DataTalks.Club

By DataTalks.ClubTechnology

DataTalks.Club episodes

September 08, 2023Pragmatic and Standardized MLOps - Maria Vechtomova
We talked about:
Maria's background

Marvelous MLOps

Maria's definition of MLOps

Alternate team setups without a central MLOps team

Pragmatic vs non-pragmatic MLOps

Must-have ML tools (categories)

Maturity assessment

What to start with in MLOps

Standardized MLOps

Convincing DevOps to implement

Understanding what the tools are used for instead of knowing all the tools

Maria's next project plans

Is LLM Ops a thing?

What Ahold Delhaize does

Resource recommendations to learn more about MLOps

The importance of data engineering knowledge for ML engineers
Links:
LinkedIn: https://www.linkedin.com/company/marvelous-mlops/
Website: https://marvelousmlops.substack.com/
Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
54 min
August 25, 2023Democratizing Causality - Aleksander Molak
We talked about:
Aleksander's background

Aleksander as a Causal Ambassador

Using causality to make decisions

Counterfactuals and and Judea Pearl

Meta-learners vs classical ML models

Average treatment effect

Reducing causal bias, the super efficient estimator, and model uplifting

Metrics for evaluating a causal model vs a traditional ML model

Is the added complexity of a causal model worth implementing?

Utilizing LLMs in causal models (text as outcome)

Text as treatment and style extraction

The viability of A/B tests in causal models

Graphical structures and nonparametric identification

Aleksander's resource recommendations
Links:
The Book of Why: https://amzn.to/3OZpvBk

Causal Inference and Discovery in Python: https://amzn.to/46Pperr

Book's GitHub repo: https://github.com/PacktPublishing/Causal-Inference-and-Discovery-in-Python

The Battle of Giants: Causality vs NLP (PyData Berlin 2023): https://www.youtube.com/watch?v=Bd1XtGZhnmw

New Frontiers in Causal NLP (papers repo): https://bit.ly/3N0TFTL

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
56 min
August 18, 2023Mastering Data Engineering as a Remote Worker - José María Sánchez Salas
We talked about:
José's background

How José relocated to Norway and his schedule

Tech companies in Norway and José role

Challenges of working as a remote data engineer

José's newsletter on how to make use of data

The process of making data useful

Where José gets inspiration for his newsletter

Dealing with burnout

When in Norway, do as the Norwegians do

The legalities of working remotely in Norway

The benefits of working remotely

Links:
LinkedIn: https://www.linkedin.com/in/jmssalas

Github: https://github.com/jmssalas

Website & Newsletter: https://jmssalas.com

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
47 min
August 04, 2023The Good, the Bad and the Ugly of GPT - Sandra Kublik
We talked about:
Sandra's background

Making a YouTube channel to break into the LLM space

The business cases for LLMs

LLMs as amplifiers

The befits of keeping a human in the loop when using LLMs (AI limitations)

Using LLMs as assistants

Building an app that uses an LLM

Prompt whisperers and how to improve your prompts

Sandra's 7-day LLM experiment

Sandra's LLM content recommendations

Finding Sandra online

Links:
LinkedIn: https://www.linkedin.com/in/sandrakublik/

Twitter: https://twitter.com/sandra_kublik

Youtube: https://www.youtube.com/@sandra_kublik

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
51 min
July 28, 2023LLMs for Everyone - Meryem Arik
We talked about:
Meryam's background

The constant evolution of startups

How Meryam became interested in LLMs

What is an LLM (generative vs non-generative models)?

Why LLMs are important

Open source models vs API models

What TitanML does

How fine-tuning a model helps in LLM use cases

Fine-tuning generative models

How generative models change the landscape of human work

How to adjust models over time

Vector databases and LLMs

How to choose an open source LLM or an API

Measuring input data quality

Meryam's resource recommendations

Links:
Website: https://www.titanml.co/

Beta docs: https://titanml.gitbook.io/iris-documentation/overview/guide-to-titanml...

