Episode description
Topics:
00:00 Introduction
01:12 Malte’s background
07:58 NLP crossing paths with Search
11:20 Product discovery: early stage repetitive use cases pre-dating Haystack
16:25 Acyclic directed graph for modeling a complex search pipeline
18:22 Early integrations with Vector Databases
20:09 Aha!-use case in Haystack
23:23 Capabilities of Haystack today
30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders
39:00 Examples of value for the end-users of Deepset Cloud
46:00 Success metrics
50:35 Where Haystack is taking us beyond MLOps for search experimentation
57:13 Haystack as a smart assistant to guide experiments
1:02:49 Multimodality
1:05:53 Future of the Vector Search / NLP field: large language models
1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic
1:16:25 The magical question of WHY
1:23:47 Announcements from Malte
Show notes:
- Haystack: https://github.com/deepset-ai/haystack/
- Deepset Cloud: https://www.deepset.ai/deepset-cloud
- Tutorial: Build Your First QA System: https://haystack.deepset.ai/tutorials/v0.5.0/first-qa-system
- Open NLP Meetup on Sep 29th (Nils Reimers talking about “Incorporating New Knowledge Into LMs”): https://www.meetup.com/open-nlp-meetup/events/287159377/
- Atlas Paper (Few shot learning with retrieval augmented large language models): https://arxiv.org/abs/2208.03299
- Tweet from Patrick Lewis: https://twitter.com/PSH_Lewis/status/1556642671569125378
- Zero click search: https://www.searchmetrics.com/glossary/zero-click-searches/
Very large LMs:
- 540B PaLM by Google: https://lnkd.in/eajsjCMr
- 11B Atlas by Meta: https://lnkd.in/eENzNkrG
- 20B AlexaTM by Amazon: https://lnkd.in/eyBaZDTy
- Players in Vector Search: https://www.youtube.com/watch?v=8IOpgmXf5r8 https://dmitry-kan.medium.com/players-in-vector-search-video-2fd390d00d6
- Click Residual: A Query Success Metric: https://observer.wunderwood.org/2022/08/08/click-residual-a-query-success-metric/
- Tutorials and papers around incorporating Knowledge into Language Models: https://cs.stanford.edu/people/cgzhu/
Podcast design: Saurabh Rai https://twitter.com/srvbhr