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 Cloud TPU - Accelerate Deep Learning in 2026 training are: Advanced mastery of TensorFlow/JAX, experience with Google Cloud Platform, large-scale deep learning training. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Google Cloud TPU - Accelerate Deep Learning in 2026 training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training Google Cloud TPU - Accelerate Deep Lear... | 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 Cloud TPU - Accelerate Deep Learning in 2026 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 Cloud TPU - Accelerate Deep Learning in 2026 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 Cloud TPU - Accelerate Deep Learning in 2026 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 Google Cloud TPUs for ultra-fast professional deep learning training

Configure optimized TPU v5p clusters for enterprise models

Develop scalable training pipelines with XLA and JAX

Optimize TPU performance to reduce cloud costs by 70%

Implement certified TPU production deployment workflows

Design hybrid CPU/TPU architectures for advanced deep learning skills

Training program

Module 1Google Cloud TPU Fundamentals: Configuration and Initial Benchmarks (TensorFlow, JAX, XLA)

Dive into the Google Cloud TPU environment with creation of v5p clusters, hands-on with the Vertex AI console, installation of TensorFlow and JAX libraries adapted to accelerators, execution of benchmarks on ImageNet datasets, comparison of GPU vs TPU performance, practical exercises to measure x10 acceleration, production of initial optimization reports applicable in enterprise settings.

Module 2Google Cloud TPU Optimization: XLA Compilation and Model Sharding (Pod Strategies, Multi-Slicing)

Deepen XLA compilation for TPU with transformation of PyTorch/TensorFlow models, implement automatic and manual sharding on TPU slices, test Transformer architectures on massive datasets like C4, analyze bottlenecks via TensorBoard Cloud, perform optimization exercises reducing training time by 50%, generate reusable configuration deliverables for professional projects.

Module 3Advanced Google Cloud TPU Pipelines: Distributed Training and Fine-Tuning (Vertex AI, Kubeflow)

Build end-to-end pipelines on Vertex AI with Kubeflow orchestration for TPU Pods, integrate fine-tuning of LLMs like Gemini on custom datasets, manage multi-host distribution with AllReduce, simulate enterprise cases with 1B parameters, perform automated hyperparameter tuning, produce production-ready trained models, evaluate gains in accuracy and speed to demonstrate immediate ROI.

Module 4Google Cloud TPU Production Deployment: Monitoring and Scaling (Cloud Run, Autoscaling, Security)

Deploy your TPU models in serving with Cloud Run and intelligent autoscaling, configure advanced monitoring via Cloud Monitoring for latency and costs, integrate IAM and VPC security for enterprise environments, test high-load serving on real inferences, optimize costs with TPU spot instances, finalize a complete ongoing project with performance dashboard, prepare TPU skills certifications for career boost.

Evaluation method

Expert multiple-choice quiz to validate TPU knowledge at the end of the training
Continuous evaluation through practical benchmarks and optimizations
Defense of the TPU deep learning pipeline in front of the trainer