How to: Talks About MLOps
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1. ["MLOps: Automated Machine Learning" by Emmanuel Raj](https://www.youtube.com/watch?v=m32k9jcY4pY)
1. [DeliveryConf 2020. "Continuous Delivery For Machine Learning: Patterns And Pains" by Emily Gorcenski](https://youtu.be/bFW5mZmj0nQ)
1. [MLOps Conference: Talks from 2019](https://www.mlopsconf.com?wix-vod-comp-id=comp-k1ry4afh)
1. [Kubecon 2019: Flyte: Cloud Native Machine Learning and Data Processing Platform](https://www.youtube.com/watch?v=KdUJGSP1h9U)
1. [Kubecon 2019: Running LargeScale Stateful workloads on Kubernetes at Lyft](https://www.youtube.com/watch?v=ECeVQoble0g)
1. [A CI/CD Framework for Production Machine Learning at Massive Scale (using Jenkins X and Seldon Core)](https://youtu.be/68_Phxwaj-k)
1. [MLOps Virtual Event (Databricks)](https://youtu.be/9Ehh7Vl7ByM)
1. [MLOps NY conference 2019](https://www.iguazio.com/mlops-nyc-sessions/)
1. [MLOps.community YouTube Channel](https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ)
1. [MLinProduction YouTube Channel](https://www.youtube.com/channel/UC3B_Z9FTeu4i8xtxDjGaZxw)
1. [Introducing MLflow for End-to-End Machine Learning on Databricks. Spark+AI Summit 2020. Sean Owen](https://youtu.be/nx3yFzx_nHI)
1. [MLOps Tutorial #1: Intro to Continuous Integration for ML](https://youtu.be/9BgIDqAzfuA)
1. [Machine Learning At Speed: Operationalizing ML For Real-Time Data Streams (2019)](https://youtu.be/46l_C7ibpuo)
1. [Damian Brady - The emerging field of MLops](https://humansofai.podbean.com/e/damian-brady-the-emerging-field-of-mlops/)
1. [MLOps - Entwurf, Entwicklung, Betrieb (INNOQ Podcast in German)](https://www.innoq.com/en/podcast/076-mlops/)
1. [Instrumentation, Observability & Monitoring of Machine Learning Models](https://www.infoq.com/presentations/instrumentation-observability-monitoring-ml/)
1. [Efficient ML engineering: Tools and best practices](https://learning.oreilly.com/videos/oreilly-strata-data/9781492050681/9781492050681-video327465?autoplay=false)
1. [Beyond the jupyter notebook: how to build data science products](https://towardsdatascience.com/beyond-the-jupyter-notebook-how-to-build-data-science-products-50d942fc25d8)
1. [An introduction to MLOps on Google Cloud](https://www.youtube.com/watch?v=6gdrwFMaEZ0#action=share) (First 19 min are vendor-, language-, and framework-agnostic. @visenger)
1. [How ML Breaks: A Decade of Outages for One Large ML Pipeline](https://youtu.be/hBMHohkRgAA)
1. [Clean Machine Learning Code: Practical Software Engineering](https://youtu.be/PEjTAJHxYPM)
1. [Machine Learning Engineering: 10 Fundamentale Praktiken](https://www.youtube.com/watch?v=VYlXNWxqJ2A)
1. [Architecture of machine learning systems (3-part series)](https://www.youtube.com/playlist?list=PLx8omXiw3n9y26FKZLV5ScyS52D_c29QN)
1. [Machine Learning Design Patterns](https://youtu.be/udXjlvCFusc)
1. [The laylist that covers techniques and approaches for model deployment on to production](https://youtube.com/playlist?list=PL3N9eeOlCrP5PlN1jwOB3jVZE6nYTVswk)
1. [ML Observability: A Critical Piece in Ensuring Responsible AI (Arize AI at Re-Work)](https://www.youtube.com/watch?v=2FE1sg749V[o)
1. [ML Engineering vs. Data Science (Arize AI Un/Summit)](https://www.youtube.com/watch?v=lP_4lT2k7Kg&t=2s)
1. [SRE for ML: The First 10 Years and the Next 10 ](https://www.usenix.org/conference/srecon21/presentation/underwood-sre-ml)
1. [Demystifying Machine Learning in Production: Reasoning about a Large-Scale ML Platform](https://www.usenix.org/conference/srecon21/presentation/mcglohon)
1. [Apply Conf 2022](https://www.applyconf.com/apply-conf-may-2022/)
1. [Databricks' Data + AI Summit 2022](https://databricks.com/dataaisummit/north-america-2022)
1. [RE•WORK MLOps Summit 2022](https://www.re-work.co/events/mlops-summit-2022)
1. [Annual MLOps World Conference](https://mlopsworld.com/)