Training Example: MLflow – Review the Data, Give Your Score & Compare to the Real AI Evaluation

[H1] Deliver High-Quality AI, Fast
Building AI products is all about iteration.MLflow lets you move 10x faster by simplifying how you debug, evaluate, and monitor your LLM applications, Agents, and Models.Try DemoGet Started30M+ Downloads/moLLMs & AgentsModel Training
[H3] Observability
Capture complete traces of your LLM applications and agents to get deep insights into their behavior. Built on OpenTelemetry and supports any LLM provider and agent framework. Monitor production quality, costs, and safety.Quickstart→Try Demo→
[IMG: Observability screenshot]
Code
[H3] Evaluation
Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own with highly flexible APIs.Automatically detect issues in your traces using AI-powered analysis across correctness, latency, execution, adherence, relevance, and safety dimensions.Quickstart→Try Demo→
[IMG: Evaluation screenshot 1]
[IMG: Evaluation screenshot 2]
Code
[H3] Prompts & Optimization
Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance.Quickstart→Try Demo→
[IMG: Prompts & Optimization screenshot]
Code
[H3] AI Gateway
Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through a unified OpenAI-compatible interface.Quickstart→
[IMG: AI Gateway screenshot]
Code
[H3] Agent Server
Deploy agents to production with a single command. The MLflow Agent Server provides a FastAPI-based hosting solution with automatic request validation, streaming support, and built-in tracing — so you can go from prototype to production endpoint in minutes.Quickstart→from mlflow.agent_server import AgentServer, invoke, streamfrom mlflow.types.agent import ResponsesAgentRequest, ResponsesAgentResponse
@invoke()async def run_agent(request: ResponsesAgentRequest) -> ResponsesAgentResponse: msgs = [i.model_dump() for i in request.input] result = await Runner.run(agent, msgs) return ResponsesAgentResponse( output=[item.to_input_item() for item in result.new_items] )
# Start the serveragent_server = AgentServer("MyAgent")agent_server.run(app_import_string="server:app")
[IMG: Observability screenshot]
CodeMost Adopted Open-Source AIOps PlatformBacked by Linux Foundation, MLflow has been fully committed to open-source for 5+ years. Now trusted by thousands of organizations and research teams worldwide to power their LLMOps and MLOps workflows.mlflow/mlflow30 Million+Package Downloads / MonthWorks With Any FrameworkFrom LLM agent frameworks to traditional ML libraries - MLflow integrates seamlessly with 100+ tools across the AI ecosystem. Supports Python, TypeScript/JavaScript, Java, R, and natively integrates with OpenTelemetry.Why Teams Choose MLflowFocus on building great AI, not managing infrastructure. MLflow handles the complexity so you can ship faster.
[H3] Open Source
100% open source under Apache 2.0 license. Forever free, no strings attached.
[H3] No Vendor Lock-in
Works with any cloud, framework, or tool you use. Switch vendors anytime.
[H3] Production Ready
Battle-tested at scale by Fortune 500 companies and thousands of teams.
[H3] Full Visibility
Complete tracking and observability for all your AI applications and agents.
[H3] Community
20K+ GitHub stars, 900+ contributors. Join the fastest-growing AIOps community.
