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 Mixtral 2026 - Developing Generative AI Apps training are: Proficiency in Python, basic knowledge of machine learning and REST APIs. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Mixtral 2026 - Developing Generative AI Apps training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Mixtral 2026 - Developing Generative AI... | 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 Mixtral 2026 - Developing Generative AI Apps 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 Mixtral 2026 - Developing Generative AI Apps 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 Mixtral 2026 - Developing Generative AI Apps 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 access terms and deadlines

Training objectives

Master Mixtral 2026 to create certified enterprise AI solutions

Develop advanced prompts optimizing generative capabilities

Implement fine-tuning and RAG with professional Mixtral 2026

Design scalable AI pipelines for enterprise projects

Optimize and deploy Mixtral 2026 models in production

Integrate Mixtral 2026 into secure full-stack applications

Evaluate AI performance for concrete business gains

Training program

Module 1Advanced Fundamentals of Mixtral 2026: Architecture and Installation (Hugging Face, Ollama, Local Tools)

Dive into the MoE architecture of Mixtral 2026, install the environment via Hugging Face and Ollama for rapid deployment, explore the specifics of 8 experts per layer with practical exercises on enterprise datasets, generate your first optimized outputs, and produce an initial benchmark report to evaluate immediate performance gains in your professional projects.

Module 2Prompt Engineering for Mixtral 2026: Advanced Techniques and Chain-of-Thought (LangChain Tools, Business Examples)

Master prompt engineering with chain-of-thought and few-shot learning tailored to Mixtral 2026, test complex templates via LangChain for precise RAG tasks, apply to concrete cases like customer data analysis or code generation, optimize responses to reduce hallucinations by 40%, and validate with a red thread exercise producing reusable enterprise prompts.

Module 3Fine-Tuning Mixtral 2026: LoRA Customization and Datasets (PEFT, Accelerate, Sector-Specific Cases)

Set up efficient fine-tuning with LoRA on Mixtral 2026 using PEFT and Accelerate, prepare customized datasets for sectors like finance or marketing, train on cloud GPUs with metric tracking, test improvements on specific enterprise tasks, generate deliverable adapted models, and analyze ROI in terms of increased accuracy for your professional AI skills.

Module 4Integration and RAG for Mixtral 2026: APIs and Vector Stores (FAISS, Pinecone, FastAPI)

Integrate Mixtral 2026 via secure APIs with FastAPI, implement advanced RAG using FAISS or Pinecone for reliable contextual responses, connect to enterprise databases for intelligent chatbots, develop a full-stack prototype with exercises on real workflows, optimize latency for production, and produce a deployable MVP demonstrating concrete productivity gains.

Module 5Deployment and Optimization of Mixtral 2026: Scaling and Monitoring (Docker, Kubernetes, MLflow)

Deploy Mixtral 2026 in production with Docker and Kubernetes for scalability, monitor via MLflow and Prometheus to detect drifts, optimize inference with quantization and batching reducing GPU costs by 50%, apply to the enterprise red thread project, evaluate business performance, and finalize with a maintenance plan certifying your professional generative AI skills.