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 AI Models training are: Proficiency in Docker/Kubernetes, TensorFlow/PyTorch/ONNX, Linux, and gRPC/HTTP APIs. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training Triton Inference Server - Deploying Sca... | 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

9 spots remaining for the next session

FormatParticipantsDateDetails

Which format do you prefer?

Methods, approaches, and teaching resources

The Training Triton Inference Server - Deploying Scalable AI Models 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 AI Models 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 AI Models 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 scalable multi-model servers in enterprise environments

Optimize inference performance with TensorRT and dynamic batching

Integrate Triton with Kubernetes for certified AI pipelines

Develop gRPC/HTTP clients and monitor in real production environments

Diagnose failures and secure critical inference flows

Training program

Module 1Triton Inference Server Fundamentals: Installation and Basic Models (Docker, TensorFlow)

Quick installation of Triton via Docker on cloud environments, loading simple TensorFlow models for immediate inferences, configuring backends and HTTP/gRPC protocols, first latency tests on real datasets, practical exercises to validate a standalone server, production of a setup report with basic metrics, all while simulating enterprise constraints for smooth and professional implementation.

Module 2Advanced Triton Inference Server Models: Multi-Frameworks (PyTorch, ONNX, TensorRT)

Exploration of PyTorch and ONNX backends for optimized model conversions, integration of TensorRT for NVIDIA GPU acceleration, management of dynamic model ensembles with versioning, exercises on mixed framework migrations, comparative throughput benchmarks, creation of a production-ready multi-model repository, focus on real enterprise cases like image recognition or NLP to boost practical skills and immediate value.

Module 3Scaling Triton Inference Server: Dynamic Batching and Kubernetes (Helm, Autoscaling)

Triton deployment on Kubernetes with custom Helm charts, activation of dynamic batching and instance groups for horizontal scaling, CPU/GPU-based autoscaling configuration, peak load simulations with Locust tools, memory optimization for 1000+ req/s, development of secure K8s manifests, delivery of a reproducible pilot cluster for enterprise use, transforming your AI pipelines into robust and scalable solutions.

Module 4Triton Inference Server Integration: gRPC/HTTP Clients and CI/CD Pipelines (GitLab, Triton CLI)

Development of Python/Go clients for asynchronous inferences via gRPC/HTTP, integration of Triton Model Analyzer for automated profiling, setup of CI/CD pipelines with rolling model updates, end-to-end tests on microservices, securing with TLS and auth tokens, exercises on your ongoing project, generation of automated deployment scripts, accelerating enterprise workflows toward certified and performant AI maturity.

Module 5Optimization and Monitoring of Triton Inference Server: Performance Tuning, Prometheus/Grafana (Troubleshooting)

Analysis of Triton metrics with Prometheus exporter and custom Grafana dashboards, performance tuning via model ensembling and sequence batching, GPU/CPU failure diagnostics with structured logs, failure simulations for resilience, cloud AWS/GCP cost/infra optimization, finalization of ongoing project with full audit, delivery of operational monitoring guide, ensuring 99.9% uptime and maximum ROI for critical production AI deployments.