[H3] Integrations
Works out of the box with LangChain, OpenAI, PyTorch, and 100+ AI frameworks.Get Started in 3 Simple StepsFrom zero to full-stack LLMOps in minutes. No complex setup or major code changes required.Get Started →1
[H3] Start MLflow Server
One command to get started. Docker setup is also available.bashuvx mlflow server~30 seconds2
[H3] Enable Logging
Add minimal code to start capturing traces, metrics, and parameterspythonimport mlflow
mlflow.set_tracking_uri( "http://localhost:5000")mlflow.openai.autolog()~30 seconds3
[H3] Run your code
Run your code as usual. Explore traces and metrics in the MLflow UI.pythonfrom openai import OpenAI
client = OpenAI()client.responses.create( model="gpt-5-mini", input="Hello!",)~1 minuteFrequently Asked QuestionsVisit our FAQ page for everything you need to know about MLflow.What is MLflow?MLflow is the largest open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data. With over 30 million monthly downloads, thousands of organizations rely on MLflow each day to ship AI to production with confidence.MLflow's comprehensive feature set for agents and LLM applications includes production-grade observability, evaluation, prompt management, prompt optimization, an AI Gateway for managing costs and model access, and more. Learn more at MLflow for LLMs and Agents.For machine learning (ML) model development, MLflow provides experiment tracking, model evaluation capabilities, a production model registry, and model deployment tools.Why do I need an AI engineering platform like MLflow?Is MLflow free?How does MLflow compare to other LLMOps/MLOps tools?Can I use MLflow with my existing AI infrastructure?Do I need to use Python to use MLflow?Can I use MLflow in my enterprise organization?BlogLatest newsView allMay 25, 2026
[H3] Route Claude Code Through MLflow AI Gateway
[IMG: Route Claude Code Through MLflow AI Gateway]
May 6, 2026
[H3] From Black Box to Observability: Tracing OpenClaw with MLflow
[IMG: From Black Box to Observability: Tracing OpenClaw with MLflow]
Apr 28, 2026
[H3] See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files
[IMG: See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files]
View allGET INVOLVEDConnect with the open source communityJoin millions of MLflow usersDocumentationRead DocsGitHub20k starsLinkedIn69k followersYouTubeView tutorialsXFollow us on XSlackJoin our Slack

Master the ML LifecycleFrom experimentation to production, MLflow for machine learning models streamlines your complete ML journey, with comprehensive experiment tracking, model management, and deployment.Try DemoGet Started30M+ Downloads/moBuild production quality modelsMLflow makes it easy to iterate toward production-ready models by organizing and comparing runs, helping teams refine training pipelines based on real performance insights.Learn more
[IMG: Building production quality ML models with MLflow]
Framework neutralWorks seamlessly with popular tools like scikit-learn, PyTorch, TensorFlow, and XGBoost without vendor lock-in, providing flexibility with a common interface.Learn more
[IMG: MLflow integrations with scikit-learn, PyTorch, TensorFlow, and XGBoost]
Reliable reproducibilityAutomatically logs parameters, weights, artifacts, code, metrics, and dependencies to ensure experiments can be restored accurately, enabling confident governance for enterprise deployments.Learn more
[IMG: MLflow logging parameters, artifacts, and dependencies for reproducible experiments]
Deployment readySimplifies the path from experimentation to production with a built-in registry that gives you complete control over model states, whether sharing new approaches or deploying solutions.Learn more
[IMG: MLflow model registry with lifecycle management and deployment controls]
Unified workflowMLflow streamlines your entire ML process with tracking, packaging, and deployment capabilities, eliminating tool fragmentation so you can focus on model development rather than infrastructureLearn more
[IMG: Unified ML workflow from experimentation to production]
[IMG: Building production quality ML models with MLflow]
