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 Amazon Bedrock - Mastering Generative AI in the Enterprise training are: Basic AWS accounts, basic knowledge of Python and REST APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Amazon Bedrock - Mastering Generative AI in the Enterprise training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Amazon Bedrock - Mastering Generative AI… | 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.

Configure your training

Response within 24hNo commitment100% free

7 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training Amazon Bedrock - Mastering Generative AI in the Enterprise 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 Amazon Bedrock - Mastering Generative AI in the Enterprise 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 Amazon Bedrock - Mastering Generative AI in the Enterprise 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 the fundamentals of Amazon Bedrock for professional AI applications

Develop skills in invoking AI models via the AWS console

Design intelligent conversational agents with Bedrock

Implement RAG pipelines for contextual responses

Optimize costs and security in certified enterprise projects

Deploy scalable AI prototypes in a professional environment

Training program

Module 1Amazon Bedrock Fundamentals: Console Discovery and AI Models (API, Playground)

Dive into the world of Amazon Bedrock via the AWS console, set up the development environment with Python SDK and Boto3, explore foundation models like Claude and Llama to generate text, create your first simple prompts on concrete business cases, test in interactive playground with immediate feedback, produce an initial test report to evaluate the relevance of AI responses.

Module 2Amazon Bedrock Prompt Engineering: Advanced Techniques (Few-Shot, Chain-of-Thought)

Master the art of prompt engineering dedicated to Amazon Bedrock, apply few-shot and chain-of-thought methods on Titan and Jurassic models, practice on enterprise scenarios like marketing content generation, optimize response accuracy with collaborative iterations, integrate dynamic variables via Python code, create a deliverable: reusable prompt library for your teams.

Module 3Amazon Bedrock AI Agents: Construction and Orchestration (Knowledge Bases, Guardrails)

Build your first intelligent agents with Amazon Bedrock Agents, configure Knowledge Bases for custom RAG on enterprise data, integrate Guardrails for security and GDPR compliance, test multi-turn conversational flows via API, simulate real customer cases like automated support, develop a functional agent prototype with analysis logs and improvement plan.

Module 4Amazon Bedrock Integration: API and Pipelines (Lambda, Step Functions)

Integrate Amazon Bedrock into serverless architectures with AWS Lambda and Step Functions, code Python functions to invoke models in batch, manage vector embeddings with embedding models, deploy a complete RAG pipeline on S3 and OpenSearch, process business data volumes, produce an automated deployment testable in real enterprise conditions.

Module 5Amazon Bedrock Deployment and Optimization: Monitoring and Costs (CloudWatch, Provisioned Throughput)

Optimize your Amazon Bedrock solutions for production, configure CloudWatch for AI metrics monitoring, analyze costs with provisioned throughput and on-demand, secure IAM accesses and encrypt sensitive data, review the red thread project in collective code review, prepare an enterprise scaling plan with quantified ROI, validate skills via MCQ and presentation.

Evaluation method

Multiple-choice quiz to validate knowledge on Amazon Bedrock at the end of the training
Continuous evaluation through practical exercises and AI prototypes
Presentation of the red thread project: conversational agent