All-in-one masterclass from development to deployment for computer vision anomaly detection

Databricks + Computer Vision Anomaly Detection & Model Deployment Complete Guide

📌 Course Overview

This course is a hands-on course that teaches you how to effectively perform computer vision-based anomaly detection, and covers everything from data processing for deep learning to model optimization and deployment .

In particular, it provides practical know-how on optimizing large-scale data and building and deploying anomaly detection models using Apache Spark & Databricks .

Beyond simple code writing techniques, you'll learn advanced optimization strategies and cost-saving techniques that you can leverage in real-world projects .

In this course, you will learn step-by-step the essential contents for real-world projects , including data collection, preprocessing, augmentation, model training and evaluation, model serving via REST API, and model version management .
In particular, you can acquire powerful skills that can be immediately applied in practice through optimization strategies that implement high performance at low cost in Databricks .

#Python, #Artificial Intelligence (AI), #Machine Learning, Deep Learning, #azure-databricks

🎯 Recommended for these people!

✔ AI/ML engineers who want to learn computer vision-based anomaly detection
✔ Developers who want to build anomaly detection systems in manufacturing, healthcare, security, etc.
✔ Those who want to learn optimized data processing techniques using Apache Spark & Databricks
✔ Anyone who wants to learn how to build anomaly detection models and deploy them as real-time APIs

After class

🔹 Data Engineer & AI Engineer
→ Learn how to optimize every process from data collection, processing, learning, and distribution to run an efficient AI project.

🔹 Machine Learning & Deep Learning Developer
→ By learning MLflow-based experiment management, transfer learning, and model optimization techniques, you can develop more powerful and practical models.

🔹 AI/Data Startup Founder & Project Leader
→ Learn cost-saving and performance optimization know-how to efficiently utilize resources and maximize project ROI (return on investment).

🔹 Corporate Data & AI Manager
→ Learn Databricks & Spark optimization technologies to effectively process large-scale data and efficiently operate AI projects within your company.

#Python, #Artificial Intelligence (AI), #Machine Learning, Deep Learning, #azure-databricks

Why this course is special

✅ Covers practical optimization techniques . Rather than simple theoretical lectures, it provides practical know-how to solve performance issues and cost problems that frequently occur in actual projects.

✅ You can learn cost-saving strategies using Spark & Databricks . You can learn how to implement high performance with low-cost resources and achieve great cost-saving effects in practice.

✅ From deep learning data preprocessing to model training, deployment, and REST API serving, all in one place ! Covers the entire process of data collection → storage → preprocessing → augmentation → training → deployment .

✅ Revealing optimization secrets that you can only learn in this lecture !
We will exclusively reveal optimization strategies and cost-saving know-how in Databricks and Spark environments that are not covered in other lectures.

🎯 Key things you learn in class

🔹 1. Data Optimization for Anomaly Detection Systems

✅ Optimizing the collection, storage, and preprocessing of large-scale image data
✅ Large-scale image processing using Apache Spark & Databricks
✅ Data loading & streaming processing techniques considering memory efficiency

🔹 2. Computer Vision-Based Anomaly Detection Concepts and Techniques

✅ Main principles of Anomaly Detection
✅ Comparison of supervised learning vs unsupervised learning based anomaly detection models
✅ Salt and Pepper Patches, an abnormal pattern learning technique using Noise Injection

🔹 3. Optimization of large-scale image data analysis and preprocessing

✅ Image processing using OpenCV and PIL
✅ Data preprocessing techniques such as image resizing, normalization, and channel conversion
✅ Automating large-scale image conversion using Spark UDF

🔹 4. Build anomaly detection model and evaluate its performance

✅ Transfer Learning Using the Hugging Face Pre-trained Model
✅ Comparison and application of anomaly detection models based on autoencoder, GAN, and CNN
✅ Model performance evaluation and experiment management using MLflow
✅ Performance measurement technique using F1-score and Precision-Recall Curve

🔹 5. Deploy real-time anomaly detection system and serve API

✅ Building a real-time anomaly detection API using FastAPI
✅ Automated deployment with Databricks Model Serving
✅ REST API-based anomaly detection request and response processing

🔹 6. Cost optimization and operational strategy of anomaly detection model

✅ Reduce costs and improve performance through Apache Spark optimization
✅ Parallel processing techniques in large-scale anomaly detection systems
✅ Model deployment and version management using MLflow & Databricks

#Python, #Artificial Intelligence (AI), #Machine Learning, Deep Learning, #azure-databricks

Who created this course

hello.
I have been working on various projects in the fields of AI, machine learning, and data engineering for over 10 years, and have gained in-depth experience in deep learning model optimization and large-scale data processing .

He is currently an adjunct professor at Korea University and the CEO of DC Solutions, where he carries out AI and data engineering projects for major domestic corporations and research institutes .
In addition, we have been conducting practical projects in various industrial fields such as Apache Spark, ML model development, MLOps construction, and medical AI research , and have been researching how to design and operate optimized AI and data processing systems .

🎯 Why I created this course

Through working on numerous projects, I have realized that ‘data optimization’ is just as important as developing AI models .
In particular , in the process of training and deploying deep learning models, we often encounter problems of wasted computing resources due to inefficient data processing .

Most AI projects end up costing several times, or even dozens of times, more than initially planned .
If this is not addressed, projects often fail at enormous cost before they are even successful .

So, I actually planned this lecture by gathering together proven data optimization & deep learning model deployment strategies from companies and research institutes .
Beyond developing deep learning models, we want to share practical know-how on building faster and more powerful AI systems at a lower cost .

📌 Questions & Answers for prospective students

Things to note before taking the class