Databricks Ml Training

ML Training on Databricks

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

Train with MLflow → register to Unity Catalog → consume the same artifact as either a batch Spark UDF over Delta or (when low-latency is required) a real-time serving endpoint.

Always train on Databricks (serverless job or notebook), never in the local Python process the agent is running in. Local training has no access to the silver tables, no MLflow tracking server, no UC registry path, and dies if the chat session drops — submit databricks jobs submit --no-wait (see "Train + deploy as a serverless job" below). Only fall back to local execution if the user explicitly asks for it.

If you need to deploy a real time model serving endpoint after the model is registered (creating endpoints, traffic config, version-swapping, querying, Foundation Model API endpoints), see databricks-model-serving.

Default Canonical flow

Copy code
silver_<features>  +  silver_<labels>
        ▼
   notebook (as a serverless job):
   ├── train with mlflow.autolog (XGBoost / sklearn / etc.)
   ├── mlflow.register_model → UC: {catalog}.{schema}.{model}
   ├── set_registered_model_alias(name, "prod", version)
   └── spark_udf(@prod) over latest features → MERGE into gold_predictions
        ▼
gold_<entity>_predictions   ◄── dashboards, apps, Genie read this

Feature-store-backed models diverge here. If training used fe.log_model(training_set=...), replace spark_udf(@prod) with fe.score_batch(model_uri, df=<keys_only>) — it auto-joins features via the model's registered feature lineage. See the Feature Engineering section below.

One notebook, one artifact. Re-running = retraining. Gold is where truth lives — read paths never call the model directly. Keep label-window logic (failure occurred within 7 days) in the notebook during dev; once stable, promote to a silver materialized view in SDP.

Train and register (the 90% case)

mlflow.autolog() captures params, metrics, code, and the model artifact for every run; registered_model_name=... auto-registers the best run to UC (auto-incremented version). Wrap training with Optuna so each trial is a child run and the best one is what gets registered.

Always mlflow.set_registry_uri("databricks-uc") — without it, models land in the deprecated workspace registry. The experiment's parent folder must exist — set_experiment does NOT auto-create it (fails with NOT_FOUND: Parent directory does not exist). Pre-create it once with databricks workspace mkdirs before the job runs.

bashCopy code
# Once per project — create the parent folder for the MLflow experiment.
databricks workspace mkdirs /Users/me@example.com/turbine_project

Use the Databricks notebook source format (# Databricks notebook source header, # COMMAND ---------- separators, # MAGIC %md/%sql magics for markdown/SQL cells):

pythonCopy code
# Databricks notebook source
# MAGIC %md
# MAGIC # Turbine failure prediction
# MAGIC
# MAGIC Train an XGBoost classifier on engineered turbine telemetry features.
# MAGIC ## Data exploration

# COMMAND ----------

# (basic data exploration — class balance, schema sanity, etc.)

# COMMAND ----------
# MAGIC %md
# MAGIC ## Training the model

# COMMAND ----------

import mlflow, mlflow.xgboost, optuna
from mlflow.tracking import MlflowClient
from xgboost import XGBClassifier
from sklearn.metrics import roc_auc_score

mlflow.set_registry_uri("databricks-uc")
mlflow.set_experiment("/Users/me@example.com/turbine_project/mlflow_experiment")

CATALOG, SCHEMA, NAME = "ai_demo_gen", "wind_farm", "turbine_failure"
FULL_NAME = f"{CATALOG}.{SCHEMA}.{NAME}"

# Autolog WITHOUT registered_model_name — otherwise every Optuna trial registers a new UC
# version, and a max-by-version pick lands on the last trial to finish, not the best one.
mlflow.xgboost.autolog(log_input_examples=True)

# For imbalanced labels: stratify the split, set scale_pos_weight = neg/pos.
def objective(trial):
    params = {
        "n_estimators":  trial.suggest_int("n_estimators", 100, 400),
        "max_depth":     trial.suggest_int("max_depth", 3, 10),
        "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.3, log=True),
    }
    with mlflow.start_run(nested=True):
        m = XGBClassifier(**params).fit(X_train, y_train)
        return roc_auc_score(y_test, m.predict_proba(X_test)[:, 1])

with mlflow.start_run(run_name="hpo") as parent:
    study = optuna.create_study(direction="maximize")
    study.optimize(objective, n_trials=20)

# COMMAND ----------
# MAGIC %md
# MAGIC ## Retrain best params and register

# COMMAND ----------
# Retrain on the winning trial's params explicitly, then register that single model.
with mlflow.start_run(run_name="best"):
    best = XGBClassifier(**study.best_params).fit(X_train, y_train)
    mlflow.log_metric("val_auc", study.best_value)
    info = mlflow.xgboost.log_model(best, name="model", registered_model_name=FULL_NAME)

# Stages are deprecated — UC uses movable aliases. Repoint @prod at the version we just registered.
client = MlflowClient(registry_uri="databricks-uc")
client.set_registered_model_alias(FULL_NAME, "prod", info.registered_model_version)

Framework autolog: mlflow.{sklearn,xgboost,lightgbm,pytorch,tensorflow,spark}.autolog().

