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Training Supervised Fine-Tuning (SFT) - Mastering AI Software Testing

Ref: CKM703
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 supervised fine-tuning (SFT) for generating automated unit tests in certified professional training
  • Develop expert skills in optimizing AI models for functional tests in business environments
  • Design SFT pipelines tailored to software quality assurance using Hugging Face tools
  • Implement advanced SFT strategies to reduce false positives in software testing
  • Optimize the performance of fine-tuned LLMs for scalable and certified QA
  • Evaluate the business impact of SFT in professional automated testing contexts

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 1SFT Fundamentals: Preparing Software Testing Datasets (PyTorch, Hugging Face)

Discover advanced principles of supervised fine-tuning applied to software testing, by preparing labeled datasets from real source code and QA logs. Use PyTorch and Hugging Face Transformers to structure instruction-response pairs focused on unit tests, perform automated data cleaning, and validate quality using expert metrics like BLEU and ROUGE. Conclude with a practical exercise curating 500 examples for a first simulated fine-tuning, with real-time feedback to optimize your professional workflow.

Module 2SFT Architectures: Fine-Tuning LLMs for Unit Tests (LoRA, QLoRA)

Dive into optimized architectures for SFT with LoRA and QLoRA, tailored to expert unit test generation. Set up training sessions on cloud GPUs via Google Colab Pro, integrate specialized software testing prompts, and monitor convergence with Weights & Biases. Perform a complete fine-tuning on a base model like Mistral-7B, generate 100 unit tests for an open-source project, analyze results using coverage tools like pytest, and iterate on hyperparameters for over 90% accuracy.

Module 3Advanced SFT: Integrating Functional Tests and QA (PEFT, Hybrid RLHF)

Explore PEFT techniques for hybrid SFT integrating functional tests and quality assurance, using multi-modal datasets from Selenium and Cypress. Implement training loops with simulated human feedback via lightweight RLHF, deploy on Dockerized environments, and test robustness against edge cases in software QA. Produce a deliverable report with F1-score metrics > 0.95, exercises on automated mutation testing, and memory optimization for enterprise scaling.

Module 4SFT Deployment: CI/CD Pipelines for AI Software Testing (Kubernetes, MLflow)

Master the deployment of fine-tuned SFT models in CI/CD pipelines dedicated to software testing, using MLflow for tracking and Kubernetes for orchestration. Integrate your QA LLM into GitHub Actions, automate functional test generation post-commit, and set up A/B tests to validate speed gains. Complete an end-to-end project on an e-commerce repo, measure ROI through 40% reduction in testing time, and prepare REST APIs for seamless production integration.

Module 5Optimization and Expert SFT Cases: Scalable QA in Enterprise (DistilBERT, TRL)

Perfect SFT optimization for scalability in software QA using DistilBERT and TRL libraries, handling massive volumes of legacy code. Analyze real cases from Fortune 500 companies, implement knowledge distillation for lightweight models, and evaluate via custom benchmarks like HumanEval-QA. Finalize with a hackathon challenge: fine-tune a model for 1000+ unit/functional tests, produce a certifying portfolio, and plan a post-training roadmap for maximum ROI.

Evaluation method

  • Advanced technical quizzes on SFT and QA metrics daily
  • Practical projects evaluated by experts with video feedback
  • Final Qualiopi certifying exam on real-world software testing cases

Learning method

  • Active pedagogy with 70% hands-on practice on real QA datasets
  • Unlimited access to Jupyter labs and pre-fine-tuned models
  • 3-month post-training support by expert mentors
  • Alumni community for ongoing exchanges in AI testing

Methods, materials and delivery

The Training Supervised Fine-Tuning (SFT) - Mastering AI Software Testing 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) - Mastering AI Software Testing 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) - Mastering AI Software Testing 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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