Realities & Misconceptions about Enterprise Applications of AI/ML and Data
summary
Claims that enterprise AI succeeds on hype alone are rebutted with specifics: a Gartner stat shows only 53% of AI projects move from prototype to production, so emphasis is on MLOps pipelines (CI/CD, GitHub Actions, Azure ML, Synapse, cloud-agnostic Azure/AWS deployments) and stewardship—automated retraining, observability, reproducibility to prevent model decay and bias. Three concrete builds include abattoir computer vision for provenance and recalls, PipeAI for pipe-crack detection with geolocated CCTV, and a student dropout predictor reaching ~96% accuracy. Practical timelines and cost of durable ML infrastructure are detailed.
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chapters (4)
- 0:00Start
- 0:06Realities & Misconceptions about Enterprise Applications
- 7:18Myth: Code is the most important part of Al/Miand Data Science
- 12:46Myth: Companies are good at moving from Proof of Concept to Production
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