​Alexey Grigorev, the course creator, will officially start the new cohort of the [Machine Learning Zoomcamp](https://airtable.com/appflP5cuR8bD5MIm/shryxwLd0COOEaqXo) in this live session. He’ll walk you through the course structure, key topics, and what you’ll build.
​What You’ll Learn During the Session

​Alexey will walk you through:

* ​What’s included in the course: topics, tools, and hands-on projects
* ​How assignments, feedback, and scoring work
* ​How to approach the material, even if you’re new to ML
* ​What it’s like to learn together with thousands of learners in the DataTalks.Club community

​You’ll also have a chance to ask Alexey your questions live.
​
​**Thinking About ML Zoomcamp?**
​ML Zoomcamp is a free 4-month course designed to take you from beginner to advanced ML engineer. It covers the entire ML pipeline, from fundamentals like regression and classification to deployment and deep learning.
​The new cohort of the ML Zoomcamp starts on September 15, 2025. You can join it by [registering here](https://airtable.com/appflP5cuR8bD5MIm/shryxwLd0COOEaqXo).
​
​**About the Speaker**

​**Alexey Grigorev** is the Founder of DataTalks.Club and creator of the Zoomcamp series.
​Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge.

​**Join our slack: https://datatalks.club/slack.htm**

Berlin DataTalksClub Group

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Heramb Nishad

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Puneet Sharma

Marcia Cabral

Torsten

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maria

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Sarita Das

Ievgen (Gene) Ishchuk

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Piotr Laskowski

Bhavya Goyal

ML Zoomcamp 2025 Course Launch

Online event

**How do you learn machine learning while studying medicine and turn it into a full-time career? - Pastor Soto**

​In this episode, we’re joined by Pastor Soto, a Machine Learning Engineer who learned ML through focused online learning and consistent public work. While still in medical school, Pastor took on side projects that led him into machine learning, and the [ML Zoomcamp course](https://datatalks.club/blog/machine-learning-zoomcamp.html) served as a turning point in his career.

​By sharing his work online, Pastor opened the door to job interviews, including at Meta and DeepLearning.AI.

​We plan to cover:

* ​How Pastor learned ML while studying medicine
* ​How ML Zoomcamp helped Pastor to get interviews at companies like Meta and DeepLearning AI
* ​Lessons for time-constrained learners: small wins, public updates, and ROI
* ​How to position a non-traditional background (like an MD) in AI interviews

​
About the speaker:
​
**Pastor Soto** is a Machine Learning Engineer and mentor with a practical bias toward shipping. While studying medicine at the university, he took on side work that pulled him into machine learning. The inflection point was **ML Zoomcamp**: by publishing exercises and projects publicly, he attracted interviews and job offers.

​Pastor’s work centers on production ML: moving data, wiring APIs, and feeding LLMs so applications run. He mentors with **DeepLearning.AI**, leads sections for **Stanford’s Code in Place**, and writes about focused learning and small, finished steps that compound.

**Join our slack: https://datatalks.club/slack.html**

Tomoko Okada

Muhammad

Kushala R S 

Jayita Gulati

Elalfy Mohamed

Marc Navarro Sotés

Mekuria Tegegne

Ranamad

Yulia Vilensky

nikolinacrnogorac99

swari.tamboli

nandu subash

Haripriya Ganesh

Priyanshu Gupta

islatnia24

From Medicine to Machine Learning: How Public Learning Turned into a Career

**A low-cost, auto-scaling approach to serving ML models - Alexey Grigorev**

​This is the second workshop in our ML series on ML model deployment.

​Building on the FastAPI service created in Part 1, we’ll now show how to deploy that service using Kubernetes, the industry standard for managing containerized applications in production.

​In this hands-on session, you’ll learn how to deploy machine learning models using serverless infrastructure, specifically AWS Lambda. Serverless deployment is a great alternative to managing your own infrastructure, ideal for lightweight models or infrequent inference.

​This session walks you through packaging your model, creating a Lambda function, and exposing it via API Gateway.

​ What You'll Learn

* ​What serverless means and when to use it
* ​How AWS Lambda works for ML model serving
* ​How to prepare your model for deployment with TensorFlow Lite
* ​How to package your code and dependencies in a Docker image
* ​How to expose your Lambda function using API Gateway

​It will be a live demo with practical tips and a chance to ask your questions. This workshop gives you a real feel for how ML models are deployed in real-world environments.

