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Tag: ml

  • Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not?

    Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? A case study on techniques to maximize your clusters The post Scaling ML Inference on Databricks: Liquid or Partitioned? Salted or Not? appeared first on Towards Data Science. Hector Mejia Go to original source

  • The Evolving Role of the ML Engineer

    The Evolving Role of the ML Engineer Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and how her day-to-day work changed with the rise of LLMs. The post The Evolving Role of the ML Engineer appeared first on Towards Data Science. TDS Editors Go to original source

  • Causal ML for the Aspiring Data Scientist

    Causal ML for the Aspiring Data Scientist An accessible introduction to causal inference and ML The post Causal ML for the Aspiring Data Scientist appeared first on Towards Data Science. Ross Lauterbach Go to original source

  • Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1

    Azure ML vs. AWS SageMaker: A Deep Dive into Model Training — Part 1 Compare Azure ML and AWS SageMaker for scalable model training, focusing on project setup, permission management, and data storage patterns, to align platform choices with existing cloud ecosystem and preferred MLOps workflows The post Azure ML vs. AWS SageMaker: A Deep…

  • Why Your ML Model Works in Training But Fails in Production

    Why Your ML Model Works in Training But Fails in Production Hard lessons from building production ML systems where data leaks, defaults lie, populations shift, and time does not behave the way we expect. The post Why Your ML Model Works in Training But Fails in Production appeared first on Towards Data Science. Sudheer Singamsetty…

  • Don’t Build an ML Portfolio Without These Projects

    Don’t Build an ML Portfolio Without These Projects What recruiters are looking for in machine learning portfolios The post Don’t Build an ML Portfolio Without These Projects appeared first on Towards Data Science. Egor Howell Go to original source

  • MLE coding rounds (UK)

    MLE coding rounds (UK) I’m a data scientist transitioning to ML-AI Engineer roles. What kind of coding questions-rounds should I expect? I’ve heard that it’s a mixed bag, can be leetcode, can be Pytorch,tf for all ML related I’ve also heard about building ML concepts-algos from scratch using numpy etc. Or even an ML pipeline…

  • Learning, Hacking, and Shipping ML

    Learning, Hacking, and Shipping ML Vyacheslav Efimov on AI hackathons, data science roadmaps, and how AI meaningfully changed day-to-day ML Engineer work The post Learning, Hacking, and Shipping ML appeared first on Towards Data Science. TDS Editors Go to original source

  • 🚀 Perpetual ML Suite: Now Live on the Snowflake Marketplace!

    🚀 Perpetual ML Suite: Now Live on the Snowflake Marketplace! submitted by /u/mutlu_simsek [link] [comments] /u/mutlu_simsek Go to original source

  • Advice for DS/AS/MLE interviews

    Advice for DS/AS/MLE interviews I am looking for data scientist (ML heavy), applied scientist or ML engineer roles in product based companies. For my interview preperation, I am unsure about which book or resources to pick so that I can cover the rigor of ML rounds in these interviews. I have background in CS and…

  • Build Algorithm-Agnostic ML Pipelines in a Breeze

    Build Algorithm-Agnostic ML Pipelines in a Breeze The framework is now an open-source Python package for streamlined ML workflows The post Build Algorithm-Agnostic ML Pipelines in a Breeze appeared first on Towards Data Science. Mena Wang Go to original source

  • Is ML/AI engineering increasingly becoming less focused on model training and more focused on integrating LLMs to build web apps?

    Is ML/AI engineering increasingly becoming less focused on model training and more focused on integrating LLMs to build web apps? One thing I’ve noticed recently is that increasingly, a lot of AI/ML roles seem to be focused on ways to integrate LLMs to build web apps that automate some kind of task, e.g. chatbot with…

  • Pipelining AI/ML Training Workloads with CUDA Streams

    Pipelining AI/ML Training Workloads with CUDA Streams PyTorch Model Performance Analysis and Optimization — Part 9 The post Pipelining AI/ML Training Workloads with CUDA Streams appeared first on Towards Data Science. Chaim Rand Go to original source

  • ML case study rounds

    ML case study rounds I am asking this from context of interview. In almost every company these days, there is an ML case study round where the focus is on solving a real world case study. Idk if this is somewhat similar to ML system design or not (I think ML system design rounds are…

  • 2025 stack check: which DS/ML tools am I missing?

    2025 stack check: which DS/ML tools am I missing? Hi all, I work in ad-tech, where my job is to improve the product with data-driven algorithms, mostly on tabular datasets (CTR models, bidding, attribution, the usual). Current work stack (quite classic I guess) pandas, numpy, scikit-learn, xgboost, statsmodels PyTorch (light use) JupyterLab & notebooks matplotlib,…

  • Mixed-Integer Optimization for Responsible Machine Learning

    Mixed-Integer Optimization for Responsible Machine Learning arXiv:2505.05857v1 Announce Type: cross Abstract: In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice and finance. But its deployment in increasingly sophisticated, critical, and sensitive areas affecting individuals, the groups they belong to,…

  • Uncertainty Quantification in Machine Learning with an Easy Python Interface

    Uncertainty Quantification in Machine Learning with an Easy Python Interface Uncertainty quantification (UQ) in a Machine Learning (ML) model allows one to estimate the precision of its predictions. This is extremely important for utilizing its predictions in real-world tasks. For instance, if a machine learning model is trained to predict a property of a material,…

  • The Ultimate AI/ML Roadmap For Beginners

    The Ultimate AI/ML Roadmap For Beginners AI is transforming the way businesses operate, and nearly every company is exploring how to leverage this technology. As a result, the demand for AI and machine learning skills has skyrocketed in recent years. With nearly four years of experience in AI/ML, I’ve decided to create the ultimate guide…

  • ML Feature Management: A Practical Evolution Guide

    ML Feature Management: A Practical Evolution Guide In the world of machine learning, we obsess over model architectures, training pipelines, and hyper-parameter tuning, yet often overlook a fundamental aspect: how our features live and breathe throughout their lifecycle. From in-memory calculations that vanish after each prediction to the challenge of reproducing exact feature values months…

  • tHe wINdoWs mL EcOsYteM

    tHe wINdoWs mL EcOsYteM submitted by /u/Hire_Ryan_Today [link] [comments] /u/Hire_Ryan_Today Go to original source

  • ML pipeline questions

    ML pipeline questions I am building an application that processes videos and that needs to run many tasks (some need to be sequentially and some in parallel). Think audio extraction, ASR, diarization, translation, video classification, etc… Note that this is in supposed to be run online, i.e. this is supposed to be used in a…

  • How (and Where) ML Beginners Can Find Papers

    How (and Where) ML Beginners Can Find Papers From conferences to surveys Continue reading on Towards Data Science » Pascal Janetzky Go to original source