Building a RAG chain using LangChain Expression Language (LCEL) - Learning the building blocks of LCEL to develop increasingly complex RAG chains by roshan santhosh buff.ly/4cVRwDB

If you need to train a specialized LLM and are looking for a resource-conscious approach, Cameron R. Wolfe, Ph.D. walks us through several options in his latest deep dive, paying special attention to Low-Rank Adaptation (LoRA). buff.ly/49Hfell

'For people who are interested in transitioning to a data science or AI role, find any way to complete a successful data science project.'

Based on his experience prior to joining LinkedIn, Jimmy Wong stresses the importance of hands-on projects. buff.ly/49CNSfY

What's the best way to help your non-technical colleagues learn some SQL for their daily workflows? Jordan Gomes shares insights based on his own experience running internal trainings. buff.ly/3T9NoXz

Follow along Rhys Goldstein's fascinating new deep dive to discover five 3D grid neighborhoods you can use to solve AI problems like navigation and visibility in 3D. buff.ly/4a6xiVw

Gender bias has been a perennial issue in AI systems and LLM outputs. Yennie Jun provides a thorough overview of research into this issue, and discusses 'what it means to 'fix' bias.' buff.ly/3v2qdGI

How well do LLMs perform when conducting numeric evaluations? Aparna Dhinakaran shares findings from her recent experimentations and unpacks some nontrivial issues and limitations to keep in mind. buff.ly/3IpzJa9

In the emerging era of AI development, what should be the focal points for data science teams?

The Role of Data Science in Democratizing AI by Lior Sidi buff.ly/48GJz24

Using household income data, James Koh presents a full visualization workflow you can apply and adapt to your own dataset, too. buff.ly/3v0R0mL

In a recent article, Marco Peixeiro explored the architecture of Time-LLM and how it can effectively allow an LLM to predict time series data. buff.ly/49DUGdd

Do recent advances in AI represent a major leap forward — or will we end up considering them underwhelming? Stephanie Kirmer takes a step back to reflect on the bigger — and more complex — picture. buff.ly/3UnDeDF

In the latest installment of his series exploring Microsoft Fabric workflows, Roger Noble takes a close, hands-on look at feature engineering with Dataflow Gen2. buff.ly/4aVWzCk

Exploring causality in Python. Difference-in-differences by Łukasz Szubelak buff.ly/4d0GFs6

How to Grow Your Career Without Feeling Stuck - Whether you are just starting up or aspiring to make another leap ensure that you are ready and your boss knows that by Aliaksei Mikhailiuk,PhD buff.ly/3W2mvb6

Learn about the structure of LangChain pipelines, callbacks, how to create custom callbacks and integrate them into your pipelines for improved monitoring - by roshan santhosh buff.ly/3W3OmaZ

'I’ve collected some of the most important data points around climate change. And in this blog post, I’ll share with you the status quo through the lens of a data nerd 📈.'

Can AI ‘Fix’ Climate Change? Perspective of a Data Nerd by Thu Vu buff.ly/4aDKvGc

Learn how you can estimate a population's size given a discrete uniform distribution — Dorian Drost's latest explainer is an accessible introduction to the topic of sampling and statistical estimates. buff.ly/3Io9T6e

'As AI technology progresses such is the expectation to adopt it and build smarter AI-driven products and we as AI-experts hold the responsibility in supporting it across the organization.'

My latest post on Towards Data Science

towardsdatascience.com/the-role-of-da…

In part two of his exploration of lifelong machine learning, Anand Majmudar zooms in on two recent arrivals: Efficient Lifelong Learning Algorithm (ELLA) and Voyager. buff.ly/3vXNJVV

Excited to have my article on #LLM deployment shared with Towards Data Science. Check it out 👇🏻