Welcome back to “Building Real World AI applications” – our 4-part MLOPS (Building Successful AI Applications in Production) series.
In the previous part you learned about planning for success: what are the things you need in deciding on research , data and model planning. Only then can you build applications that are robust and sustainable.
In this part 5 MLOPs series, you will learn the art of designing Effective MLOPs pipelines, for instance can you imagine to ship 100s or even 1000s of models into production and everything works like a charm? Also , can you imagine doing it all in matter of seconds or minutes instead of hours, days and weeks?
Even back in 1913 when Ford was shipping its popular Model T, it cut the time it took to build each car from 12 to 3 hours. This was great for the manpower that he had for that day but today Tesla is shipping cars much faster since all that data of the production process is in machines.
So, just like physical manufacturing process, a software manufacturing process — especially when you have Machine Learning solution, needs to be a clear sequence of well-defined steps (which we also call a pipeline. Today the rising cost of IT (know as Technical Debt) will further worsen as more tools will be pushed into our faces – which we’re calling MLOPs debt, will be added to it and it may make it impossible for companies to operate in a highly competitive AI economy.
If we can bring this cost down while making it a seamless and efficient operation, then we are poised for a great future for all those applications in production.
Thanks and talk soon,
Tarry and deepkapha.ai’s MLOPs team
In this 4-part mini-series, Tarry will guide you through designing, developing, testing and finally deploying AI applications. Along with the LiveAI team the best practices, tools and techniques are shared, to help you gain confidence in building and deploying AI applications in production.
It’s one thing to have a few AI Models running in production, but it requires a a mature organization to run thousands of such systems in production.
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