🎁Azure · AWS · Google — 1 free certification per learner, up to $400.Get the offer →
← Back

Training Synthetic Data Generation - Creating Scalable AI Datasets

Ref: VTQ997
10 people max.
6125€ 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

Share in 2 clicks

EquansAptarArcelorMittalUbisoftINSEECLa PlateformeCESIEFREIEPSIINGETISMy Digital SchoolYnovEquansAptarArcelorMittalUbisoftINSEECLa PlateformeCESIEFREIEPSIINGETISMy Digital SchoolYnov

Learning objectives

  • Master GAN and VAE techniques for professional synthetic data generation
  • Develop synthetic data generation pipelines adapted to enterprise constraints
  • Implement tools like SDV and CTGAN for certified, GDPR-compliant datasets
  • Optimize synthetic data quality via advanced fidelity metrics
  • Design synthetic data strategies to accelerate production AI projects
  • Deploy scalable data generation solutions for data teams
  • Acquire certifying skills in synthetic data to enhance expertise

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.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Advanced Fundamentals: Modeling Distributions for Synthetic Data Generation (GAN, VAE, Probabilistic Tools)

Dive into GAN and VAE algorithms to faithfully recreate complex real data distributions, install and configure TensorFlow Probability and PyTorch for rapid prototypes, perform practical exercises on sensitive tabular datasets like health or finance, evaluate initial quality via KS-test and Wasserstein distance, produce your first GDPR-compliant synthetic dataset with controlled privacy budgets, and document metrics for immediate professional use.

Module 2Open-Source Tools: Implementing Synthetic Data Generation with SDV and CTGAN (Automated Pipelines)

Get hands-on with the SDV library for generating multi-table relational synthetic data, integrate CTGAN for heterogeneous tabular data with custom conditions, develop automated Python scripts on concrete enterprise cases like e-commerce or IoT, test correlation preservation via PCA and Pearson correlations, optimize hyperparameters to scale on large volumes, and generate a deliverable ready for ML training with a detailed validation report.

Module 3Quality and Privacy: Evaluating Synthetic Data for Robust ML (Metrics, Differential Privacy)

Learn to measure synthetic data fidelity with SNBA, train-test overlap, and downstream ML performance, integrate differential privacy via Opacus or TensorFlow Privacy to protect sensitive data, apply these techniques to your ongoing project with anonymized real datasets, simulate inference attacks to validate robustness, adjust models to balance utility and privacy, and produce a quality monitoring dashboard usable in professional data teams.

Module 4Enterprise Scalability: Orchestrating Synthetic Data Pipelines (Dask, Kubernetes, Cloud)

Design distributed workflows with Dask and Ray to generate terabytes of synthetic data, deploy on AWS SageMaker or GCP Vertex AI with Docker containers, integrate into CI/CD pipelines using GitHub Actions for automatic regeneration, test scalability on virtual clusters with timed exercises, manage cloud costs via spot instances, and finalize a deployable blueprint to accelerate ML iterations in production, boosting your company's competitiveness.

Module 5Advanced Cases: Synthetic Data for Generative AI and Edge Cases (Time-Series, Images, Multimodal)

Explore synthetic time-series generation with TimeGAN and images via adapted StyleGAN, combine modalities for multimodal datasets using CLIP embeddings, resolve edge cases like imbalanced classes or rare events with hybrid SMOTE-GAN approaches, apply everything to a real enterprise challenge with fine-tuning, evaluate impact on final ML accuracy, and prepare a certifying project presentation with source code, report, and strategic recommendations for maximum ROI.

Evaluation method

  • Technical quiz on synthetic data algorithms and tools
  • Practical evaluation via ongoing project and quality metrics
  • Oral defense of the deployed pipeline before experts

Learning method

  • Sessions led by active data scientist trainers
  • Hands-on exercises on real and sensitive company datasets
  • Evolving 5-day ongoing project for lasting skills
  • Complete digital materials and provided source code

Methods, materials and delivery

The Training Synthetic Data Generation - Creating Scalable AI Datasets 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 Synthetic Data Generation - Creating Scalable AI Datasets 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 Synthetic Data Generation - Creating Scalable AI Datasets 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.

Your professional training, anywhere

Let's build
your next
program.

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

Reply within 24 h · Industry-certified · Corporate funding
WhatsApp