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Introduction to intelligent automation and computer vision
Images can become a usable information source for your applications. Connect computer vision, models and automation needs to structure your first technical choices. Develop a framework for examining results and identifying appropriate conditions of use.
- Duration
- 1 day 7 hours
- Code
- IA037FR Code
Presentation
Intelligent automation is no longer limited to processing text or numerical data. With computer vision, machines can now see, analyse and interpret visual streams to trigger automated actions. This one-day course introduces a rapidly expanding technology capable of transforming sectors as varied as industry, retail and security.
The programme offers a pragmatic exploration of visual recognition technologies. You will learn to distinguish between detection, classification and segmentation and discover how they fit into broader automation chains, including RPA and workflows. The aim is to help you identify manual visual tasks that would benefit from automation.
Far from being purely theoretical, the day is built around practice. You will use established tools such as OpenCV and Python to create your first image analysis scripts, including optical character recognition (OCR) and shape detection. You will leave with a clear understanding of these solutions' potential and their ethical and technical limitations.
Objectives
By the end of this course, you will be able to:
- define the key concepts of intelligent automation and computer vision;
- identify relevant visual automation opportunities in your business context;
- use industry-standard libraries and tools, including OpenCV, Tesseract and YOLO;
- design a simple script to automate an image-analysis-based task;
- assess the technical, ethical and regulatory constraints of a computer vision project.
Program
Module 1: Understanding the challenges of visual automation
- Intelligent automation fundamentals and its different types, including RPA and AI.
- Exploring application areas: quality control, security, healthcare and retail.
Hands-on exercises
- Identify and assess tasks suitable for automation in your own business environment.
Module 2: Exploring computer vision
- Computer vision mechanisms: detection, classification and segmentation.
- Managing visual data: processing images, video and real-time streams.
Hands-on exercises
- Analyse an image with OpenCV to detect shapes or faces.
Module 3: Using tools and technologies
- The technical ecosystem: Python, OpenCV and Tesseract for OCR.
- Analysing advanced models such as YOLO for object detection.
- Integrating visual components into automated workflows.
Hands-on exercises
- Create a small Python script to automate a practical visual task, such as text extraction.
Module 4: Scoping and deploying a project
- Technical challenges: image variability, lighting and scalability.
- Managing ethical risks relating to surveillance and regulatory risks relating to GDPR.
- Best practices for documenting and maintaining solutions.
Hands-on exercises
- Develop a brief project scope for integrating computer vision into an existing business process.
Audience
This course is intended for technical and business professionals involved in innovation, including:
- product owners and AI project managers seeking to expand their use cases;
- developers, data analysts and engineers seeking an introduction to image processing;
- innovation and digital transformation managers seeking performance improvements;
- anyone involved in optimising business processes.
Prerequisites
The following prerequisites apply:
- general knowledge of computing or project management;
- basic Python knowledge is recommended to gain the full benefit of the workshops, but is not mandatory.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Practical approach: discover visual automation through hands-on use of real tools such as OpenCV from the first day.
- Combined expertise: connect process automation logic with the technical aspects of image analysis for a complete view of solutions.
- Business relevance: move beyond novelty to identify use cases that create real business value.
- Responsible perspective: incorporate crucial ethical and regulatory considerations relating to image analysis and surveillance from the design stage.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.
Any brand names and logos mentioned in this course description, such as Python, OpenCV, YOLO and Tesseract, belong to their respective owners. Their mention for educational purposes does not constitute an endorsement or partnership.