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Training Supervised fine-tuning (SFT) 2026 - Optimize LLMs for High-Performance APIs

Ref: ZXT840
10 people max.
5500€ HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Master advanced Supervised fine-tuning (SFT) 2026 techniques for LLMs
  • Develop professional optimized datasets in certified training
  • Design SFT pipelines integrated into scalable enterprise APIs
  • Implement optimization strategies to reduce computational costs
  • Deploy SFT models in secure production API environments
  • Acquire certified SFT skills to boost your career

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 1Supervised fine-tuning (SFT) 2026: Advanced Dataset Preparation (Hugging Face, Professional Labeling)

Discover innovative dataset curation methods for SFT 2026 using Hugging Face Datasets and automated labeling tools like LabelStudio. Apply advanced cleaning techniques to minimize noise, complete practical exercises on real business cases, produce a fine-tuning-ready dataset with quality metrics, and integrate bias mitigation from the initial phase for robust models.

Module 2Supervised fine-tuning (SFT) 2026: Optimized LLM Architectures (LoRA, QLoRA)

Dive into LLM architectures suited for SFT 2026, configure LoRA and QLoRA for efficient fine-tuning on limited GPUs, train your first models on custom datasets with Hugging Face Transformers, analyze real-time loss curves, optimize hyperparameters via Optuna, and generate deliverables like modeled checkpoints ready for API deployment.

Module 3Supervised fine-tuning (SFT) 2026: API and Webhooks Integration (FastAPI, Swagger)

Integrate your SFT models into RESTful APIs via FastAPI, implement webhooks for real-time inference monitoring, document with OpenAPI and Swagger for rapid team adoption, test under load with Locust, develop secure endpoints with JWT, and produce a complete API prototype connected to a deployed SFT 2026 model.

Module 4Supervised fine-tuning (SFT) 2026: Optimization and Scaling (vLLM, Ray Serve)

Optimize SFT model performance with vLLM for ultra-fast inferences, scale on clusters via Ray Serve and Kubernetes, measure latency and throughput on API benchmarks, apply distillation for lightweight models, integrate monitoring with Prometheus, and generate performance reports and an automated CI/CD pipeline for continuous enterprise deployments.

Module 5Supervised fine-tuning (SFT) 2026: Production Deployment and Evaluation (A/B Testing, Metrics)

Deploy your SFT APIs to production via Docker and cloud providers like AWS SageMaker, configure A/B testing to validate gains, evaluate with advanced metrics like BLEU, ROUGE, and human eval, secure against prompt injection attacks, review real failure cases, and produce a certified project portfolio with source code and live API demo.

Evaluation method

  • Daily interactive quizzes on advanced SFT concepts
  • Practical pair projects with mentor feedback
  • Final exam and production API simulation

Learning method

  • Learning through concrete projects on real LLMs
  • Hands-on exercises in small groups of max 10
  • Personalized mentoring by certified experts
  • Reusable video supports and Jupyter notebooks

Methods, materials and delivery

The Training Supervised fine-tuning (SFT) 2026 - Optimize LLMs for High-Performance APIs 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 Supervised fine-tuning (SFT) 2026 - Optimize LLMs for High-Performance APIs 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 Supervised fine-tuning (SFT) 2026 - Optimize LLMs for High-Performance APIs 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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