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- By Richa Bhatia
Putting models at the core of business process is the underlying principle for success for data science projects. One of the most talked about components in the Data Science Lifecycle is operationalizing models and as businesses get more model-driven, there’s a greater need for efficient model deployment.
Data Science Lifecycle Starts With
Preparing data
Engineer features
Training, building and testing models
Deploying the best performing model
A common refrain among data science teams is the lack of model management and the need to build new capabilities that can help in operationalising models. Building more models means utilsing more data and these mode
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Richa Bhatia
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.
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