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 TensorFlow - Deploy High-Performance AI Models training are: Mastery of Python, machine learning basics, initial experience with TensorFlow, and deep learning fundamentals.. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training TensorFlow - Deploy High-Performance AI Models training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training TensorFlow - Deploy High-Performance AI... | 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 TensorFlow - Deploy High-Performance 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 TensorFlow - Deploy High-Performance 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 TensorFlow - Deploy High-Performance 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 access terms and deadlines

Training program

Module 1Day 1: Installation and TensorFlow Pipelines: Set Up Advanced Environments (GPU, Massive Datasets)

Install TensorFlow 2.x with Anaconda and Docker, configure CUDA to accelerate GPU computations, handle datasets with tf.data for smooth pipelines, complete initial exercises on large imports, test deliverables like an optimized data loader, and gain speed and reliability for your daily AI projects.

Module 2Day 2: Deep Learning Models: Build CNNs and RNNs with Keras-TensorFlow

Dive into Keras to build convolutional networks on real images, train RNN/LSTM for time series, apply callbacks for precise monitoring, code practical exercises on CIFAR-10 and IMDb datasets, produce saved models ready to scale, and experience TensorFlow's power for stunning predictions.

Module 3Day 3: Optimization and Debugging: Boost TensorFlow Performance and Accuracy

Master TensorBoard for training visualization, apply pruning and quantization to reduce model sizes by 50%, debug with tf.debugging, optimize hyperparameters using Keras Tuner, address real cases of overfitting, generate performance reports, and transform slow models into efficient, deployable champions.

Module 4Day 4: Production Deployment: Integrate TensorFlow Serving and Cloud

Deploy with TensorFlow Serving on Docker/Kubernetes, integrate with Flask for REST APIs, export to TensorFlow Lite for mobile, test on GCP Vertex AI, complete a full project with Prometheus monitoring, deliver a scalable AI app, and leave with skills to monetize your models in enterprise settings tomorrow.