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 Document AI Pipelines - Automate AI Analysis of Documents training are: Mastery of Python, ML frameworks (TensorFlow/PyTorch), NLP and computer vision. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Document AI Pipelines - Automate AI Analysis of Documents training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Document AI Pipelines - Automate 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 Document AI Pipelines - Automate AI Analysis of Documents 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 Document AI Pipelines - Automate AI Analysis of Documents 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 Document AI Pipelines - Automate AI Analysis of Documents 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 objectives

Master the architecture of Document AI pipelines for professional projects

Develop advanced extraction and OCR modules in enterprise settings

Implement semantic analysis models for certified documents

Orchestrate scalable pipelines with cloud tools and DevOps

Optimize the performance of Document AI pipelines in production

Deploy robust AI solutions for massive document processing

Acquire certifying skills in document AI automation

Training program

Module 1Architecture: designing robust Document AI pipelines (MLflow, Kubeflow, Docker)

Analysis of business needs to structure high-performance Document AI pipelines, hands-on with orchestration tools like MLflow and Kubeflow, modeling an end-to-end pipeline on real cases of invoices and contracts, practical exercises in containerization with Docker, creation of architecture diagrams and first functional prototype tested in groups to validate scalability.

Module 2Extraction: implement advanced OCR and parsing in Document AI pipelines (Tesseract, LayoutLM)

Deployment of advanced OCR models like Tesseract and LayoutLM to extract text, tables, and images from complex documents, processing of scanned PDFs and various formats in batch, exercises on real enterprise datasets with automatic data cleaning, integration into a modular pipeline, production of structured datasets ready for AI analysis, evaluation of accuracy with custom metrics.

Module 3Analysis: integrate NLP and vision into Document AI pipelines (BERT, YOLO, Hugging Face)

Development of semantic analysis modules with BERT and transformers for classification and named entity recognition on documents, combination with YOLO for visual element detection, practical exercises on legal contracts and financial reports, fine-tuning of pre-trained models via Hugging Face, enrichment of the pipeline with confidence scoring, automated report generation and visualization of insights for rapid business decisions.

Module 4Orchestration: scale Document AI pipelines (Kubernetes, Airflow, AWS/GCP)

Configuration of orchestrators like Apache Airflow and Kubernetes for distributed pipelines, migration to cloud AWS SageMaker or GCP Vertex AI, management of asynchronous and parallel workflows on massive volumes, load peak simulation exercises with real-time monitoring, cost and resource optimization, end-to-end integration tests on the capstone project, documentation of deployments for DevOps teams.

Module 5Deployment: monitor and optimize Document AI pipelines in production (Prometheus, ELK)

Production deployment with CI/CD GitHub Actions, setup of advanced monitoring via Prometheus and ELK stack for logs and alerts, performance optimization through model pruning and distillation, debugging exercises on real incidents, A/B testing of pipeline versions, securing sensitive data in GDPR compliance, finalization of the capstone project with ROI report and maintenance plan for immediate enterprise use.