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 Bedrock - Deploying Scalable Generative AIs training are: Basic AWS knowledge (S3, EC2, Lambda), Python and REST APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training AWS Bedrock - Deploying Scalable Generative AIs training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training AWS Bedrock - Deploying Scalable Genera... | 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 Bedrock - Deploying Scalable Generative AIs 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 Bedrock - Deploying Scalable Generative AIs 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 Bedrock - Deploying Scalable Generative AIs 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 Bedrock for developing professional AI applications

Design intelligent agents and certified automated workflows

Integrate custom foundation models in enterprise settings

Optimize security and cost of scalable AI deployments

Deploy generative solutions connected to enterprise data

Develop skills in RAG and fine-tuning for real-world cases

Training program

Module 1AWS Bedrock Fundamentals: Configuration and First AI Models (CLI, Console, SDK)

Dive into the AWS Bedrock environment via the console and CLI, install Python SDKs for rapid integration, test foundation models like Claude or Llama in playground mode, create your first advanced prompts with chain-of-thought, build a simple assistant connected to S3, produce a functional deliverable with code review by the trainer to validate your professional skills from day one.

Module 2AWS Bedrock Agents: Orchestration and Customization (Knowledge Bases, Guardrails)

Create intelligent agents with AWS Bedrock Agents, integrate Knowledge Bases for RAG on your enterprise data, configure Guardrails for security and moderation, develop custom Lambda actions, test multi-step workflows on concrete cases like document analysis, generate a deployable agent with performance metrics, and optimize prompts for improved accuracy in professional contexts.

Module 3AWS Bedrock Integration: Fine-Tuning and Scalable APIs (Embeddings, Batch Inference)

Master fine-tuning of models on AWS Bedrock with your enterprise datasets, implement embeddings for advanced semantic search, deploy Batch Inference pipelines for large volumes, connect to Amazon Bedrock via REST APIs and streaming, integrate with SageMaker for hybridization, complete exercises on code generation and report synthesis, produce a complete RAG prototype with quantitative evaluation of results.

Module 4AWS Bedrock Deployment: Optimization and Production Monitoring (CloudWatch, Costs)

Deploy your AWS Bedrock applications in production with API Gateway and Lambda, configure CloudWatch for real-time monitoring and alerts, optimize costs via Provisioned Throughput and caching, secure with IAM and KMS encryption, simulate scalable loads on real enterprise cases, finalize a red thread project with a custom dashboard, and prepare for certification by validating best practices for immediate and cost-effective implementation.

Evaluation method

Multiple-choice quiz to validate knowledge on AWS Bedrock at the end of the training
Continuous evaluation through practical exercises and developed agents
Defense of the red thread generative AI project in front of the trainer