Why us?Why MLflow is uniqueOpen, Flexible, and ExtensibleOpen-source and extensible, MLflow prevents vendor lock-in by integrating with the GenAI/ML ecosystem and using open protocols for data ownership, adapting to your existing and future stacks.Unified, End-to-End MLOps and AI ObservabilityMLflow offers a unified platform for the entire GenAI and ML model lifecycle, simplifying the experience and boosting collaboration by reducing tool integration friction.Framework neutralityMLflow's framework-agnostic design is one of its strongest differentiators. Unlike proprietary solutions that lock you into specific ecosystems, MLflow works seamlessly with all popular ML and GenAI frameworks.Enterprise adoptionMLflow's impact extends beyond its technical capabilities. Created by Databricks, it has become one of the most widely adopted MLOps tools in the industry, with integration support from major cloud providers.Get Started in 3 Simple StepsFrom zero to full experiment tracking in minutes. No complex setup required.Get Started →1
[H3] Start MLflow Server
One command to get started. Docker setup is also available.bashuvx mlflow server~30 seconds2
[H3] Enable Autologging
One line to automatically capture parameters, metrics, and models.pythonimport mlflow
mlflow.set_tracking_uri( "http://localhost:5000")mlflow.sklearn.autolog()~30 seconds3
[H3] Train Your Model
Train as usual. Explore runs, metrics, and models in the MLflow UI.pythonfrom sklearn.ensemble import ( RandomForestClassifier,)from sklearn.datasets import ( load_iris,)
X, y = load_iris(return_X_y=True)clf = RandomForestClassifier()clf.fit(X, y)~1 minuteBlogLatest newsView allMay 25, 2026
[H3] Route Claude Code Through MLflow AI Gateway
[IMG: Route Claude Code Through MLflow AI Gateway]
May 6, 2026
[H3] From Black Box to Observability: Tracing OpenClaw with MLflow
[IMG: From Black Box to Observability: Tracing OpenClaw with MLflow]
Apr 28, 2026
[H3] See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files
[IMG: See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files]
View allGET INVOLVEDConnect with the open source communityJoin millions of MLflow usersDocumentationRead DocsGitHub20k starsLinkedIn69k followersYouTubeView tutorialsXFollow us on XSlackJoin our Slack

FeaturedRoute Claude Code Through MLflow AI GatewayLearn how to route Claude Code through MLflow AI Gateway to get full observability, budget controls, and guardrails across all your coding agent sessions, with no changes to how you use Claude Code.
[IMG: Route Claude Code Through MLflow AI Gateway]
[IMG: From Black Box to Observability: Tracing OpenClaw with MLflow]
May 6, 2026From Black Box to Observability: Tracing OpenClaw with MLflow
[IMG: See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files]
Apr 28, 2026See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files
[IMG: AI observability for production: Seeing Inside Your Multi-Agent System with MLflow]
Apr 24, 2026AI observability for production: Seeing Inside Your Multi-Agent System with MLflow
[IMG: Structuring AI Evaluation and Observability with MLflow: From Development to Production]
Apr 22, 2026Structuring AI Evaluation and Observability with MLflow: From Development to Production
[IMG: Enforce Content Policies at the Gateway with AI Gateway Guardrails]
Apr 21, 2026Enforce Content Policies at the Gateway with AI Gateway Guardrails
[IMG: How to Prevent Runaway Agent Costs with MLflow AI Gateway]
Apr 9, 2026How to Prevent Runaway Agent Costs with MLflow AI Gateway
[IMG: Tired of Reviewing Traces? Meet Automatic Issue Detection for Your Agent]
Apr 9, 2026Tired of Reviewing Traces? Meet Automatic Issue Detection for Your Agent
[IMG: Harness Your OpenHands Agent with AI Observability and Governance]
Mar 24, 2026Harness Your OpenHands Agent with AI Observability and Governance
[IMG: Testing and Refining Claude Code Skills with MLflow]
Mar 23, 2026Testing and Refining Claude Code Skills with MLflow

Open Source AI Engineering PlatformConfidently ship agents and LLM applications to production with built-in observability, evaluation, prompt management, monitoring, cost controls, and much more.Try DemoGet Started30M+ Downloads/mo
[H3] Observability
Capture complete traces of your LLM applications and agents to get deep insights into their behavior. Built on OpenTelemetry and supports any LLM provider and agent framework. Monitor production quality, costs, and safety.Quickstart→Try Demo→
