Serving Machine Learning models with Google Vertex AI

Deploying and serving any machine learning model at any scale. Vertex AI Endpoint provides great flexibility compared with easy usage. You can keep it simple or go full in and customize it to your needs using custom containers. 📓 Notebook: https://colab.research.google.com/drive/1I2q91xmEWxkPClCACdDDp0Gn8jqlok0s?usp=sharing 📖 Article: https://medium.com/google-cloud/serving-machine-learning-models-with-google-vertex-ai-5d9644ededa3 If you enjoyed this video, please subscribe to the channel ❤️ 🎉 Subscribe for Article and Video Updates! https://saschaheyer.medium.com/subscribe https://saschaheyer.medium.com/membership You can find me here: LinkedIn: https://www.linkedin.com/in/saschaheyer Twitter: https://twitter.com/heyersascha If you or your company is looking for advice on the cloud or ML, check out the company I work for. https://www.doit.com/ We offer consulting, workshops, and training at zero cost. Imagine an extension for your team without additional costs. #vertexai #googlecloud #machinelearning #mlengineer #doit ▬ My current recording equipment ▬▬▬▬▬▬▬▬ ► Camera for recording and streaming in 4K https://amzn.to/3QQzwiN ► Lens with nice background blur https://amzn.to/3dVDAjb ► Connect the camera to PC 4K https://amzn.to/3ciYyrE ► Light https://amzn.to/3Rb065M ► Most flexible way to mount your camera + mic https://amzn.to/3TedZC5 ► Microphone (I love it) https://amzn.to/3QV3mmb ► Audio Interface https://amzn.to/3CBxj5M Support my channel if you buy with those links on Amazon ▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 Introduction 00:39 Different ways 01:10 Pre-build container 01:50 Custom container 03:16 Model serving steps 03:40 Model upload 03:55 Model endpoint 04:10 Model deployment 04:38 Container requirements 06:08 Custom container I 07:02 Custom container II 07:59 Custom container III 08:40 Get predictions 08:51 Limitations 10:24 Pricing 10:50 Code 17:25 Bye