A practical guide to running 8x RTX PRO 6000's

Maximize parallelism across 8x RTX PRO 6000's: high-concurrency inference, model fleets, and 70B fine-tuning.

How to Build an ML Pipeline With Python and SQL | Aman Kharwal

Learn how to build a practical ML pipeline using Python and SQL, from data extraction to model deployment, with real-world tips.

FineTune LLMs with 6 Open-Source Libraries | Greg Coquillo posted on the topic | LinkedIn

6 Open-Source Libraries to FineTune LLMs 1. Unsloth GitHub: https://lnkd.in/gQvPWVFb → Fastest way to fine-tune LLMs locally → Optimized for low VRAM (even laptops) → Plug-and-play with Hugging Face models 2. Axolotl GitHub: https://lnkd.in/g34kuExT → Flexible LLM fine-tuning…

Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing) - MarkTechPost

Safely Deploying Machine Learning Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)

Note: This bookmark, **[Safely Deploying ML Models to Production: Four Controlled Strategies](#rid:1652757915)**, is an article from MarkTechPost that outlines how to release machine learning models into production while minimizing risk. The article details four key deployment strategies: * **A/B Testing**: Randomly splits a small percentage of total traffic to the new model while the rest stays on the…

A Complete End-to-End Coding Guide to MLflow Experiment Tracking, Hyperparameter Optimization, Model Evaluation, and Live Model Deployment…

Weights & Biases: A KDnuggets Crash Course - KDnuggets

A hands-on guide to tracking experiments, versioning models, and keeping your ML projects reproducible with Weights & Biases.

10 Must-Know Python Libraries for MLOps in 2025

In this article, we’ll explore 10 Python libraries that every machine learning professional should know in 2025.

Note: 1. MLflow tracks ML experiments and models. It makes it easy to compare results and share models with your team. Experiment Tracking: Track and compare multiple runs of your machine learning experiments. Model Packaging: Package code in a standard format using a MLproject file. Model Registry: A centralized store for managing lifecycle stages of models. 2. Data Version Control (DVC) lets you…

thoughtworks/mlops-platforms: Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow…

Compare MLOps Platforms. Breakdowns of SageMaker, VertexAI, AzureML, Dataiku, Databricks, h2o, kubeflow, mlflow... - thoughtworks/mlops-platforms

Easy MLOps with PyCaret + MLflow - KDnuggets

A beginner-friendly, step-by-step tutorial on integrating MLOps in your Machine Learning experiments using PyCaret.