Operationalizing machine learning for the enterprise
summary
Covers how enterprises move models from notebooks into COBOL and legacy apps using IBM tools such as Watson deep learning APIs and TensorFlow with R and Python integration, plus a proposed fast scoring engine with API calls. Describes concrete needs: model lifecycle tracking (who deployed where and when), real time retraining to prevent staleness, data cleaning examples like inferring gender from exam records, and a DataSense product aiming to replace brittle rules engines with deployed ML.
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chapters (4)
- 0:00Introduction
- 3:18How do you get from competition mentality to real
- 6:58Common challenges
- 10:42How IBM can improve machine learning
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