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 Google Vertex AI - Deploying ML Models in Production training are: Experience in Python, basic machine learning (TensorFlow or PyTorch), knowledge of Google Cloud Platform. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Google Vertex AI - Deploying ML Models… | 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 Google Vertex AI - Deploying ML Models 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 Google Vertex AI - Deploying ML Models 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 Google Vertex AI - Deploying ML Models 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 to orchestrate professional MLOps pipelines

Develop and train scalable ML models on Vertex AI

Implement automated deployment of certifiable models in production

Optimize monitoring and version management in Vertex AI

Design end-to-end workflows for enterprise MLOps skills

Integrate Vertex AI into enterprise CI/CD environments

Training program

Module 1Vertex AI: MLOps Pipelines and Development Environment (GCP, Vertex AI Workbench)

Dive into the Vertex AI ecosystem by creating a dedicated workspace, setting up Jupyter environments via Vertex AI Workbench, exploring integrated services like Pipelines and Model Registry, building your first end-to-end MLOps pipeline on real enterprise datasets, practical exercises to automate data ingestion and ML preprocessing, producing a functional deliverable ready for iteration, with a focus on professional scalability and security.

Module 2Vertex AI: Model Training and Tuning (AutoML, Custom Training Jobs)

Master distributed training on Vertex AI by launching custom jobs with TensorFlow and PyTorch, using hyperparameter tuning to optimize performance, comparing AutoML versus manual training on concrete cases like image classification or real-time prediction, hands-on exercises to scale on GPU/TPU, generating detailed metrics and Model Garden artifacts, creating an optimized deployable model for enterprise use, directly enhancing your certifiable MLOps skills.

Module 3Vertex AI: Model Deployment and Serving (Endpoints, Online/Offline Prediction)

Deploy your models to production via Vertex AI Endpoints with automatic scaling configuration, batch and online prediction tests on simulated enterprise traffic, integrating drift monitoring and explainability via Vertex Explainable AI, practical exercises for A/B model testing, secure rollback and traffic splitting management, producing a live endpoint connected to a REST API, demonstrating immediate value for professional workflows and reduced time-to-market.

Module 4Vertex AI: Advanced Monitoring and MLOps CI/CD (Vertex Pipelines, GitHub Actions Integrations)

Orchestrate complete CI/CD with Vertex Pipelines and GitHub/GitLab integrations, implement continuous monitoring via Vertex Model Monitoring to detect degradations, exercises on automated remediation and retraining triggers, enterprise case study with full MLOps lifecycle, finalizing the ongoing project with production-like deployment, code review and GCP cost optimization, acquiring certifiable skills to boost your career in scalable AI.