Introducing the ML Agent: From Data to Deployed Model in Minutes

Building ML models should not require a separate universe of tools. Yet that is exactly what most data teams experience: Jupyter notebooks that are disconnected from production data pipelines, experiment tracking that lives in a different system than your data catalog, and model deployment that requires a completely separate workflow from everything else your team does.

Today we are releasing the ML and AutoML Agent — the 15th agent in the Data Workers swarm. It brings experiment tracking, model registry, feature engineering, and model deployment into the same MCP workflow your team already uses for data discovery, quality monitoring, and pipeline management.

Why We Built This

Every ML engineer we talked to described the same friction: they spend more time on infrastructure than on modeling. Setting up experiment tracking. Configuring model registries. Building feature pipelines. Managing deployments. The actual machine learning — choosing algorithms, tuning hyperparameters, evaluating results — is maybe 20% of the job.

The tools exist. MLflow, Weights and Biases, SageMaker, Vertex AI. But they are all separate systems. None of them know about your data catalog. None of them check data quality before training. None of them know which features are already computed in your warehouse. The ML engineer becomes the human integration layer between all these tools.

What the ML Agent Does

The agent provides 16 MCP tools that cover the full ML lifecycle — from feature engineering to production deployment:

• •Feature engineering. suggest_features analyzes your dataset columns and recommends transformations — one-hot encoding for categoricals, normalization for numerics, date decomposition for timestamps. It knows about your data because it queries the catalog agent.
• •Model selection. select_model recommends algorithms based on your dataset characteristics. Classification or regression, dataset size, feature types — it considers all of these and suggests XGBoost, LightGBM, RandomForest, or linear models with pros and cons for each.
• •Experiment tracking. create_experiment and log_metrics give you MLflow-compatible experiment tracking. Every training run is logged with hyperparameters, metrics, and timestamps. compare_experiments shows side-by-side results across runs.
• •Model registry. register_model stores trained models with semantic versioning. get_model_versions shows the full version history. Stage management (staging, production, archived) is built in.
• •Feature pipelines. create_feature_pipeline defines reusable feature transformations. get_feature_stats shows distribution statistics so you can catch drift before it affects model performance.
• •Explainability. explain_model provides SHAP-style feature importance scores. Know which features drive your model's decisions — critical for regulated industries.
• •Drift detection. detect_model_drift uses PSI and KS tests to detect when production data diverges from training data. Catch model degradation before it affects business outcomes.
• •A/B testing. ab_test_models configures traffic splitting between model versions with statistical significance tracking. Ship model improvements with confidence.

The Swarm Advantage

The real power is not any individual tool — it is the integration with the rest of the swarm. When you run train_model, the agent automatically checks data quality via the quality agent. When you deploy a model, the governance agent verifies compliance. When you create a feature pipeline, it registers with the catalog so other teams can discover and reuse your features.

This is what you cannot get from standalone ML tools. MLflow does not know about your data quality scores. SageMaker does not check governance policies before deployment. The ML agent does both because it is part of a swarm that shares context.

Tier Model

• •Community (free). Feature suggestions, model selection, evaluation, status tracking, explainability, feature stats, model version listing. Everything you need to explore and understand.
• •Pro ($500/month). Training orchestration, experiment tracking, model registry, feature pipeline creation. The tools that create and persist artifacts.
• •Enterprise. Drift detection, A/B testing, full experiment comparison. Production-grade ML operations.

Getting Started

The ML agent is available now in the Data Workers swarm. If you are already running npx dw-claw, the agent is included. Start with suggest_features to see what your data looks like through the lens of feature engineering, then use select_model to get algorithm recommendations.

For teams already using MLflow or Weights and Biases, the experiment tracking tools use compatible concepts — experiments, runs, metrics, parameters. Migration is not required. The ML agent complements your existing tools by connecting them to your data stack.

We built this because we believe ML should not be a separate workflow. It should be part of the same data operations your team runs every day. The ML agent is our first step toward that vision.