As enterprise AI matures, the focus of practitioners is starting to shift from the practice of model creation to a more holistic view of productizing AI to generate real business value for the business. The most significant problem in data science today is not about how to find the best algorithm and parameters, but rather about deploying models as part of an application, to generate real, measurable business impact.
The common approach of developing models in a data science silo leads to waste of valuable resources and lengthy route to production.
Instead of a siloed, complex and manual process, start by designing the production pipeline using a modular strategy, where the different parts provide a continuous, automated, and far simpler way to move from research to scalable production pipelines, without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering.
In this whitepaper, you’ll learn:
- The different definitions for MLOps in the market today
- The prevailing paradigm of the research-first approach, and its pitfalls
- Why enterprises are opting for adopting a production-first approach to data science, to enable continuous rollout of new AI services
- The four key components of every production pipeline:
- Feature Store
- ML CI/CD
- Real-time application pipeline
- Real-time data and model monitoring
