Have you heard of GEPA? 🔥 GEPA has quickly become one of the most popular prompt optimization techniques in AI—and through our collaboration with @DSPyOSS, #MLflow now makes it easier than ever to apply GEPA to your agents. MLflow brings GEPA to where your traces and eval

END-TO-END MLOPS PIPELINE. I'm working on an end-to-end MLOps project covering: 1. Data Ingestion 2. Feature Engineering 3. Training (parent/child models) 4. Inference using ONNX 5. MLflow for visualization and model registry 6. API design for ML projects 7. Docker 8.

Databricks announced a suite of compelling MLflow-powered capabilities. Specifically for evaluating the accuracy and performance of AI agents. These enhancements introduce LLM-as-a-Judge features. https://t.co/hiLwZ08SuH

"MLOps with Databricks" 12 lectures covering everything from MLflow basics to model deployment, monitoring, and CI/CD strategies - perfect for hands-on learning without any cost barriers Link in comments 😎👇

This FREE course teaches you MLOps with Databricks using their new Free Edition. 12 lectures covering everything from MLflow basics to model deployment, monitoring, and CI/CD strategies - perfect for hands-on learning without any cost barriers.

mlflow >> dvclive for experiment tracking ui of mlflow is smooth compared to dvclive which is very confusing

math/statistics ↓ python ↓ pandas ↓ numpy ↓ matplotlib ↓ mysql ↓ scikit learn ↓ xgboost ↓ pytorch ↓ transformers ↓ sbert ↓ fastapi ↓ dvc ↓ mlflow ↓ pyspark ↓ docker ↓ airflow ↓ ultralytics ↓ spacy ↓ opencv ↓ langchain ↓ qdrant congrats! ur now a full stack

MLOps is an essential skill every ML Engineer needs to build ML systems for business. If you're new to MLOps, this playlist is an absolute goldmine. You'll learn and build using: - Git & Github - DVC, MLFlow, DagsHub - CI/CD (on AWS) - Docker, Kubernetes for ML - Prometheus,

Full room, great energy, and tons of practical insights at yesterday’s Terminal-Bench 2.0 meetup in SF! 🙌 Danny Chiao (Engineering Lead, @Databricks) provided a hands‑on tour of building high‑quality agents faster with MLflow, and Alex Shaw (Member of Technical Staff,

Devops and Mlops exam done. I got the easy ones, rest were AWS. 1. Create flask app, docker build, containerize,push to dockerhub,CI/CD tests, failure logging and email service. 2. Dvc, model and mlflow tracking,fast api host endpoint, testing and failure email service.

tried connecting the local server through dagshub MLflow is really cool

Built Observa — a full-stack #MLOps project for NLP inference with MLflow, DVC, Grafana & Prometheus integration. Everything tracked, versioned & observable.

MLFlowとかWandBみたいな実験管理ツールが便利なのは分かるんだけど、Hydraみたいなパラメータの管理ツールっていまいち使いこなせないんだよな。ハイパーパラメータならOptunaとかで一番いいやつ使えばいいし、みんなどうやって使いこなしてるんだ

Here is the tech stack I'm currently using for building AI-based applications: • Python - The programming language • Google ADK - For building agents • Metaflow - Orchestration and pipelines • LiteLLM - To abstract away the use of specific models • MLflow - Experiments,

DSPy、ここにおったんかワレ！って思いました。MLflowのプロンプト最適化機能ではDSPyが使われています。 Databricksにおけるプロンプトの最適化 #MLflow - Qiita https://t.co/oDVoTQn4AB

This complete MLOps tutorial series will teach you how to build end-to-end machine learning pipelines from scratch. Covers Docker, FastAPI, MLflow, DVC, CI/CD with GitHub Actions, model deployment, and experiment tracking - everything you need for production ML systems. Link is

MLflow 3.6.0 just landed. Thanks to MLflow maintainers and the community. 🔗 Check it out: https://t.co/SI4fWECYkQ Big rocks include: 👉 Full OpenTelemetry Support in MLflow Tracking Server 👉 Session-level View in Trace UI 👉 New Supported Frameworks in TypeScript Tracing SDK

CVE-2025-11200 - #Pruva repro of MLflow Weak Password Requirements Authentication Bypass Vulnerability Report: https://t.co/LYfI3XQWYJ Advs: https://t.co/Iqb7EggMvC nice bug from @gothburz

I'm back Will learn and implement mlflow I'll try to integrate it to my project