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Training Vision-language models - Mastering Multimodal AI in the Enterprise

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

On quote$5,775

5 days
Onsite
10 people max.

Request a quote See the program

Ref: XWB442

10 people max.

On quote$5,775

In-company rate: on quotefrom $10,395 for the whole team

On-site and certification exam on quote

5 days

Onsite

Don't let this gap widen

Why this program matters

Without mastery of Vision-language models, 75% of data scientists risk falling behind the competition, while companies adopting multimodal AI boost their productivity by 40% according to Gartner.
This leads to estimated losses of 200k€ per project annually due to inefficient manual pipelines and doubled time-to-market.
Your career stagnates amid explosive demand for these skills, with 60% of ML job postings requiring VLMs by 2025.
Invest now to secure your expertise and propel your company to the forefront of AI.

Trusted by — schools, companies and industrial groups

25,719 programs · 25 countries

Every program built with a dedicated engineer

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

Learning objectives

Master the architectures of Vision-language models for professional certifying projects.
Develop skills in fine-tuning VLMs tailored to business needs.
Implement MLOps pipelines to deploy Vision-language models efficiently.
Design multimodal applications combining vision and language using TensorFlow and PyTorch.
Optimize VLM performance for scalable production integration.
Evaluate and deploy AI solutions based on Vision-language models in a professional context.

Program

Module 1Vision-language models: Fundamentals and Key Architectures (TensorFlow, PyTorch)

Discover the principles of Vision-language models through theoretical and practical modules, exploring CLIP, BLIP, and Flamingo with multimodal datasets like LAION or COCO. Perform joint vision-text encoding exercises, analyze multimodal transformers, and generate your first cross-modal embeddings for classification tasks, all in interactive remote sessions for rapid mastery of essential skills.

Module 2Vision-language models: Fine-tuning and Domain-Specific Adaptation (PyTorch)

Dive into fine-tuning VLMs on real business cases, use Hugging Face Transformers to adapt pre-trained models to your custom visual and textual data, implement LoRA and QLoRA techniques to optimize resources, test on benchmarks like VQA or captioning, generate performance reports, and deploy a functional prototype ready for professional integration.

Module 3Vision-language models: Advanced Multimodal Tasks (TensorFlow, PyTorch)

Explore advanced Vision-language model applications for visual search, description generation, and zero-shot learning. Code pipelines with TensorFlow for retrieval and PyTorch for generation, work on real datasets like Visual Genome, evaluate with FID and BLEU metrics, collaborate in live sessions to solve business challenges, and deliver a complete optimized notebook.

Module 4Vision-language models: MLOps Integration and Scalable Deployment

Integrate Vision-language models into professional MLOps workflows using Docker, Kubernetes, and MLflow. Set up inference servers with TensorRT and ONNX, automate CI/CD pipelines for multimodal model monitoring, test latency on cloud GPUs, manage enterprise model versions, and produce an end-to-end deployment with performance dashboards.

Module 5Vision-language models: Real Projects and Production Optimization (MLOps)

Complete a capstone project on a business application like medical image analysis or e-commerce using Vision-language models. Optimize for efficiency with distillation and pruning, integrate secure APIs, evaluate business impact via simulated ROI, present your solution in a professional pitch, receive certifying feedback, and leave with a concrete portfolio to boost your multimodal AI career.

Evaluation method

Interactive quizzes and multiple-choice questions at the end of each module to validate theoretical knowledge.
Hands-on practical projects evaluated by MLOps experts.
Real-world case study with oral presentation and technical report for certification.

Learning method

Active pedagogy with 70% hands-on practice on real code and professional datasets.
Live remote sessions in small groups for personalized follow-up.
Unlimited resources: notebooks, videos, and post-training forums.
Expert 1-to-1 mentoring to address your specific challenges.

Methods, materials and delivery

The Training Vision-language models - Mastering Multimodal AI in the Enterprise 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 Vision-language models - Mastering Multimodal AI in the Enterprise 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 Vision-language models - Mastering Multimodal AI in the Enterprise 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

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.

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

Frequently asked questions

Which tools are used in Vision-language models training?+

Hands-on work runs on tensorFlow, pyTorch and MLOps. Those tools come from the Vision-language models program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.

What can participants do after Vision-language models training?+

They can master the architectures of Vision-language models for professional certifying projects, develop skills in fine-tuning VLMs tailored to business needs and implement MLOps pipelines to deploy Vision-language models efficiently. Those are the stated objectives of the Vision-language models program at Intermediate level, checked by the end-of-session assessment.

What does the Vision-language models program cover, day by day?+

Day 1: Vision-language models: Fundamentals and Key Architectures (TensorFlow, PyTorch). Day 2: Vision-language models: Fine-tuning and Domain-Specific Adaptation (PyTorch). Day 3: Vision-language models: Advanced Multimodal Tasks (TensorFlow, PyTorch). Day 4: Vision-language models: MLOps Integration and Scalable Deployment. Day 5: Vision-language models: Real Projects and Production Optimization (MLOps). The Vision-language models program runs over 5 days, delivered on site.

How is Vision-language models training assessed?+

Assessment relies on interactive quizzes and multiple-choice questions at the end of each module to validate theoretical knowledge, hands-on practical projects evaluated by MLOps experts and real-world case study with oral presentation and technical report for certification. Learni issues a completion certificate together with the individual assessment report at the end of the Vision-language models program, which an L&D team can file as evidence.

How much does Vision-language models training cost per participant?+

Vision-language models training is priced on quote. The amount depends on headcount, format and how far the syllabus is tailored to your team. A detailed quote is sent within one business day, and team pricing covers the whole group from the second participant.

How long does Vision-language models training take?+

5 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 Vision-language models 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 Vision-language models training?+

Mastery of Python, basics in deep learning, PyTorch or TensorFlow, and knowledge of image and text processing.

Is a certificate issued after Vision-language models 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.

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