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 - Deploying Scalable Generative AIs training are: Mastery of AWS (IAM, Lambda, S3), advanced Python, basics of LLMs and prompt engineering. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Amazon Bedrock - Deploying Scalable… | 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 Amazon 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 Amazon 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 Amazon 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 Amazon Bedrock to orchestrate generative AI models in the enterprise

Develop intelligent agents with Knowledge Bases and Guardrails

Implement scalable and secure RAG pipelines

Optimize Bedrock invocation costs and performance

Design certified AI applications integrating Bedrock

Deploy professional monitored AI solutions

Training program

Module 1Amazon Bedrock Architecture: configuring models and secure access (IAM, API endpoints)

Dive into the advanced architecture of Amazon Bedrock, configure fine-grained IAM access and provision models like Claude or Llama via the AWS console, test initial API calls with Python/Boto3 SDK on real enterprise cases, implement key rotation and CloudWatch logging for GDPR compliance, produce a first production-ready customized model with practical exercises and personalized code review.

Module 2Amazon Bedrock Agents: creating intelligent workflows (Agents, Action Groups, Knowledge Bases)

Build autonomous agents with Amazon Bedrock Agents, integrate vector Knowledge Bases on Amazon OpenSearch, define Action Groups for external Lambda calls, test multi-step reasoning chains on business scenarios like AI customer support, optimize prompts for maximum accuracy, generate functional deliverables with trace monitoring via Bedrock console, transform your ideas into deployable prototypes in just a few hours.

Module 3RAG and Guardrails in Amazon Bedrock: implementing secure augmented retrieval (Embeddings, Filters)

Master advanced RAG with Amazon Bedrock, generate Titan embeddings and store them in vector stores like Pinecone or Kendra, implement Guardrails to filter sensitive content and hallucinations, integrate into Streamlit apps for interactive demos, analyze performance with latency and token usage metrics, develop a complete red-thread RAG project on confidential enterprise data, secure and scale for immediate production use.

Module 4Amazon Bedrock Deployment: optimizing costs and monitoring (Provisioned Throughput, CloudWatch)

Deploy to production with Provisioned Throughput for predictable workloads, optimize costs via auto-scaling and spot instances, configure CloudWatch alarms and X-Ray for end-to-end tracing, migrate legacy ML to serverless Bedrock, perform stress tests on 1000+ requests/second, produce custom dashboards and rollback plans, conclude with a certifying project defense, ready to boost your enterprise's AI innovation tomorrow.