Aliases, not stages: UC dropped Staging/Production. Use movable @prod/@challenger; load with models:/{full_name}@prod. Promoting a new version is one set_registered_model_alias call.

Consume: batch scoring over Delta

The cheap, default path for models NOT backed by feature tables. Load the registered model as a Spark UDF and score a Delta table; write predictions to a gold table that downstream consumers read. For feature-store-backed models (logged with fe.log_model(training_set=...)), skip spark_udf entirely and use fe.score_batch() — see the Feature Engineering section.

pythonCopy code
# COMMAND ----------
# MAGIC %md
# MAGIC ## Score and save to a gold predictions table

# COMMAND ----------
import mlflow
from pyspark.sql import functions as F

# env_manager rules:
#   "local"     → same runtime as training (same notebook/job). Fastest, default in dev/demo.
#   "virtualenv"→ different runtime than training; rebuilds the model's env.
#   "uv"        → same as virtualenv but faster (MLflow ≥ 2.22).
predict = mlflow.pyfunc.spark_udf(
    spark,
    model_uri=f"models:/{FULL_NAME}@prod",
    env_manager="local",
)

features = spark.table(f"{CATALOG}.{SCHEMA}.silver_turbine_features_latest")
feature_cols = [c for c in features.columns if c != "turbine_id"]   # exclude the join key
scored = features.withColumn("risk_score", predict(*[features[c] for c in feature_cols]))

# Overwrite-per-run pattern for "latest score per entity":
scored.select("turbine_id", "risk_score", F.current_timestamp().alias("scored_at")) \
    .write.mode("overwrite").saveAsTable(f"{CATALOG}.{SCHEMA}.gold_turbine_predictions")

For incremental scoring with history, MERGE into the predictions table instead of overwrite.

Real-time serving (when required)

After registering a model to UC, deploy it behind a Model Serving endpoint. The dev-side call is mlflow.deployments.get_deploy_client("databricks").create_endpoint(...) for classical ML or agents.deploy(...) for ResponsesAgents. First deploy is ~5 min for classical ML.

For endpoint create / update / version-swap, traffic config, AI Gateway, querying, the state.ready + state.config_update two-field readiness check, and Foundation Model API endpoints, see databricks-model-serving.

Train + deploy as a serverless job

Training notebooks run a few minutes (Optuna + UC register; endpoint warmup adds 5–15 min if you also deploy). Submit as a serverless one-time run so the CLI doesn't block. The notebook ends with dbutils.notebook.exit(json.dumps({...})) so the structured result (model_version, val_auc, endpoint_name) reaches .notebook_output.result.

bashCopy code
# 1. Upload the training notebook
databricks workspace import /Workspace/Users/me@example.com/turbine_project/train \
  --file ./train_notebook.py --format SOURCE --language PYTHON --overwrite

# 2. Submit as serverless one-time run (returns {"run_id": N} immediately with --no-wait)
RUN_ID=$(databricks jobs submit --no-wait --json '{
  "run_name": "turbine-train-and-deploy",
  "tasks": [{
    "task_key": "train",
    "notebook_task": {"notebook_path": "/Workspace/Users/me@example.com/turbine_project/train"},
    "environment_key": "ml_env"
  }],
  "environments": [{
    "environment_key": "ml_env",
    "spec": {
      "client": "4",
      "dependencies": ["mlflow==3.1.0", "xgboost==2.1.3", "optuna==4.1.0", "scikit-learn==1.5.2"]
    }
  }]
}' | jq -r .run_id)

# 3. Poll until a terminal life_cycle_state.
for _ in $(seq 60); do
  STATE=$(databricks jobs get-run "$RUN_ID" | jq -r '.state.life_cycle_state // "UNKNOWN"')
  echo "$(date +%H:%M:%S) $STATE"
  [[ "$STATE" =~ ^(TERMINATED|SKIPPED|INTERNAL_ERROR)$ ]] && break
  sleep 30
done
[[ "$STATE" =~ ^(TERMINATED|SKIPPED|INTERNAL_ERROR)$ ]] || { databricks jobs cancel-run "$RUN_ID"; exit 1; }

