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

Name: All-in-one masterclass from development to deployment for computer vision anomaly detection
Price: 84700 KRW
Rating: 5 (10 reviews)

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

(5.0) 10 reviews

56 learners

nexthumans

이상탐지

실습 중심

Python

Machine Learning(ML)

Deep Learning(DL)

azure-databricks

Reviews from Early Learners

What you will gain after the course

MLflow
MLOps
Databricks
Deep Learning
Computer Vision
Anomaly detection
Computer vision
Deep learning
Databricks

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

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

✅ 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.

🔹 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 .

❓ Why should I learn Data Optimization & Deep Learning Model Deployment?

💡 As important as developing AI and machine learning models is optimizing and efficiently deploying data .
In many projects, the model works perfectly, but problems arise that make it difficult to actually service it due to inefficient data processing and high operational costs.

In this course, you will learn how to optimize large-scale data and deploy deep learning models faster and at lower cost using Apache Spark & Databricks .
✔ You will learn practical know-how to build high-performance AI systems while reducing costs .

❓ What can I do after taking this course?

✅ You can acquire practical capabilities to carry out AI/ML projects in companies and research institutes .
✅ Large-scale data optimization and processing is possible using Apache Spark & Databricks .
✅ You can learn the entire process of training, optimizing, and deploying deep learning models and apply it directly to your work.
✅ You can perform model training, experiment tracking, and version management using MLflow .
✅ You can learn the technology to deploy AI models through REST API and apply them to real-world services .

❓ What level does the course cover? (Beginner, Intermediate, Advanced?)

🔹 This is an intermediate to advanced level course .
🔹 Covers Apache Spark, data engineering, deep learning model training and deployment processes, focusing on practical application as well as theory .
🔹 Recommended for those who have basic knowledge of Python and machine learning concepts rather than complete beginners.
🔹 However! Since the lectures are structured so that you can learn step by step while practicing actual code , you can follow along even if you only know the basic concepts.

Recommended for
these people

Who is this course right for?

A developer interested in computer vision and AI
Data analysts and engineers in manufacturing, finance, and security
Developers interested in AI model serving and deployment
Someone who wants to apply AI projects in practice.
A data scientist looking to develop a deep learning-based anomaly detection model
Machine Learning Engineer
Experts interested in the potential real-world applications of the technology in manufacturing (defective product inspection), finance (fraud detection), and security (intrusion detection).
People looking to implement AI in fields that require anomaly detection, such as quality control, risk analysis, and security monitoring.
Anyone who wants to learn the entire process from data preparation to model training and API serving in a Databricks environment.
Developer with practical experience in model management and real-time deployment using MLflow
Learners who want to complete projects through actual coding practice, not just theory
Someone who wants to improve their problem-solving skills in real-world development scenarios.

Need to know before starting?

Python Basics
Spark Language Basics

Hello
This is

153

Learners

Reviews

Answers

4.9

Rating

Courses

현재 대기업 중심으로 아래와 같은 프로젝트의 개발책임 및 컨설팅을 맡고 있습니다. 현역^^입니다.

더불어, 고려대 대학원에서 인공지능 관련 겸임교수로도 활동하고 있습니다.

저의 목표는 실전에 바로 써먹을 수 있는 현장감 있는 프로그래밍 기술입니다. 앞으로 많은 여러분과 함께 재미난 수업 만들어 나가고 싶습니다.

엔터프라이즈 인공지능 구조 및 서비스 설계
머신러닝 서비스 구현
벡엔드 서비스 개발
클라우드(Azure) Databricks, ETL, Fabric 등 각종 클라우드 환경에서의 데이터베이스 구축 및 서비스 개발

Curriculum

All

31 lectures ∙ (11hr 10min)

Section 1. Anomaly detection is

2 lectures ∙ (11min)

Section 2. Data Preparation and Azure Storage Loading

7 lectures ∙ (2hr 34min)

3. Virtual Environment and Data Preparation
26:06
4. Azure OpenAI Data Labeling #1 - Resource Creation and Model Deployment
13:50
5. Azure OpenAI for Data Labeling #2 - Labeling Basics
29:36
6. Azure OpenAI Data Labeling #3 - Controlling LLM Return Value Schema through Prompt Engineering
15:21
7. Azure OpenAI Data Labeling Using Multithreading #4
25:48
8. Azure OpenAI Data Labeling Using DataFrames #5 - DataFrames
09:55
9. Azure OpenAI Data Labeling Using Caching #6 - Caching
34:11

Section 3. Loading Azure Storage Data in Databricks

4 lectures ∙ (1hr 8min)

