Databricks CV Anomaly Detection

👁️ Databricks + Computer Vision Anomaly Detection & Model Deployment

A complete guide to anomaly detection with Databricks and Apache Spark

“From data ingestion to real-time serving — build and deploy scalable computer vision anomaly detection models.”

📎 Full Project:
👉 View Jupyter Notebooks on GitHub

📌 One-Line Summary

This project provides a full pipeline for computer vision–based anomaly detection, covering data ingestion, preprocessing, model training, deployment, and REST API serving — all within Databricks and powered by Apache Spark.

1️⃣ How It Was Built

1. Utilities (00_utils.ipynb)

Common helper functions for preprocessing and visualization
Reusable utilities to streamline workflows

2. Data Ingestion & ETL (01_Ingestion_ETL.ipynb)

Ingested large-scale image datasets into Databricks
Implemented Spark-based ETL for scalability
Optimized storage and partitioning for performance and cost efficiency
Image Processing Visualization

3. Deep Learning Training (02_HF_Deep_Learning.ipynb)

Applied image preprocessing and augmentation
Trained models using PyTorch + Hugging Face
Evaluated performance with metrics like Accuracy, Loss, and PR-AUC

4. Model Deployment (03_Model_Deployment.ipynb)

Registered trained models in MLflow
Managed versions for reproducibility
Optimized inference pipelines for deployment

5. Model Serving (04_Model_Serving.ipynb)

Deployed models with Databricks Model Serving
Exposed REST API endpoints for real-time predictions
Integrated anomaly detection into external systems

2️⃣ Optimization & Best Practices

Spark optimizations for large-scale image data
Databricks cluster configuration for cost efficiency
Strategies for balancing performance and resource usage

🛠 Technologies Used

Step	Technology
Data Processing	Apache Spark, Databricks
Deep Learning	PyTorch, Hugging Face
Experiment Mgmt	MLflow
Deployment	Databricks Model Registry
Serving	REST API, Databricks Serving

💡 Key Learnings

Full lifecycle ML on Databricks: ingestion → training → deployment → serving
How to optimize Databricks for low-cost, high-performance workflows
Practical experience with model versioning, reproducibility, and API integration

🔗 GitHub Repository

📂 View Project on GitHub