Fantastic episode of'Talk Python Podcast' on'Python at Netflix' featured two guests from Netflix's Python Infrastructure team, Amjith Ramanujam and Srinivasan Ramanujam 📺
youtube.com/watch?v=yPBslO…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps

Meta / Facebook has released their 'approach to explaining (ML) ranking' 💡
This includes how posts are ranked in newsfeed, recommendations, ranking of comments, friend recommendations, notifications and more.

transparency.fb.com/features/expla…

#ML #MachineLearning

New free online course on 'Large Language Models: Application through Production' 💡

A great resource for developers, data scientists, and engineers who aim to build applications powered by large language models.

edx.org/course/large-l…

#ML #MachineLearning

Future of Custom & Fine-Tuned LLMs 🔮 A great article from Outerbounds, the company behind Metaflow, which explores instruction tuning for large language models.
outerbounds.com/blog/custom-ll…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps

ML System Design Case Studies 🖥️ Evidently AI created a fantastic resource containing 200 real-word case studies of ML Systems from 64 companies including Netflix, Airbnb, and Doordash.

evidentlyai.com/ml-system-desi…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops

🚀 This week in Machine Learning at Scale 🚀 In this edition we cover:

1) ML System Design Case Studies
2) Future of Custom Tuned LLMs
3) LLM App to Prod Databaricks
4) + more 🚀

linkedin.com/pulse/issue-23…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps

Dealing with Train-serve Skew in Real-time ML Models: A Short Guide 🔭

A comprehensive article that demystifies the training-serving skew on ML models, which arises due to differences between training and serving environments in real-time ML models

building.nubank.com.br/dealing-with-t…

#ML

Stanford has put together an informative repo which introduces the 'Foundation Model Transparency Index'; a framework which rates major AI providers like OpenAI and Google on their adherence to requirements specified in the EU Parliament's AI Act.

Repo: github.com/stanford-crfm/…

New course from Andrew Ng's DeepLearning.AI in collaboration with AWS on Generative AI 🚀

It includes data gathering, model selection, performance evaluation, deployment, and how to adapt and optimize models to various use cases.

deeplearning.ai/courses/genera…

#ML #AI

The UK Government has adopted 3 of the 4 recommendations we made for the initial UK AI Regulation proposal; We have submitted further recommendations as part of the current open policy consultation for the UK AI Regulation Proposal 🚀

linkedin.com/feed/update/ur…

#ML #AI #ML Ops

🚀 This week in Machine Learning at Scale 🚀

1) UK Government AI Regulation
2) Andrew Ng Generative AI Course
3) MLOps at Reasonable Scale
4) Foundation Model Transparency
5) Train Serve Skew in ML Models
6) Upcoming MLOps Events
7) + more 🚀

linkedin.com/pulse/issue-23…

#ML

8 annoying A/B testing mistakes every engineer should know 💡 This article highlights common A/B testing pitfalls as well as useful learnings.

posthog.com/blog/ab-testin…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps #augmentedintelligence #deeplearning

MITRE ML Security Mitigations 🥷 The article outlines a set of security mitigation strategies for machine learning systems based on MITRE ATLAS case studies.

atlas.mitre.org/mitigations/

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps #augmentedintelligence

Playing with Streamlit & LLMs 🎮 A great practical insight diving into practical LLM applications by Will Lethain, author of 'The Staff Engineer'.

lethain.com/streamlit-llms/

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps #augmentedintelligence

The state of production ML in the 2023 Python ecosystem 💡 Our PyData Talk is now up in youtube!

youtube.com/watch?v=xRFX5t…

#ML #MachineLearning #ArtificialIntelligence #AI #ML Ops #AI Ops #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python