​**Thinking About ML Zoomcamp?**
This workshop reflects topics from Module 9 in ML Zoomcamp, our free 4-month course that takes you from beginner to advanced ML engineer. It covers the fundamentals of ML, from regression and classification to deployment and deep learning.

​The new cohort of the ML Zoomcamp starts on September 15, 2025. You can join it by registering here.

​**About the Speaker**
​**Alexey Grigorev** is the Founder of DataTalks.Club and creator of the Zoomcamp series.

​Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge.
​

**Join our slack: https://datatalks.club/slack.html**

Seirana

Ioana

Ameen siddiqui

Nicolas Tomatis

Vlad-Emil Petrea

Ashwanth

KIRTI ANAND

Felix Pupo

Yacine Touati

Adam Vajda

Moussa Ndour

Jorge Nacer

meesha

Prashanth 

Huseyin OZDOGAN

Deploying ML Models with AWS Lambda (Serverless)

Curious about the [Machine Learning Zoomcamp](https://airtable.com/appflP5cuR8bD5MIm/shryxwLd0COOEaqXo)? Join us for a live, interactive Q&A session with course creator Alexey Grigorev and get all your questions answered before the new cohort begins on September 15, 2025.

​What You’ll Learn During the Session

​Alexey will walk you through:

* ​**Course curriculum**: An overview of the topics, tools, and real-world projects you'll work on
* ​**Assignments and grading**: How homework, quizzes, and peer reviews are structured
* ​**Getting help and feedback**: How support works during the course and in the community
* ​**Portfolio-building projects**: Capstone and midterm projects to showcase your skills
* ​**Learning with the community**: How to engage with 70k+ learners in the DataTalks.Club Slack

​He’ll also share tips on how to follow the material effectively, pace your learning, and stay motivated throughout the course.
​You’ll get a chance to meet the instructor, learn more about the course structure, and ask your questions.
​
​**Thinking About ML Zoomcamp?**
​ML Zoomcamp is a free 4-month course designed to take you from beginner to advanced ML engineer. It covers the entire ML pipeline, from fundamentals like regression and classification to deployment and deep learning.

​The new cohort of the ML Zoomcamp starts on September 15, 2025. You
can join it by [registering here](https://airtable.com/appflP5cuR8bD5MIm/shryxwLd0COOEaqXo).

​**About the Speaker**

​**Alexey Grigorev** is the Founder of DataTalks.Club and creator of the Zoomcamp series.

​Alexey is a seasoned software and ML engineer with over 10 years in engineering and 6+ years in machine learning. He has deployed large-scale ML systems at companies like OLX Group and Simplaex, authored several technical books including Machine Learning Bookcamp, and is a Kaggle Master with a 1st place finish in the NIPS'17 Criteo Challenge.

**Join our slack: https://datatalks.club/slack.html**

Aftab Alam

Dmitry P

Zoryana Paliy

Nadia Babienko

Olaf

Mircea Manole

ML Zoomcamp 2025 Pre-Course Live Q&A

**What Generative AI Has Changed and Which Fundamentals Still Matter - Vadim Smolyakov**

​Vadim’s path spans from algorithmic ML and MIT research to building Copilot at Microsoft, providing him with a front-row view of how practice has shifted from hand-built algorithms to agentic systems.

​In this conversation, he reflects on what genuinely changed with generative AI and what fundamentals still matter.

​We’ll discuss assistants as complements to human thinking, why embodiment (“physical AI”) may be the next step, and how goal-setting and sharing knowledge shape work that lasts.

​We plan to cover:

* ​What Copilot taught about scope, trade-offs, and evaluation
* ​Agents as assistants: avoiding over-reliance while gaining real leverage
* ​Fine-tuning, synthetic data, and how the craft of ML has evolved
* ​Physical AI: what to build in vs. what to learn from the environment
* ​Purpose, habits, and leaving durable digital/physical artifacts

​
​**About the speaker**

​**Vadim Smolyakov** is a Machine Learning Engineer at Microsoft, working on Copilot AI, an author of Machine Learning Algorithms in Depth, and a former MIT PhD student. He focuses on generative AI (agents, RL), self-development, knowledge sharing, and the societal impact of intelligent systems.

​**Join our slack: https://datatalks.club/slack.html**

Alik (Oleksandr) Golub

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גל דהן

Daniel Skolimowski

Istrati Traian

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Ladislav Macejak

Ajai

Giovanna

Marcel H.

Matthias Prellwitz

Saber Ben taher (Solsoft)

Thanos

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Abila Annamalai

Jesi Fre

Batul Khatib

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From Algorithms to Agents: Lessons from Building Copilot

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