10. Azure Storage Creation and Data Loading
15:15
11. Databricks #1 - Creating Databricks Resources in Azure Subscription
11:24
12. Databricks #2 - Azure Storage Mount
09:08
13. Databricks #3 - Loading ADLS Data and Labeling
32:21

Section 4. Databricks: Deep Learning Model Image Processing

7 lectures ∙ (2hr 32min)

Section 5. Databricks (Advanced): Deep Learning Model Image Processing

2 lectures ∙ (1hr 6min)

Section 6. Creating Deep Learning Models

5 lectures ∙ (1hr 52min)

Section 7. Deep Learning Model Deployment

1 lectures ∙ (27min)

Section 8. Deep Learning Model Serving (REST API)

2 lectures ∙ (1hr 8min)

Section 9. Patch Update Lecture

1 lectures ∙ (10min)

Published:

Last updated:

Reviews

All

10 reviews

5.0

10 reviews

devshin91
Reviews 5
∙
Average Rating 5.0
Edited
5
100% enrolled
I learned a lot. It was very helpful.
kish19190060
Reviews 4
∙
Average Rating 5.0
5
30% enrolled
main33730814
Reviews 3
∙
Average Rating 3.7
Edited
5
20% enrolled
I'm learning high-quality skills. This is a lecture that's completely worth the money. I only knew basic Python concepts like classes and inheritance superficially or could only apply them shallowly. (To the extent that I could interpret the code written by the instructor) Should I call it code refactoring...? It felt like watching a video visualization of the instructor's actual work process. I also realized that I need to learn how to utilize Python's basic built-in libraries more effectively. Especially these days, code interpretation and error handling have become relatively easier due to AI, so I'm studying with AI's help for difficult concepts or interpretations in between. Thank you for providing such a good lecture at this price.
- nexthumans
  Instructor
  Thanks for the uplifting review~ It feels like a wonderful gift. I will strive harder!
everythx
Reviews 10
∙
Average Rating 5.0
5
100% enrolled
The AI lecture utilizing Databricks seems unique, and I thank you for the excellent lecture. However, it would have been better if you had provided more overview explanations for each detailed unit. For example, it would have been even better if you had explained the functions of each menu in machine learning and supplemented how each code is utilized in which menu. Additionally, for those taking this lecture, Spark or Databricks is more of a focus, so there are likely many people who are interested in AI but lack knowledge. If you had supplemented this point as well, it would have been an even more excellent lecture.
- nexthumans
  Instructor
  Thank you for your sincere course feedback. I will strive to deliver even better lectures in the future!
ttm016ng4767
Reviews 6
∙
Average Rating 5.0
5
30% enrolled

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

Reviews from Early Learners

What you will gain after the course

Databricks + Computer Vision Anomaly Detection & Model Deployment Complete Guide

📌 Course Overview

🎯 Recommended for these people!

After class

Why this course is special

🎯 Key things you learn in class

🔹 1. Data Optimization for Anomaly Detection Systems

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

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

🔹 4. Build anomaly detection model and evaluate its performance

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

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

Who created this course

🎯 Why I created this course

📌 Questions & Answers for prospective students

❓ Why should I learn Data Optimization & Deep Learning Model Deployment?

❓ What can I do after taking this course?

❓ What level does the course cover? (Beginner, Intermediate, Advanced?)

Things to note before taking the class

Practice environment

Learning Materials

Player Knowledge and Notes

Recommended for
these people

Hello
This is

Curriculum

Reviews

nexthumans's other courses

Similar courses

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

Reviews from Early Learners

What you will gain after the course

Databricks + Computer Vision Anomaly Detection & Model Deployment Complete Guide

📌 Course Overview

🎯 Recommended for these people!

After class

Why this course is special

🎯 Key things you learn in class

🔹 1. Data Optimization for Anomaly Detection Systems

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

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

🔹 4. Build anomaly detection model and evaluate its performance

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

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

Who created this course

🎯 Why I created this course

📌 Questions & Answers for prospective students

❓ Why should I learn Data Optimization & Deep Learning Model Deployment?

❓ What can I do after taking this course?

❓ What level does the course cover? (Beginner, Intermediate, Advanced?)

Things to note before taking the class

Practice environment

Learning Materials

Player Knowledge and Notes

Recommended for these people

HelloThis is .css-1q3zd4q{text-decoration-line:underline;text-underline-position:under;text-underline-offset:1px;}

Curriculum

Reviews

nexthumans's other courses

Similar courses

Recommended for
these people

Hello
This is