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Training Multimodal RAG - Deploying High-Performance Multimodal AIs

Ref: QPP700
10 people max.
$6,600 HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$180/day onsite · +$540 with certification exam
5 days
remote

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Learning objectives

  • Master Multimodal RAG architectures to boost professional skills in hybrid AI
  • Develop RAG systems integrating text, image, and video using TensorFlow and PyTorch in certified training
  • Design optimized MLOps pipelines for Multimodal RAG in enterprise settings
  • Implement advanced multimodal retrievers with precise performance evaluation
  • Optimize models for scalability and low latency in professional contexts
  • Deploy robust Multimodal RAG applications with integrated MLOps monitoring

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Advanced Fundamentals of Multimodal RAG: Hybrid Architectures (TensorFlow, PyTorch)

Dive into expert concepts of Multimodal RAG, exploring cross-modal embeddings for text, images, and audio using TensorFlow and PyTorch. Conduct practical exercises on multimodal datasets like LAION or CLIP, configure hybrid dense retrievers, test real-time modality fusion, produce an initial evaluable prototype with metrics like Recall@K and NDCG, while integrating best MLOps practices for immediate scalability.

Module 2Multimodal Retrievers in Multimodal RAG: Indexing and Hybrid Search (PyTorch, FAISS)

Build high-performance multimodal indexes with PyTorch and FAISS, integrate advanced contrastive retrievers for multiple modalities, apply data augmentation techniques on real enterprise cases like visual-textual search, optimize cosine similarity and late-interaction, develop a neural reranking module, generate quantified performance reports, and prepare for MLOps integration with Docker containers.

Module 3Augmented Generation in Multimodal RAG: Fusion and Fine-Tuning (TensorFlow, LlamaIndex)

Master generation in Multimodal RAG using TensorFlow and LlamaIndex, fine-tune LLMs like LLaVA on retrieved multimodal contexts, implement dynamic multi-modal prompts, test on benchmarks like MM-Vet and GroundingDINO, manage hallucinations with visual grounding, produce coherent outputs for e-commerce or AI diagnostics applications, and integrate MLOps hooks for automatic model versioning.

Module 4MLOps Pipelines for Multimodal RAG: Orchestration and Scaling (Kubeflow, MLflow)

Orchestrate end-to-end Multimodal RAG pipelines with Kubeflow and MLflow, automate distributed training on multi-node GPUs, deploy via Kubernetes with autoscaling, monitor real-time metrics like throughput and multimodal drift, implement CI/CD for iterative updates, simulate production loads on real enterprise cases, generate custom Grafana dashboards, ensuring full operational resilience.

Module 5Advanced Deployment and Optimization of Multimodal RAG: Security and Monitoring (TensorFlow Serving, Prometheus)

Finalize production deployment of Multimodal RAG with TensorFlow Serving and Prometheus, secure APIs against multimodal injections, optimize inference with quantization and distillation, integrate A/B testing for hybrid PyTorch-TensorFlow models, analyze logs for root-cause analysis, produce a complete deliverable: source code, documentation, and MLOps playbook ready for enterprise integration, with certification of acquired skills.

Evaluation method

  • Daily technical quizzes on Multimodal RAG architectures and MLOps
  • Capstone project: deployment of a complete Multimodal RAG system
  • Peer-review evaluation and Qualiopi-certified attestation

Learning method

  • Hands-on projects on real multimodal datasets with TensorFlow and PyTorch
  • Enterprise use cases in MLOps for scalable RAG
  • 6-month post-training support via dedicated platform
  • Agile methods with continuous expert feedback

Methods, materials and delivery

The Training Multimodal RAG - Deploying High-Performance Multimodal AIs program is delivered onsite or remote (blended-learning, e-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.

The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.

Equipment required

For the smooth delivery of the Training Multimodal RAG - Deploying High-Performance Multimodal AIs program, the following equipment is required:

  • Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
  • Training environments installed on workstations or accessible online
  • Course materials, hands-on exercises and complementary resources
  • Post-training access to materials and educational resources

For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.

* contact us for remote delivery feasibility** ratio varies depending on the program

Skills assessment methods

Assessment of skills acquired during the Training Multimodal RAG - Deploying High-Performance Multimodal AIs program is performed through:

  • During training: case studies, hands-on labs and professional scenarios
  • End of training: self-assessment questionnaire and skills evaluation by the trainer
  • After training: completion certificate detailing acquired skills

Program accessibility

Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.

Enrollment terms and lead times

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
Our method

Training quality, guaranteed at every step

Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.

Step 1

Rigorous trainer selection

Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.

  • Triple validation: technical, pedagogical, sectoral.
  • Minimum rating 4.8/5 over the last 12 sessions.
Step 2

You meet the trainer beforehand

30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.

  • Live briefing on goals and team context.
  • Veto right — we swap the trainer for free if needed.
Step 3

Content tailored to your context

No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.

  • Hands-on cases drawn from your stack and projects.
  • Program co-written then validated by your team.
Step 4

Continuous quality follow-up

Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.

  • NPS, knowledge quizzes and skills self-assessment.
  • Satisfaction guarantee: fully satisfied or free rework.

A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.

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