Algorithmia in Action: Take your ML models from training to production
summary
Algorithmia is an MLOps platform that demo deploys regression, gradient-boosting, image-classification, sentiment and time-series models from notebooks into a centralized model catalog, containerizing code with a one-click build and publish to create immutable versions. It supports seven languages and 14 client SDKs, native connectors to S3/GCS/Azure, GPU sharing, event listeners (SQS/Service Bus), and pipeline chaining for multi-step inference. Runtime handles auto-scaling and observability; a Kafka-backed metrics pipeline ships inference metrics to Datadog/Grafana for drift detection and retraining triggers.
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chapters (11)
- 0:00Introduction to workshop
- 1:48Algorithmia overview and challenges
- 8:31ML lifecycle and workflow
- 12:40Platform concepts and setup
- 22:29From training to production
- 35:54Consuming models externally
- 39:03Building model pipelines
- 43:13Usage reports and compliance
- 45:38Model performance monitoring
- 48:05Summary and key benefits
- 51:15Q&A session
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