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 Vertex AI - Deploying Generative AI in Production training are: Experience in Python, fundamentals in Machine Learning and Google Cloud Platform. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Vertex AI - Deploying Generative AI in Production training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Vertex AI - Deploying Generative AI in ... | 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.

Configure your training

Response within 24hNo commitment100% free

8 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training Vertex AI - Deploying Generative AI in Production 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 Vertex AI - Deploying Generative AI in Production 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 Vertex AI - Deploying Generative AI in Production 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 Vertex AI pipelines for professional AI workflows

Develop and train scalable ML models with Vertex AI

Configure Vertex AI Studio to create generative AI applications

Implement automated deployments and monitoring in production

Optimize costs and performance of enterprise AI solutions

Integrate Vertex AI with APIs and certified applications

Design conversational AI agents for real business use cases

Training program

Module 1Vertex AI Fundamentals: Configuration and Data Pipelines (Google Cloud Console, Vertex AI SDK, Prepared Datasets)

Installation and configuration of the Vertex AI environment on Google Cloud, creation of structured datasets for ML training, setup of initial automated pipelines with Vertex AI Pipelines, practical exercises on importing real enterprise data, validation of flows with integrated monitoring, production of a functional pipeline ready for model training, immediate feedback from the trainer to accelerate your professional skills.

Module 2Vertex AI Model Training: AutoML and Custom Training (Vertex AI Workbench, Hyperparameters, TensorFlow/PyTorch)

Exploration of Vertex AI Workbench for collaborative notebooks, model training with AutoML for quick performance gains, advanced customization via TensorFlow or PyTorch on complex datasets, real-time hyperparameter optimization, practical cases inspired by business predictions in enterprise, evaluation of precision and recall metrics, delivery of a trained deployable model with annotated source code and dedicated optimization plan.

Module 3Vertex AI Studio: Generative AI and Agents (Gemini, Prompt Engineering, Model Fine-Tuning)

Dive into Vertex AI Studio to generate content with Gemini, mastery of prompt engineering for precise and contextual responses, creation of conversational AI agents tailored to business needs, fine-tuning of generative models on enterprise data, interactive tests on chatbots and virtual assistants, secure integration with external APIs, development of a complete AI application with tested deliverables deployed to a production endpoint.

Module 4Vertex AI Deployment and MLOps: Scaling and Monitoring (Endpoints, Vertex AI Feature Store, CI/CD)

Deployment of models to scalable endpoints with auto-scaling, configuration of advanced monitoring to detect production drifts, use of Vertex AI Feature Store for reusable features, setup of CI/CD pipelines for continuous deployments, cost optimization via Cloud quotas and budgets, simulation of real incidents with rapid resolution, finalization of a certifying capstone project with performance report and MLOps strategy for the enterprise.