Using llama2.0 in TitanML Blog: https://medium.com/@TitanML/the-easiest-way-to-fine-tune-and-inference-llama-2-0-8d8900a57d57

Discord: https://discord.gg/83RmHTjZgf

Meryem LinkedIn: https://www.linkedin.com/in/meryemarik/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
56 min
July 21, 2023Investing in Open-Source Data Tools - Bela Wiertz
We talked about:
Bela's background

Why startups even need investors

Why open source is a viable go-to-market strategy

Building a bottom-up community

The investment thesis for the TKM Family Office and the blurriness of the funding round naming convention

Angel investors vs VC Funds vs family offices

Bela's investment criteria and GitHub stars as a metric

Inbound sourcing, outbound sourcing, and investor networking

Making a good impression on an investor

Balancing open and closed source parts of a product

The future of open source

Recent successes of open source companies

Bela's resource recommendations

Links:
Understand who is engaging with your open source project article: https://www.crowd.dev/

Top 6 Books on Developer Community Building: https://www.crowd.dev/post/top-6-books-on-developer-community-building

Which open source software metrics matter: https://www.bvp.com/atlas/measuring-the-engagement-of-an-open-source-software-community#Which-open-source-software-metrics-matter

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
55 min
July 14, 2023Why Machine Learning Design is Broken - Valerii Babushkin
Links:
Book: https://www.manning.com/books/machine-learning-system-design?utm_source=AGMLBookcamp&utm_medium=affiliate&utm_campaign=book_babushkin_machine_4_25_23&utm_content=twitter

Discount: poddatatalks21 (35% off)

Evidently: https://www.evidentlyai.com/

Article: https://medium.com/people-ai-engineering/design-documents-for-ml-models-bbcd30402ff7

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
52 min
July 07, 2023Interpretable AI and ML - Polina Mosolova
We talked about:
Polina's background

How common it is for PhD students to build ML pipelines end-to-end

Simultaneous PhD and industry experience

Support from both the academic and industry sides

How common the industrial PhD setup is and how to get into one

Organizational trust theory

How price relates to trust

How trust relates to explainability

The importance of actionability

Explainability vs interpretability vs actionability

Complex glass box models

Does the explainability of a model follow explainability?

What explainable AI bring to customers and end users

Can all trust be turned into KPI?

Links:
LinkedIn: https://www.linkedin.com/in/polina-mosolova/

Neural Additive Models paper: https://proceedings.neurips.cc/paper/2021/file/251bd0442dfcc53b5a761e050f8022b8-Paper.pdf

Neural Basis Model paper: https://arxiv.org/pdf/2205.14120.pdf

Interpretable Feature Spaces paper: https://kdd.org/exploration_files/vol24issue1_1._Interpretable_Feature_Spaces_revised.pdf
53 min
June 30, 2023From Scratch to Success: Building an MLOps Team and ML Platform - Simon Stiebellehner
We talked about:
Simon's background

What MLOps is and what it isn't

Skills needed to build an ML platform that serves 100s of models

Ranking the importance of skills

The point where you should think about building an ML platform

The importance of processes in ML platforms

Weighing your options with SaaS platforms

The exploratory setup, experiment tracking, and model registry

What comes after deployment?

Stitching tools together to create an ML platform

Keeping data governance in mind when building a platform

What comes first – the model or the platform?

Do MLOps engineers need to have deep knowledge of how models work?

Is API design important for MLOps?

Simon's recommendations for furthering MLOps knowledge

Links:
LinkedIn: https://www.linkedin.com/in/simonstiebellehner/

Github: https://github.com/stiebels

Medium: https://medium.com/@sistel

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
54 min
June 23, 2023From MLOps to DataOps - Santona Tuli
We talked about:
Santona's background

Focusing on data workflows

Upsolver vs DBT

ML pipelines vs Data pipelines

MLOps vs DataOps

Tools used for data pipelines and ML pipelines

The “modern data stack” and today's data ecosystem

Staging the data and the concept of a “lakehouse”

Transforming the data after staging

What happens after the modeling phase

Human-centric vs Machine-centric pipeline

Applying skills learned in academia to ML engineering

Crafting user personas based on real stories

A framework of curiosity

Santona's book and resource recommendations

Links:
LinkedIn: https://www.linkedin.com/in/santona-tuli/

Upsolver website: upsolver.com

Why we built a SQL-based solution to unify batch and stream workflows: https://www.upsolver.com/blog/why-we-built-a-sql-based-solution-to-unify-batch-and-stream-workflows

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp
Join DataTalks.Club: https://datatalks.club/slack.html
Our events: https://datatalks.club/events.html
54 min