Episode description
Episode description
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:
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