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Dynamic Scaling for Intensive Workloads
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Classroom Contents
MLOps Automation with Git Based CI-CD for ML
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- 1 Intro
- 2 80% of AI Projects Never Make it to Produc
- 3 Did you Try Running Notebooks in Product
- 4 Model and Code Development are Just the First Step
- 5 Example: Predictive Maintenance Pipeline
- 6 You can use Separate Tools & Services, Or you can use Kubernetes as the Baseline
- 7 What is an Automated ML Pipeline ?
- 8 Under The Hood: Open, Scalable, Production Ready
- 9 Serverless Simplicity, Maximum Performance
- 10 Serverless: Resource Elasticity, Automated Deployment and Operations
- 11 Dynamic Scaling for Intensive Workloads
- 12 KubeFlow: Automated ML Pipelines & Tracking
- 13 Simple, Production-Ready Development Process
- 14 Building CI/CD Process for ML(Ops)
- 15 Traditional Fraud-Detection Architecture (Hadoop)
- 16 Real-Time Fraud Prediction & Prevention
