NASSCOM, in association with Genpact and EY, has compiled a guide for enterprises to enable effective setup, management, and scaling of ML operations. The pandemic has forced organisations across the world to move to the cloud and led to a proliferation of ML models to drive digitisation. However, merely 27% of AI projects move to production. Addressing the anomaly from challenges related to model development, iteration, deployment and monitoring is the need of the hour. The compendium will act as a blueprint for deploying MLOps in your organisation.
Key highlights:
- What is MLOps?
A set of practices and methodology used to automate ML model development, achieve automated and reliable ML model deployment.
- Need for MLOps?
It combines the best of automation, IT operations and management and Continuous Development & Continuous Integration (CI/CD) in Machine Learning and Artificial Intelligence.
- Benefits of MLOps?
Reduced time-to-market for ML products, improved RoI on AI/ML initiatives, advanced Data Management, etc.
- Dimensions of MLOps
The compilation brings out 6 pillars of MLOps classified under Implementation and business operations.
- Future of MLOps
MLOps promises standardisation of processes and methodologies and is expected to boost efficiencies in terms of cost, quality, and time to value.
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