Workflow Script Automation for AI Computer Vision Projects
Automate your entire Computer Vision pipeline — from raw data to deployment-ready models — using intelligent scripting for 2D & 3D vision tasks.
Course Description
This course is designed for computer vision and AI practitioners looking to build scalable, automated pipelines for real-world vision tasks. Covering both 2D and 3D domains, the course empowers learners with skills to automate data preprocessing, model implementation, evaluation, optimization, and reporting using powerful scripting techniques. You’ll build reusable workflows that streamline the entire AI lifecycle — from raw image ingestion to intelligent decision-making systems.
Whether you’re handling RGB, depth, or multi-modal data, this course equips you with the tools to build intelligent automation for classical and deep learning-based computer vision pipelines.
🚀 Course Highlights
Hands-On Automation Projects
Real-world projects with Python code for 2D & 3D CV pipeline automation.2D & 3D Data Workflow Coverage
Comprehensive automation for images, RGB-D data, point clouds, and 3D models.End-to-End Pipelines
Automate complete CV pipelines from preprocessing to visualization and reporting.Tools & Libraries Mastery
Work with OpenCV, Open3D, Matplotlib, Scikit-learn, PyTorch, and more.Career-Oriented Design
Focused on job-ready skills, automation thinking, and project portfolio creation.Support & Community Access
Access code, labs, private community, Q&A, and expert mentorship.📚 Course Modules Overview
1. Introduction to Computer Vision Automation
Learn foundational concepts and the growing role of automation in the field of computer vision.2. Programming Fundamentals for Automation
Understand core programming tools, libraries, and integration techniques for building vision automation systems.3. Automation for 2D Computer Vision Pipeline
Explore how automation is applied across 2D data workflows including preprocessing, feature extraction, and evaluation.4. Automation for 3D Computer Vision Pipeline
Build end-to-end automation for RGBD data, 3D preprocessing, conversion, modeling, and analysis tasks.5. Automation for 2D Machine/Deep Learning Pipelines
Automate training workflows for 2D deep learning tasks including classification, detection, and segmentation.6. Automation for 3D Machine/Deep Learning Pipelines
Create automated deep learning pipelines for 3D data including model implementation, optimization, and reporting.🧰 Tools & Libraries You’ll Master in This Course
Python
Shell Scripting
YAML
JSON
argparse
Hydra
🎯 Learning Outcomes
Master Automation Concepts
Grasp the core principles and benefits of automating computer vision pipelines to enhance workflow efficiency.
Develop Programming Proficiency
Apply Python and relevant libraries like OpenCV, TensorFlow, and PyTorch to build scalable automated CV solutions.
Implement Automated 2D & 3D Pipelines
Build end-to-end automated workflows for 2D and 3D vision tasks including data preprocessing, feature extraction, and model evaluation.
Optimize and Tune Models Automatically
Employ automated hyperparameter tuning and evaluation techniques to maximize the performance of computer vision models.
Generate Automated Reports & Insights
Create detailed automated reports summarizing pipeline execution, results, and actionable insights for decision making.
Build Adaptable AI Vision Workflows
Design customizable automation pipelines suited to a variety of 2D/3D CV applications including classification, detection, and segmentation.
🧠Assignments, Quizzes & Interview Overview
Hands-On Assignments
Apply your knowledge through practical assignments designed to simulate real-world computer vision automation tasks. These exercises will deepen your understanding of pipeline automation, data preprocessing, and model optimization.
Regular Quizzes
Reinforce your learning with quizzes after each major module to assess your grasp of key concepts and automation techniques. Quizzes will help identify areas for improvement and ensure readiness for advanced topics.
Interview Preparation
Prepare confidently for technical interviews with curated questions and mock interviews focused on computer vision automation, programming skills, and pipeline design. Gain insights into industry expectations and best practices.
🚀 Sample Project List
Project 1: Automated Image Classifier
Develop an automated image classifier utilizing a 2D computer vision pipeline. Automate data ingestion, preprocessing, feature extraction, and model training to classify images into predefined categories.
Project 2: 3D Object Detection and Segmentation
Build a 3D object detection and segmentation system with automated data processing, 2D-to-3D conversion, feature extraction, and deployment of detection and segmentation models for complex 3D vision tasks.
Project 3: Automated Face Recognition System
Construct an automated face recognition system combining 2D and 3D vision techniques. Automate data preprocessing, feature extraction, and classification to identify faces from images or video streams.
Project 4: Autonomous Navigation System
Design an autonomous navigation system using computer vision with automated pipelines for environment perception, object detection, and decision-making for navigation in real-world scenarios.
Project 5: Anomaly Detection in Surveillance Videos
Develop an automated system for real-time anomaly detection in surveillance videos. Use 2D/3D pipelines to preprocess, extract features, and identify unusual activities efficiently.
Project 6: Automated Medical Image Analysis
Create an automated medical image analysis system to preprocess medical images, segment anatomical structures, and detect abnormalities using 2D/3D vision pipelines.
Project 7: Automated Quality Control in Manufacturing
Develop an automated visual quality control system to inspect products, detect defects, and ensure compliance using computer vision pipelines for manufacturing processes.
Project 8: Automated Visual Search Engine
Construct an automated visual search engine that performs content-based image retrieval (CBIR) by identifying similar images or objects within large databases using computer vision automation.