00:00 Introduction

01:10 Max's deep experience in search and how he transitioned from structured data

08:28 Query-term dependence problem and Max's perception of the Vector Search field

12:46 Is vector search a solution looking for a problem?

20:16 How to move embeddings computation from GPU to CPU and retain GPU latency?

27:51 Plug-in neural model into Java? Example with a Hugging Face model

33:02 Web-server Mighty and its philosophy

35:33 How Mighty compares to in-DB embedding layer, like Weavite or Vespa

39:40 The importance of fault-tolerance in search backends

43:31 Unit economics of Mighty

50:18 Mighty distribution and supported operating systems

54:57 The secret sauce behind Mighty's insane fast-ness

59:48 What a customer is paying for when buying Mighty

1:01:45 How will Max track the usage of Mighty: is it commercial or research use?

1:04:39 Role of Open Source Community to grow business

1:10:58 Max's vision for Mighty connectors to popular vector databases

1:18:09 What tooling is missing beyond Mighty in vector search pipelines

1:22:34 Fine-tuning models, metric learning and Max's call for partnerships

1:26:37 MLOps perspective of neural pipelines and Mighty's role in it

1:30:04 Mighty vs AWS Inferentia vs Hugging Face Infinity

1:35:50 What's left in ML for those who are not into Python

1:40:50 The philosophical (and magical) question of WHY

1:48:15 Announcements from Max

25% discount for the first year of using Mighty in your great product / project with promo code VECTOR:

https://bit.ly/3QekTWE

Show notes:

- Max's blog about BERT and search relevance: https://opensourceconnections.com/blog/2019/11/05/understanding-bert-and-search-relevance/

- Case study and unit economics of Mighty: https://max.io/blog/encoding-the-federal-register.html

- Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696

Watch on YouTube: https://youtu.be/LnF4hbl1cE4