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: Advanced mastery of AWS (Lambda, API Gateway, S3), advanced Python, knowledge of LLMs and fine-tuning. 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 distanciel. 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 to orchestrate generative AI models in professional production environments

Develop custom AI agents with RAG and secure guardrails

Implement scalable and cost-optimized inference pipelines

Design hybrid Bedrock architectures with multi-model agents

Optimize performance and GDPR compliance in enterprise projects

Deploy certified AI applications connected to enterprise data

Training program

Module 1AWS Bedrock: Advanced Architecture and Foundation Models (Claude, Llama, Titan)

Dive into the serverless architecture of AWS Bedrock, configure access to models via console and Python/Boto3 SDK, explore the InvokeModel and Converse APIs for optimized invocations, perform comparative benchmarks on real enterprise use cases such as content generation, create your first advanced prompts with few-shot learning, and validate through an exercise migrating an open-source LLM to Bedrock with latency and cost measurements.

Module 2AWS Bedrock: Model Customization and RAG Knowledge Bases (vector stores, embeddings)

Customize Bedrock models via fine-tuning and LoRA on enterprise datasets, integrate Knowledge Bases with Amazon OpenSearch for high-performance RAG, chain retrieval and generation with LangChain on Bedrock, test on concrete cases like internal Q&A or industry-specific chatbots, optimize embeddings with Titan Embeddings, generate deliverables such as a deployed RAG prototype and a precision/recall report, while measuring immediate business impact.

Module 3AWS Bedrock: AI Agents and Security Guardrails (evaluation, moderation, compliance)

Build autonomous agents with Bedrock Agents and Action Groups for complex workflows, implement custom guardrails to filter biases and toxicity via Prompt Flows, integrate automated evaluation with Bedrock Model Evaluation, simulate sensitive enterprise scenarios such as finance or healthcare, deploy security hooks with Lambda, produce a complete agent with CloudWatch logging and monitoring dashboard, demonstrating a 50% reduction in non-compliance risks.

Module 4AWS Bedrock: Production Deployment and Optimization (scaling, costs, monitoring)

Deploy Bedrock pipelines in production via Provisioned Throughput and auto-scaling, optimize costs with Savings Plans and spot instances for inference, monitor with CloudWatch and X-Ray for strict SLOs, integrate CI/CD with CodePipeline for model updates, test high availability on critical enterprise cases, finalize with a scalable red thread project including calculated ROI, and prepare for skills certification to showcase your professional generative AI expertise.