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Training Multimodal RAG - Integrating Text and Images in Advanced AI

Course created on July 27, 2026Updated on October 5, 2026

$2,310

2 days
Onsite
10 people max.

Request a quote See the program

Ref: QWI108

10 people max.

$2,310

In-company rate: from $4,190 for the whole team

On-site and certification exam on quote

2 days

Onsite

Don't let this gap widen

Why this program matters

Without mastering Multimodal RAG, your AI systems stay limited to text and ignore 70% of enterprise data such as log images or dashboards, producing incomplete analyses and productivity losses estimated at 25% according to Gartner.
Undetected supervision failures through multimodal monitoring cost large tech firms an average of 100k€ per major incident and expose data scientists and DevOps to career obsolescence against advanced AI competitors.
Avoid these traps by investing in certifiable skills that convert the risks into durable competitive advantages for your company.

Trusted by — schools, companies and industrial groups

40,841 programs · 25 countries

Every program built with a dedicated engineer

Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.

Multimodal RAG training objectives

Master Multimodal RAG architectures through certified professional training to boost enterprise AI skills
Develop pipelines integrating text, images, and structured data for precise contextual responses
Configure multimodal embeddings using tools like CLIP and Sentence Transformers
Implement advanced RAG agents for monitoring and supervision applications
Optimize RAG performance via fine-tuning and vector stores like Pinecone or FAISS
Deploy scalable Multimodal RAG solutions in production environments
Evaluate the impact of acquired skills on professional productivity

Multimodal RAG curriculum: 2 modules over 2 days

Module 1 : Multimodal RAG: Fundamentals and Architecture (Python, LangChain, Multimodal Embeddings)

Discover the basics of Multimodal RAG through concise theoretical modules, then dive into practical exercises with Python and LangChain to create embeddings unifying text and images via CLIP, integrate vector stores like FAISS for efficient hybrid search, analyze real corporate monitoring cases, produce your first functional Multimodal RAG prototype with live feedback from expert trainers, consolidate your learning through guided hands-on labs on real datasets mixing textual logs and dashboard captures.

Module 2 : Multimodal RAG: Optimization, Deployment, and Advanced Cases (Fine-tuning, Monitoring with Prometheus)

Advance to optimizing Multimodal RAG pipelines via fine-tuning models like Llama with PEFT, test supervision scenarios by integrating Grafana to visualize metrics, deploy on cloud with Docker and Kubernetes for scalability, explore real cases of ELK Stack enhanced with multimodal features, measure performance with RAGAS benchmarks, finalize with a deliverable capstone project where you integrate Datadog for alerting on degradations, receive a personalized audit and post-training resources for immediate production deployment.

How Multimodal RAG skills are assessed

Interactive quizzes at the end of each module to validate theoretical knowledge
Graded practical case studies on Multimodal RAG implementation
Final project evaluated by experts with detailed feedback and certification

How the Multimodal RAG course is taught

Active pedagogical methods with 70% hands-on practice on real code
Unlimited access to a remote platform with replays and resources
Individualized support by Qualiopi-certified mentors during and after training
Project-based learning inspired by corporate monitoring needs

Multimodal RAG training: access and practical arrangements

The Training Multimodal RAG - Integrating Text and Images in Advanced AI program is delivered onsite or remote (blended-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 - Integrating Text and Images in Advanced AI 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 - Integrating Text and Images in Advanced AI 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

Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.

The Learni story

Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.

Our method

How Learni guarantees the quality of your Multimodal RAG training

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.

A child walking to school with a backpack

Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

Backpack, notebooks and essential supplies
Distributed through our partner charities
Included, at no extra cost to you

Enroll and offer a kit

FAQ

Multimodal RAG: frequently asked questions

Which tools are used in Multimodal RAG training?+

Hands-on work runs on python, langChain, multimodal Embeddings, fine-tuning and monitoring with Prometheus. Those tools come from the Multimodal RAG program itself, delivered on site over 2 days, so participants practise on the stack they will use at work.

What can participants do after Multimodal RAG training?+

They can master Multimodal RAG architectures through certified professional training to boost enterprise AI skills, develop pipelines integrating text, images, and structured data for precise contextual responses and configure multimodal embeddings using tools like CLIP and Sentence Transformers. Those are the stated objectives of the Multimodal RAG program at Intermediate level, checked by the end-of-session assessment.

What does the Multimodal RAG program cover, day by day?+

Day 1: Multimodal RAG: Fundamentals and Architecture (Python, LangChain, Multimodal Embeddings). Day 2: Multimodal RAG: Optimization, Deployment, and Advanced Cases (Fine-tuning, Monitoring with Prometheus). The Multimodal RAG program runs over 2 days, delivered on site.

How is Multimodal RAG training assessed?+

Assessment relies on interactive quizzes at the end of each module to validate theoretical knowledge, graded practical case studies on Multimodal RAG implementation and final project evaluated by experts with detailed feedback and certification. Learni issues a completion certificate together with the individual assessment report at the end of the Multimodal RAG program, which an L&D team can file as evidence.

How much does Multimodal RAG training cost per participant?+

Multimodal RAG training costs $2,310 per participant. For an in-company team, the flat rate starts at $4,190 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.

How long does Multimodal RAG training take?+

2 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.

Who pays for Multimodal RAG training?+

Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.

What do participants need to know before Multimodal RAG training?+

Mastery of Python, basics in LLMs such as GPT or Llama, experience with unimodal RAG, knowledge of vector embeddings, and frameworks like LangChain or LlamaIndex.

Is a certificate issued after Multimodal RAG training?+

Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.

Does Learni provide the equipment for Multimodal RAG training?+

No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.

Your professional training, anywhere

Let's build your Multimodal RAG learning path.

30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.

Reply within 24h · Vetted experts · USD invoicing · W-9 available

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Training Multimodal RAG - Integrating Text and Images in Advanced AI

Multimodal RAG training objectives

Multimodal RAG curriculum: 2 modules over 2 days

Module 1 : Multimodal RAG: Fundamentals and Architecture (Python, LangChain, Multimodal Embeddings)

Module 2 : Multimodal RAG: Optimization, Deployment, and Advanced Cases (Fine-tuning, Monitoring with Prometheus)

How Multimodal RAG skills are assessed

How the Multimodal RAG course is taught

Multimodal RAG training: access and practical arrangements

How Learni guarantees the quality of your Multimodal RAG training

Multimodal RAG: frequently asked questions

Let's build your Multimodal RAG learning path.

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