How to Productionize ML Models at Scale
Artificial intelligence (AI) and machine learning (ML) are quickly becoming the de-facto tool to affect the bottom line of the organization. Despite the industry being in exponential growth in recent years, 85% of trained machine learning models are never deployed in the real world.
(Source: NewVantage Partners)
The widest gap is between data scientists and IT. Organizations often cite a wide variety of complexities when they want to use AI / ML models in operation with production scale data in the real world – from data management, integration, security and real-time analytics.
Key discussion topics:
- Implications of large scale models in operations
- Challenges in taking models to production
- Best practices of ML to production
- Data Engineering Maturity
- Operationalizing in the cloud
- Sigmoid’s cutting edge approach