this one, I am going to discuss stages of the machine learning project with emphasis on practical aspects of each stage. In these 30 minutes you will learn about scoping, data, modelling and deployment parts of the ML project. Proper understanding of these stages will allow a smooth transition of your Untitled_final.ipynb to production deployment bringing value to your business stakeholders. The focus of this talk is to guide the audience throughout the ML lifecycle considering best practices and advice for the following stages:
By the end of this talk, you will know which important questions to ask before starting ML project, answers on which will boost development cycle. We will also briefly touch upon tooling for MLOPS and ML team composition. The takeaway of tips and best practices with help you to set current and future ML projects on the rails of success.