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Putting a similarity index into production at scale is a pretty hard challenge. It requires a whole bunch of infrastructure working closely together. You need to handle a large amount of data at low latency. It introduces you to topics like sharding, hashing, trees, load balancing, efficient data transfer, data replication, and much more. Check out the notebook and the article on how to get started with Google Cloud Vertex AI Matching Engine 📓 Notebook: https://colab.research.google.com/dri... 📖 Article: / all-you-need-to-know-about-google-vertex-a... If you enjoyed this video, please subscribe to the channel ❤️ 🎉 Subscribe for Article and Video Updates! / subscribe / membership You can find me here: LinkedIn: / saschaheyer Twitter: / 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 00:32 Statement 00:47 Use Cases 01:25 Embedding 01:47 Input 02:23 Types 02:54 VPC 04:05 Create Embeddings 06:50 Setup 07:00 VPC Setup 08:39 Create Index 11:58 Create Endpoint 13:00 Deploy Index 14:23 Update Index 15:10 Scale Index 16:46 Query 22:23 Bye