The quantity of hype around machine learning and AI is probably second only to bitcoins and blockchains. But until a machine learning model is deployed to production the value delivered to companies is approximately equal to zero. Together with the common mantra that data science can't use agile/lean frameworks or that the best software engineering practises don't apply explains a lot about why often companies got burnt with their data science projects and why generally they under delivered.
MLOps is here to help, the machine-learning equivalent of DevOps: it solves the problems of implementing machine-learning in production. During this talk I will introduce the data science lifecycle, the concept of machine learning Ops, its characteristics, why is extremely required, how it compares to DevOps, how it will become a required capabilities for any DS/ML/AI Team, tools available and how you can start with it.