Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training AWS Rekognition - Analyze Images and Videos with AI training are: AWS knowledge (S3, Lambda, EC2), Python/Node.js, ML/AI basics. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training AWS Rekognition - Analyze Images and Videos with AI training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training AWS Rekognition - Analyze Images and Vi... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training AWS Rekognition - Analyze Images and Videos with AI training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training AWS Rekognition - Analyze Images and Videos with AI training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training AWS Rekognition - Analyze Images and Videos with AI training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master AWS Rekognition APIs for professional image analysis

Develop scalable enterprise AI video pipelines

Implement face, object, and text detection with Rekognition

Optimize AWS Rekognition integrations for certified performance

Design GDPR-compliant solutions using AWS Rekognition

Deploy secure production-ready Rekognition AI applications

Training program

Module 1AWS Rekognition Fundamentals: API Configuration and Image Analysis (SDK, Console, S3)

Installation of AWS Rekognition SDK, secure IAM configuration for API access, first calls to detect objects and scenes in real images, exercises on enterprise datasets, content moderation analysis with custom labels, creation of a basic Python script for batch processing, validation via AWS console with immediate feedback and initial cost optimization.

Module 2AWS Rekognition Face Analysis: Emotion Detection and Comparison (Celebrity, Custom Labels)

Exploration of DetectFaces API for extracting demographic attributes, implementation of SearchFaces for matching databases, integration with S3 for automated flows, practical cases on surveillance videos, development of Custom Labels for specific industry recognition, tests on real enterprise scenarios, production of a real-time emotion detection prototype with accuracy metrics.

Module 3AWS Rekognition Video Analysis: Streaming and Real-Time Events (Kinesis, Lambda Triggers)

Configuration of StartMediaAnalysis for asynchronous video jobs, monitoring with GetMediaAnalysisJob, integration of Kinesis Video Streams for live processing, exercises on motion and activity detection, chaining with Lambda for automated alerts, concrete industrial security cases, creation of a CloudWatch dashboard visualizing AI insights, latency optimization for critical applications.

Module 4Advanced AWS Rekognition: OCR Text and Content Moderation (Textract Integration, Safeguards)

Mastery of DetectText for multilingual OCR extraction, combination of Rekognition-Textract for image documents, implementation of Content Moderation with nuisance filtering, exercises on large e-commerce datasets, development of custom guards via Custom Moderation, GDPR compliance tests on sensitive data, production of an end-to-end pipeline processing thousands of assets with automated reporting.

Module 5AWS Rekognition Deployment: Production Scalability and Monitoring (ECS, Fargate, Cost Optimization)

Serverless deployment via API Gateway and Lambda, scaling with ECS/Fargate for peak loads, monitoring Rekognition performance via X-Ray and CloudWatch, cost optimization with Savings Plans, final exercises on the enterprise red thread project, security and compliance audit, packaging of deployable solution with CI/CD CodePipeline, delivery of certified deliverables and post-training plan.