MLOps focuses on deploying and maintaining machine learning models in production. It includes:
Data engineering operations for gathering and preparing data
Model development operations for multiple iterations of train, test, validate, refine and release Simply put, MLOps brings the rigour of agile principles to machine learning projects.
Challenges of not using MLOps
Fundamentally, machine learning (ML) projects are different from typical software projects. Organisations run into a myriad of issues to effectively manage ML projects. Data scientists typically need to work on multiple iterations of machine learning models. Creating and managing models by following processes that are not optimised for ML can be a cumbersome process.
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