MLOps for non-DevOps folks, a.k.a. “I have a model, now what?! - Hannes Hapke - ML4ALL 2019
MLOps for non-DevOps folks, a.k.a. “I have a model, now what?! Hannes Hapke The prospect of developing and training machine learning models on datasets is exciting. While many conference attendees understand the potential value of deploying machine learning models and may even have a model in mind, not all are aware of the frameworks and tools to needed release them in the real world. http://ml4all.org/schedule.html video by https://backpedal.tv
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MLflow Pipelines: Accelerating MLOps from Development to Production
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Building an ML Platform from Scratch: Live Coding Session // Alon Gubkin // MLOps Meetup #67
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Machine Learning Model Deployment: Strategy to Implementation
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Time-Series Behavioral Analysis for Churn Prediction - Damon Danieli - ML4ALL 2019
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Andrew Ng: Bridging AI's Proof-of-Concept to Production Gap
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MLOps meetup #14 // Kubeflow vs MLflow with Byron Allen
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Webinar: MLOps automation with Git Based CI/CD for ML
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Build, Train and Deploy Machine Learning Models on AWS with Amazon SageMaker - AWS Online Tech Talks
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MLOps explained | Machine Learning Essentials
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Ease ML deployments with TensorFlow Serving - Cambridge ML Summit ‘19
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MLOps: How to Bring Your Data Science to Production - BDL2026
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Model Biases & Data Cleaning - Matt Beatty - ML4ALL 2019
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Workflow & MLOps for batch scoring applications with DVC, MLflow and Airflow, Mikhail Rozhkov
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How to build a MLOps platform
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MLOps Tutorial #3: Track ML models with Git & GitHub Actions
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AWS re:Invent 2020: Implementing MLOps practices with Amazon SageMaker
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MLflow: An Open Platform to Simplify the Machine Learning Lifecycle
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Build, Train, and Serve Your ML Models on Kubernetes with Kubeflow - Karl Weinmeister - ML4ALL 2019
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MLOps: Accelerating Data Science with DevOps - Microsoft
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