# life_cycle_state TERMINATED only means "the run ended" — check result_state.
RESULT=$(databricks jobs get-run "$RUN_ID" | jq -r '.state.result_state // "UNKNOWN"')
echo "result_state=$RESULT"
[[ "$RESULT" == "SUCCESS" ]] || { echo "Run did not succeed"; exit 1; }

# 4. Pull structured output via the TASK run_id (NOT the submit run_id).
TASK_RUN_ID=$(databricks jobs get-run "$RUN_ID" | jq -r '.tasks[0].run_id')
databricks jobs get-run-output "$TASK_RUN_ID" | jq '.notebook_output.result'
# → '{"model_version":"3","val_auc":0.91,"rows_scored":124,"endpoint":"turbine-risk-endpoint"}'

Common jobs submit traps to be aware of: environments[].spec.client: "4" is required on serverless notebook tasks; use the TASK run_id (tasks[0].run_id) — NOT the submit run_id — for get-run-output; print() is unreliable on serverless one-time runs (use dbutils.notebook.exit(json.dumps(...))); jobs submit rejects tags. For the broader databricks-jobs skill, see databricks-jobs.

Custom pyfunc

When sklearn/XGBoost autolog isn't enough — custom preprocessing, multiple sub-models, external API calls, ensemble logic. See references/custom-pyfunc.md for a full worked example. Two non-obvious things:

• python_model="path/to/file.py" (file path, not class instance) + mlflow.models.set_model(MyModel()) at the end of that file. This is the "Models from Code" pattern — the file is logged verbatim, no pickling of the class.
• mlflow.models.predict(model_uri=..., input_data=..., env_manager="uv") before deploying. Catches missing deps before the endpoint does.

Custom GenAI agents

Hand-rolled ResponsesAgent (LangGraph + UC Function tools + Vector Search retrieval) — see references/genai-agents.md.

Prefer no-code authoring via databricks-agent-bricks (Knowledge Assistants, Supervisor Agents) unless the user explicitly needs a custom LangGraph agent.

Feature Engineering: Feature Store & Feature Views

When to reach for Feature Engineering (either flavor) instead of a plain Delta table: when the same feature must be computed identically at training and serving time (no training/serving skew), the feature is time-dependent and needs point-in-time joins against labels (no future leakage), the feature needs to be served with <10ms latency via an online store, or the feature is shared across models with lineage tracked in UC. If none apply, a plain UC table is enough — the sections below can be skipped.

Default: don't use Feature Engineering unless one of the reasons above clearly applies or the user explicitly asked for it. It adds build time and complexity (an extra Delta table layer for FeatureLookup; a materialization pipeline for Feature Views). If you're unsure, use plain UC tables and add Feature Engineering later when a concrete need surfaces.

Two flavors, one train/score path (create_training_set → fe.log_model → fe.score_batch):

• FeatureLookup API — you own the feature tables (compute, write, refresh); bind them to a training set via FeatureLookup + FeatureEngineeringClient. Reach for it when the features already exist as Delta tables or you want direct control over how they are computed. See references/feature-store.md.
• Feature Views (declarative, Public Preview, databricks-feature-engineering>=0.16.0) — Databricks owns compute, materialization (Delta offline + Lakebase online), and refresh from a spec you declare (create_feature over DeltaTableSource; RollingWindow/SlidingWindow/TumblingWindow; create_stream for Kafka features). Reach for it when the feature is a formula you want Databricks to keep fresh. See references/feature-views.md.

Gotchas (the ones that cost time)

Endpoint-lifecycle gotchas (readiness two-state, version-swap, Serving-UI SP filter) live in databricks-model-serving.

Reference files

Related skills

• databricks-model-serving — serving-endpoint lifecycle (create, query, update-config, version-swap, AI Gateway, Foundation Model API endpoints).
• databricks-agent-bricks — no-code Knowledge Assistants and Supervisor Agents. Prefer this over hand-rolling agents.
• databricks-mlflow-evaluation — evaluate model/agent quality before promoting @prod.
• databricks-vector-search — vector indexes used as retrieval tools in agents.
• databricks-jobs — async deploy pattern (--no-wait, TASK run_id trap).
• databricks-unity-catalog — UC governs the registered model: permissions, lineage, audit.