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 Azure Cognitive Services - Deploying Cognitive AI in Production training are: Advanced mastery of Azure, REST/GraphQL API development, Python/C#, ML basics and cloud deployment. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Azure Cognitive Services - Deploying Cognitive AI in Production training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Azure Cognitive Services - Deploying Co... | 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 Azure Cognitive Services - Deploying Cognitive AI in Production 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 Azure Cognitive Services - Deploying Cognitive AI in Production 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 Azure Cognitive Services - Deploying Cognitive AI in Production 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 Azure Cognitive Services APIs for professional AI solutions

Develop scalable computer vision applications in the enterprise

Implement speech recognition and NLP with performance optimization

Design custom models via Custom Vision and Language Understanding

Deploy secure and monitored AI pipelines on Azure

Optimize costs and scalability of certified cognitive services

Integrate Cognitive Services into modern microservices architectures

Training program

Module 1Azure Cognitive Services Architecture: Exploring Advanced Services and APIs (Vision, Speech, Decision)

Immersion in the Azure Cognitive Services ecosystem, configuration of an expert subscription with API keys and secure endpoints, in-depth exploration of Vision, Speech, and Language services via Azure portal and Python/.NET SDK, first API calls on real enterprise cases such as batch image analysis, practical exercises to integrate Azure AD authentication, creation of a red thread project with initial monitoring of quotas and costs.

Module 2Azure Cognitive Services Vision and Decision: Implementing Object Detection and Content Moderation (Custom Vision, Content Moderator)

Advanced development of vision solutions, training Custom Vision models on enterprise-customized datasets with automatic tagging and ONNX export, integration of Face API for secure biometric recognition, content moderation via Text and Image Moderator with custom rules, exercises on real-time video streams with Azure Functions, latency optimization via Redis caching, production of deployed and tested deliverables under simulated production conditions.

Module 3Azure Cognitive Services Speech and Language: Deploying Speech Recognition and NLP (Speech-to-Text, LUIS)

Mastery of audio services with custom Speech-to-Text models for multiple accents and ambient noise, implementation of neural Text-to-Speech for personalized voices in multiple languages, design of intelligent bots via LUIS v3 with predictive entities and dispatchers, integration of QnA Maker for enterprise knowledge bases, practical workshops on real-time transcription with WebSockets, sentiment analysis and key extraction via Text Analytics, serverless deployment with Application Insights monitoring.

Module 4Deployment and Optimization of Azure Cognitive Services: Scaling AI Pipelines in Production (Anomaly Detector, Personalizer)

Full orchestration of pipelines with Logic Apps and Azure ML for model hybridization, implementation of Anomaly Detector on critical business time series, user experience personalization via Personalizer multi-arm bandit, securing with Private Link and advanced RBAC, cost optimization via reservations and auto-scaling, final exercises on red thread project with CI/CD GitHub Actions, GDPR compliance audit, delivery of monitoring dashboard and certified AI governance plan.