[IMG: Observability screenshot]
Code
[H3] Evaluation
Run systematic evaluations, track quality metrics over time, and catch regressions before they reach production. Choose from 50+ built-in metrics and LLM judges, or define your own with highly flexible APIs.Automatically detect issues in your traces using AI-powered analysis across correctness, latency, execution, adherence, relevance, and safety dimensions.Quickstart→Try Demo→
[IMG: Evaluation screenshot 1]
[IMG: Evaluation screenshot 2]
Code
[H3] Prompts & Optimization
Version, test, and deploy prompts with full lineage tracking. Automatically optimize prompts with state-of-the-art algorithms to improve performance.Quickstart→Try Demo→
[IMG: Prompts & Optimization screenshot]
Code
[H3] AI Gateway
Unified API gateway for all LLM providers. Route requests, manage rate limits, handle fallbacks, and control costs through a unified OpenAI-compatible interface.Quickstart→
[IMG: AI Gateway screenshot]
Code
[H3] Agent Server
Deploy agents to production with a single command. The MLflow Agent Server provides a FastAPI-based hosting solution with automatic request validation, streaming support, and built-in tracing — so you can go from prototype to production endpoint in minutes.Quickstart→from mlflow.agent_server import AgentServer, invoke, streamfrom mlflow.types.agent import ResponsesAgentRequest, ResponsesAgentResponse
@invoke()async def run_agent(request: ResponsesAgentRequest) -> ResponsesAgentResponse: msgs = [i.model_dump() for i in request.input] result = await Runner.run(agent, msgs) return ResponsesAgentResponse( output=[item.to_input_item() for item in result.new_items] )
# Start the serveragent_server = AgentServer("MyAgent")agent_server.run(app_import_string="server:app")
[IMG: Observability screenshot]
CodeMost Adopted Open Source AI PlatformBacked by Linux Foundation, MLflow has been fully committed to open source for 5+ years. Trusted by thousands of organizations and research teams worldwide to power their LLMOps workflows.mlflow/mlflow30 Million+Package Downloads / MonthWorks with Any LLM and Agent FrameworkFrom LLM providers to agent frameworks — MLflow integrates seamlessly with 100+ tools across the AI ecosystem. Supports any programming language and natively integrates with OpenTelemetry and MCP.Why Teams Choose MLflowFocus on building great AI, not managing infrastructure. MLflow handles the complexity so you can ship faster.
[H3] Open Source
100% open source under Apache 2.0 license. Forever free, no strings attached.
[H3] No Vendor Lock-in
Works with any cloud, framework, or tool you use. Switch vendors anytime.
[H3] Production Ready
Battle-tested at scale by Fortune 500 companies and thousands of teams.
[H3] Full Visibility
Complete tracking and observability for all your AI applications and agents.
[H3] Community
20K+ GitHub stars, 900+ contributors. Join the fastest-growing LLMOps community.
[H3] Integrations
Works out of the box with LangChain, OpenAI, PyTorch, and 100+ AI frameworks.Get Started in 3 Simple StepsFrom zero to production-ready agents in minutes. No complex setup or major code changes required.Get Started →1
[H3] Start MLflow Server
One command to get started. Docker setup is also available.bashuvx mlflow server~30 seconds2
[H3] Enable Logging
Add minimal code to start capturing traces, metrics, and parameterspythonimport mlflow
mlflow.set_tracking_uri( "http://localhost:5000")mlflow.openai.autolog()~30 seconds3
[H3] Run your code
Run your code as usual. Explore traces and metrics in the MLflow UI.pythonfrom openai import OpenAI
client = OpenAI()client.responses.create( model="gpt-5-mini", input="Hello!",)~1 minuteBlogLatest newsView allMay 25, 2026
[H3] Route Claude Code Through MLflow AI Gateway
[IMG: Route Claude Code Through MLflow AI Gateway]
May 6, 2026
[H3] From Black Box to Observability: Tracing OpenClaw with MLflow
[IMG: From Black Box to Observability: Tracing OpenClaw with MLflow]
Apr 28, 2026
[H3] See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files
[IMG: See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files]
View allGET INVOLVEDConnect with the open source communityJoin millions of MLflow usersDocumentationRead DocsGitHub20k starsLinkedIn69k followersYouTubeView tutorialsXFollow us on XSlackJoin our Slack