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 Triton Inference Server - Deploying Scalable ML Inference training are: Experience in machine learning, Docker, Kubernetes, advanced Python and TensorFlow/PyTorch models. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Triton Inference Server - Deploying Scalable ML Inference training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Triton Inference Server - Deploying… | 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 Triton Inference Server - Deploying Scalable ML Inference 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 Triton Inference Server - Deploying Scalable ML Inference 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 Triton Inference Server - Deploying Scalable ML Inference 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 Triton Inference Server architecture for professional deployments

Configure multiple backends and optimize models in the enterprise

Implement dynamic batching and advanced concurrent management

Integrate Triton with Kubernetes for certified scalability

Develop monitoring and troubleshooting in real production

Deploy high-performance and secure inference pipelines

Training program

Module 1Installing Triton Inference Server: Advanced Backend Configuration (Docker, TF/PyTorch Models)

Complete setup of Triton Inference Server via Docker and Helm on Kubernetes, configuration of model repositories with secure protocols, loading and validation of TensorFlow, PyTorch, and ONNX models on real datasets like CIFAR-10, initial inference tests via gRPC/HTTP clients, practical exercises to tune initial configs and measure latency, production of a functional setup report ready for production.

Module 2Model Management in Triton Inference Server: Multi-Models and Versioning (Ensembles, Instances)

Advanced exploration of model ensembles and sequences in Triton Inference Server, management of multiple versions for A/B testing in the enterprise, configuration of dynamic GPU/CPU instances with memory sharing, integration of custom models via C++ or Python backends, hands-on workshops on e-commerce cases with anomaly detection, development of automation scripts for zero-downtime updates, delivery of an operational versioning pipeline.

Module 3Performance Optimization in Triton Inference Server: Batching and Scheduling (TensorRT, Dynamic)

Deep dive into dynamic and static batching in Triton Inference Server to maximize throughput, optimization with TensorRT and ONNX Runtime, tuning the scheduler for real-time priorities, simulations of high loads on GPU clusters, hands-on exercises to reduce latency by 50% on vision tasks, performance metrics analysis with Prometheus, creation of an optimized model deployed and benchmarked against baselines.

Module 4Ecosystem Integration for Triton Inference Server: Kubernetes and Monitoring (Kserve, Grafana)

Scalable deployment of Triton Inference Server on Kubernetes with HPA autoscaling, Kserve integration for serverless ML, advanced monitoring setup via Prometheus/Grafana for traces and logs, securing with TLS and RBAC, practical enterprise cases on NLP pipelines, development of custom dashboards for performance alerts, completion of a production-ready HA cluster with failover exercises.

Module 5Production and Troubleshooting for Triton Inference Server: High Availability (CI/CD, Debugging)

High availability strategies and CI/CD for Triton Inference Server with GitOps and ArgoCD, advanced diagnosis of GPU crashes using NVIDIA tools and detailed logs, cloud cost optimization with spot instances, real incident simulations in workshops, implementation of a complete red thread project with API gateway, delivery of a troubleshooting playbook and certified rollout plan for your company.