ML & AI in Databricks Dashboards
🧩 1. Introduction to ML & AI in Dashboards
🌍 Modern Story Way
Imagine your dashboard as a mirror.
A normal mirror shows you what you look like right now. But what if your mirror also told you:
✨ “You might smile tomorrow.” ✨ “Your mood will dip next week; take a break.” ✨ “Your energy level is trending up — keep going!”
This is exactly what Databricks dashboards do when you add Machine Learning (ML) and Artificial Intelligence (AI).
Instead of just showing what happened, your dashboards begin to show:
- 📈 What will happen next (sales forecasts)
- 🕵️ What’s unusual (customer anomalies)
- 🛒 What you should do next (product recommendations)
Your dashboard becomes a living assistant, not a static report.
💼 Professional Explanation
Integrating ML and AI into Databricks dashboards adds layers of intelligence such as:
- Predictive Analytics – forecasting sales, churn, demand, risk
- Anomaly Detection – spotting outliers or unusual activity
- Decision Automation – recommending actions based on data
- Natural Language Insights – using LLMs to generate summaries
Databricks supports an end-to-end ML workflow using:
- MLflow – model tracking, metrics, artifacts
- Databricks Feature Store – centralized feature management
- AutoML – automated model training
- Databricks Model Serving – deploy models as APIs
- Dashboards – visualize insights in realtime
📸 Example Visual - Databricks ML Pipeline Flow
🚀 MLflow Overview
🌍 Modern Story Way
Think of MLflow as your machine-learning diary — a notebook that never forgets.
A data scientist may test 10… 20… or 200 models:
- “This model got 92% accuracy.”
- “This one used XGBoost with learning_rate=0.1.”
- “Run #74 is the best — lowest error.”
MLflow writes everything down automatically.
It’s like a chef keeping a recipe book:
🍲 “This was the best dish — here’s exactly how I made it.”
Whenever you need to recreate or deploy the model — MLflow already saved the recipe.
💼 Professional Explanation
MLflow is an open-source platform integrated into Databricks for managing the ML lifecycle.
It includes 4 major components:
1️⃣ MLflow Tracking
Simple Explanation: MLflow Tracking is like a notebook that automatically records everything about your machine-learning experiments — what parameters you used, how well the model performed, and what files it created.
Why it matters: It helps you compare models, remember what you did, and pick the best version without confusion.
Example Code:
import mlflow
with mlflow.start_run():
mlflow.log_param("model_type", "xgboost")
mlflow.log_metric("rmse", 3.21)
2️⃣ MLflow Projects
Simple Explanation: MLflow Projects is a way to package your machine-learning code, environment, and dependencies so you or your team can run the same experiment anywhere — with one command.
Why it matters: It eliminates “it works on my machine” problems by ensuring the code behaves the same everywhere (laptop, Databricks, cluster, or cloud).
3️⃣ MLflow Models
Simple Explanation: MLflow Models stores your trained models in a standard, portable format so they can be deployed easily to different places (APIs, batch jobs, cloud services).
Why it matters: No matter how a model was trained — in Python, R, Scikit-learn, PyTorch, etc. — MLflow wraps it so other tools can use it without compatibility issues.
4️⃣ MLflow Model Registry
Simple Explanation: The Model Registry is a central “library shelf” where all your models are stored, versioned, reviewed, and approved for production.
Why it matters: It provides governance:
- You can track versions (v1, v2, v3…)
- Add comments/review notes
- Approve/reject models
- Deploy to production with a click
It keeps your ML workflow organized and safe.
📊 How MLflow Helps Dashboards
You can use MLflow to:
- Fetch the latest best model run
- Display model accuracy trends in the dashboard
- Show how performance improves over time
Example snippet to load the best model:
from mlflow import MlflowClient
client = MlflowClient()
best_run = client.search_runs(
experiment_ids=["12345"],
order_by=["metrics.rmse ASC"],
max_results=1
)[0]
best_run.info.run_id
📸 MLflow UI Example Placeholder - MLflow Tracking System Concept
📘 Why MLflow Matters for Dashboards
🎯 Simple Example
Use Case: Predict next week’s product demand.
Without MLflow:
- You forget which model was best
- Hard to reproduce results
- No version control
With MLflow:
- Best model is automatically tracked
- Code + parameters are saved
- Easy to deploy that model into the dashboard
🛠 Hands-On Example (Simple)
Below is a simple, beginner-friendly Databricks notebook-style demo.
Step 1: Load Data
df = spark.read.format("delta").load("/mnt/data/sales")
display(df)
Step 2: AutoML Training (No ML Expertise Required)
from databricks.automl import regression
summary = regression.train(
df,
target_col="weekly_sales",
timeout_minutes=10
)
summary.best_trial
Step 3: Register the Best Model
summary.register_model(model_name="sales_forecast_model")
Step 4: Create a Dashboard Cell
from pyspark.sql.functions import current_date
model = mlflow.pyfunc.spark_udf(spark, "models:/sales_forecast_model/Production")
predictions = df.withColumn("predicted_sales", model(*df.columns))
display(predictions)
Step 5: Add to Databricks Dashboard
- Run the cell
- Click “Add to Dashboard”
- Choose your dashboard
- Set refresh schedule (e.g., every hour)
📸 Dashboard Visualization Placeholder - ML Dashboard Concept
1-Minute Summary
| Topic | Modern Story | Professional |
|---|---|---|
| ML & AI in Dashboards | “Mirror predicting tomorrow” | Predictive / anomaly / automated insights |
| MLflow | “Machine-learning diary” | Tracking, projects, models, registry |
| Why MLflow | Avoid forgetting experiments | Model governance + reproducibility |
| Hands-on | Simple AutoML example | Dashboard integration |