Malte Pietsch - CTO, Deepset - Passion in NLP and bridging the academia-industry gap with Haystack

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