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Fine-Tuning LLMs at Scale With Databricks MLflow and Spark

Why Fine-Tune on Databricks? General-purpose LLMs like Llama 3, Mistral, or Falcon are impressive out of the box — but they underperform on domain-specific tasks: medical coding, l...

Monitoring discriminative ML models using Amazon SageMaker AI with MLflow

Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows...

Streaming benchmark and recommendation results to MLflow with Amazon SageMaker AI

In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark jobs to automatical...

Azure Databricks for Scalable MLOps and Feature Engineering With Apache Spark, Delta Lake, and MLflow

Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't afford to run pandas in a notebo...

Top MLOps Tools in 2026

The top 11 MLOps tools for 2026, MLflow, Kubeflow, SageMaker, Vertex AI, and more, compared by features, pricing, and best-fit use cases.

Your on-device LLM tests are slow, flaky, and can’t run in CI. Here’s the fix.

llm_replay_eval records on-device inference once, then replays it forever fast, offline, deterministic. Plus an LLM-as-judge that replays…Continue reading on Medium »

Streamline external access to Amazon SageMaker MLflow using a REST API proxy

In this post, we demonstrate how to build a secure Flask-based MLflow proxy service that provides HTTPS access to Amazon SageMaker MLflow without requiring the MLflow SDK. This sol...

How to Build Your First MLOps Pipeline

Build a complete MLOps pipeline in 90 minutes with MLflow 3, DVC, FastAPI, and Docker. Hands-on tutorial with working code and monitoring.

Model Monitoring in MLOps: Tools, Metrics, and Best Practices

Monitor ML models in production: catch data drift, prediction drift, and performance decay with top tools and a runnable Evidently drift check.

Ruby on Rails for MLOps: A Complete Guide to ML Deployment

Originally appeared on RailsCarma – Ruby on Rails Development Company specializing in Offshore Development. Machine Learning is one...

HoloViz: HoloViz for LLMs

MLOps Nedir? Modeli Laboratuvardan Гњretime TaЕџД±mak

MLOps Serisi — Yazı 1/4Continue reading on Medium В»

Federated Learning on Snowflake: Training ML Models Across Accounts Without Sharing Data

When a single hospital develops a diabetes prediction model, the amount of available training data is inherently limited. In general…Continue reading on Snowflake Builders Blog: Da...

Things I Learned Building an End-to-End ML Pipeline on Kubernetes: From Validated Data to Live…

Part 2 of an MLOps End-to-End series — 60 models, fully automated, one Airflow DAGContinue reading on Medium »

Reliable LLM Inference at Scale

At Databricks, we’ve built a unique inference platform that serves every frontier...

Using Kubernetes for MLOps

Run MLOps on Kubernetes: train with Kubeflow Trainer, serve with KServe, schedule GPUs with Kueue, autoscale with KEDA, and ship via GitOps.

Inkling model from Thinking Machines Lab now on Databricks

We are excited to announce Databricks as a day zero launch partner for Thinking Machines Lab (TML)...

How to Fine-Tune LFM2 Using QLoRA and DPO: A Complete Step-by-Step Coding Tutorial on Google Colab

Learn to fine-tune LFM2 with QLoRA, supervised fine-tuning, DPO, and adapter merging using TRL and PEFT on Colab. The post How to Fine-Tune LFM2 Using QLoRA and DPO: A Complete Ste...

ML IMP QUESTIONS DISCUSSION

Supervised LearningContinue reading on Medium »

ML Jobs in Snowflake Data Clean Rooms Now GA

ML Jobs in Snowflake Data Clean Rooms is now generally available, enabling collaborative model training and scoring across multiparty data without moving raw records.

LLM Fallbacks Break Agent Pipelines — I Built the Missing Recovery Layer

LLM rate limits don't just interrupt agent pipelines—they can silently corrupt structured outputs when fallback models receive incompatible payloads. I built a recovery layer that...

Snowflake Postgres Powers Low-Latency ML Feature Serving

Snowflake's ML team chose Snowflake Postgres to power their Online Feature Store — demonstrating 2.5x lower latency and 7x higher QPS than Databricks Lakebase in production benchma...

Predicting Workout Ratings with Machine Learning: A Practical End-to-End Guide

From a gym dataset to a deployed model, step by step.Continue reading on Medium »

LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does

In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...