In this episode, I'm speaking with Roey Mechrez from BeyondMinds. Roey holds a Ph.D. in Electrical Engineering, with vast experience in computer vision and deep learning research. We discuss the challenges of gluing together infrastructure solutions for an end-to-end ML platform, as well as generating monitoring insights for non-technical stakeholders and combating catastrophic forgetting. Join our Discord community: https://discord.gg/tEYvqxwhah ---  Timestamps:  00:00 Podcast intro 01:00 Guest intro  01:49 What does BeyondMinds do?  06:24 Audience for an end-to-end ML platform  12:14 Communicating with non-technical stakeholders/users  15:03 The future of "AI-powered tools", and human-machine collaboration  20:04 On complex system orchestration, generating insights from monitoring, and catastrophic forgetting – Biggest challenges in production ML  25:23 Why is catastrophic forgetting a hard problem and how do you deal with it?  30:02 "Secret" tips on how to get started with automating the retraining process  33:30 Generating monitoring insights and observations in a user-friendly format  38:12 Making data labeling issues explainable (automatically)  45:07 Customizing complex systems per user – Orchestrating an ML platform  52:58 API design in ML platform components  55:45 Measuring success for researchers, ML engineers, and software developers – can ML work fit into the Agile workflow.  1:02:22 Is "time to production" a good metric? Gains in time to production in the real world  1:06:02 How do you divide the work between ML researchers and engineers?  1:08:39 Recommendations for the audience ---  Relevant Links:  A16z blog about AI Data Science work in an agile environment –  A talk by Dima Goldenberg Hayot Kis (Hebrew Podcast) חיות כיס Data Engineering Podcast ACX Podcast Social Links: https://www.linkedin.com/company/beyondminds/ https://www.linkedin.com/company/dagshub/ https://twitter.com/roeyme https://twitter.com/DeanPlbn https://twitter.com/TheRealDAGsHub

Ran Romano, VP Engineering at Qwak.ai, discusses the evolution of job titles in machine learning, challenges of using Jupyter notebooks in production, and the importance of adopting a CI/CD approach. They also talk about the challenges in scaling ML models to production, ensuring data reproducibility, and using open source solutions in their ML platform.

In this episode, I'm speaking with Julien Chaumond from 🤗 HuggingFace, about how they got started, getting large language models to production in millisecond inference times, and the CERN for machine learning. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps:  01:00 - Guest intro 02:14 - Origin of HuggingFace 05:37 - Why the focus on NLP? 07:45 - The success of the HuggingFace community 13:14 - Reproducing models and scaling for the community 18:14 - Enabling large models in production 23:14 - How HuggingFace scales so many models 27:34 - The biggest challenge HuggingFace solved in MLOps 32:02 - How HuggingFace transitions from research to production 34:44 - Using notebooks vs python modules 38:27 - The most interesting topic in ML production 40:10 - Fascinating ML research 45:24 - Learning new things 51:14 - Something that is true but most people disagree with 56:54 - Tips to organize research teams 1:00:05 - New features for accelerated inference 1:01:35 - Most common use case of HuggingFace 1:04:17 - Integrating search algorithms into transformer library 1:05:09 - Integrating vision models 1:06:06 - Long term business model 1:10:55 - Automation and simplification of the process of building models 1:13:02 - Support for real-time inference 1:14:40 - Recommendations for the audience --- Relevant Links: FastDS: https://github.com/DAGsHub/fds BigScience: https://bigscience.huggingface.co https://www.linkedin.com/company/dagshub/ https://www.linkedin.com/company/huggingface/ https://twitter.com/TheRealDAGsHub https://twitter.com/huggingface

In this episode, I'm speaking with Urszula Czerwinska about her path as a data scientist, the projects she worked on, experiences gained as a data scientist, as well as the challenges she's overcome in bringing her machine learning (ML) into production. Join our Discord community: https://discord.gg/tEYvqxwhah --- Timestamps: 0:00 - Podcast intro 1:15 - Guest intro and how you got into data science 3:48 - Finding your fit – research or industry and when to transition 7:23 - What types of ML projects do you specialize in 10:41 - ML explainability and interpretability 15:26 - ML explainability with non-technical stakeholders 17:13 - What problems does your team solve within the organization 20:56 - ML in production – how to bring your ML projects from research to production 25:17 - The tools you can't live without 28:11 - Do you have a set process for productizing ML projects 30:08 - Team structures and communication for data science teams 33:42 - Who's in charge of setting up infrastructure for a project and job title discussion 36:29 - Interesting tools and repositories you work with 39:30 - How do you stay up to date 42:00 - Biggest challenges for you in ML 45:12 - Favorite and least favorite thing about being a data scientist 49:52 - Handling a workplace that doesn't understand what a data scientist is 53:07 - Data scientists are 🦄 53:30 Good papers you read recently 58:12 - Tips to improve the data science workflow   Relevant Links: - flair: https://github.com/flairNLP/flair - AllenNLP: https://github.com/allenai/allennlp - Papers with Code: https://paperswithcode.com/ - Dair.ai newsletter: https://dair.ai/newsletter/ - HuggingFace: https://huggingface.co/blog