Rigorous trainer selection
Each trainer is validated on three criteria: hands-on field expertise, proven pedagogy and alignment with your industry.
- Triple validation: technical, pedagogical, sectoral.
- Minimum rating 4.8/5 over the last 12 sessions.
Learning objectives
- Master advanced TensorFlow APIs to develop certifiable skills in professional deep learning.
- Design and optimize complex architectures tailored to business needs.
- Implement MLOps pipelines with TensorFlow for smooth production deployment.
- Deploy scalable models using TensorFlow Serving and Kubernetes.
- Analyze and debug ML performance for measurable efficiency gains.
- Integrate TensorFlow into certified and secure enterprise workflows.
The Learni story
Founded by engineers and learning experts, Learni's mission is to make high-impact tech training accessible to teams everywhere. We work remotely with organizations across the US and Canada, in your time zone, to help teams upskill fast.
Don't let this gap widen
Why this program matters
Without this upskilling, your team accumulates a technological gap that translates directly into productivity loss.
Organizations that don't train their talents on key topics see their competitiveness drop.
Every quarter without training is a gap widening with competitors who invest.
The cost of inaction quickly exceeds that of well-targeted training.
Program
Module 1Advanced TensorFlow Architectures: CNN, RNN, Transformers (modeling, practical exercises)
Discover custom layers and hybrid models with TensorFlow, modeling deep convolutional networks for computer vision, then move to RNN and LSTM for sequential processing, with exercises on real datasets like CIFAR-10, optimizing hyperparameters via TensorBoard, and produce a first trained model deliverable for immediate performance analysis.
Module 2TensorFlow Optimization: Graph mode, TPU, pruning (benchmarks, automatic tuning)
Dive into TensorFlow's graph mode to accelerate training, integrate TPUs via Colab Pro for x10 speed gains, apply pruning and quantization techniques on pre-trained models like BERT, perform comparative benchmarks with PyTorch, and generate optimization reports proving latency reductions up to 70%, ready for enterprise integration.
Module 3TensorFlow MLOps Pipelines: Kubeflow, MLflow, monitoring (CI/CD deployment)
Build end-to-end pipelines with Kubeflow and TensorFlow Extended, integrate MLflow for experiment tracking, deploy via Docker and Kubernetes with automated tests, monitor model drift in real-time on concrete e-commerce cases, and validate workflow reproducibility, resulting in a functional, certifiable MLOps prototype for your team.
Module 4Advanced TensorFlow Deployment: Serving, Edge TFLite, scaling (real enterprise cases)
Master TensorFlow Serving for high-scale inferences, convert models to TFLite for edge deployment on mobile and IoT, scale with Kubernetes and auto-scaling, test on real use cases like anomaly detection in finance, integrate secure APIs, and finalize with a deliverable capstone project, with final optimization for immediate production.
Evaluation method
- Daily technical quizzes on advanced TensorFlow APIs.
- Final MLOps deployment project evaluated by experts.
- Qualiopi certifying attestation validating acquired skills.
Learning method
- 70% hands-on with exercises on real datasets and TensorFlow tools.
- Case studies from leading AI companies for contextualization.
- Individual mentoring in small groups of max 10 participants.
- 3-month post-training support with resources and Q&A.
Methods, materials and delivery
The TensorFlow Training - Optimizing and Deploying Advanced DL Models program is delivered onsite or remote (blended-learning, virtual classroom, remote presence). At Learni, an industry-certified training organization, every program is built to maximize skills acquisition regardless of the chosen format.
The trainer alternates between demonstrative, interrogative and active methods (through hands-on labs and/or scenarios). This pedagogical approach guarantees concrete learning that's immediately applicable at work.
Equipment required
For the smooth delivery of the TensorFlow Training - Optimizing and Deploying Advanced DL Models program, the following equipment is required:
- Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, projector or interactive touch screen (for remote sessions)
- Training environments installed on workstations or accessible online
- Course materials, hands-on exercises and complementary resources
- Post-training access to materials and educational resources
For intra-company training on a site outside Learni, the client commits to providing all required teaching materials (computers, internet, etc.) for the smooth delivery of the program in line with the prerequisites in the communicated program.
* contact us for remote delivery feasibility** ratio varies depending on the program
Skills assessment methods
Assessment of skills acquired during the TensorFlow Training - Optimizing and Deploying Advanced DL Models program is performed through:
- During training: case studies, hands-on labs and professional scenarios
- End of training: self-assessment questionnaire and skills evaluation by the trainer
- After training: completion certificate detailing acquired skills
Program accessibility
Learni is committed to making its programs accessible. All our programs are accessible to people with disabilities. Our teams are available to adapt the pedagogical methods to your specific needs. Please contact us for any adjustment request.
Enrollment terms and lead times
Registration is possible up to 48 business hours before the start of training. All our programs are built for corporate L&D budgets and delivered onsite or remotely.
Our method
Training quality, guaranteed at every step
Before, during, after: we frame the brief, introduce the trainer, tailor the content and measure impact. You stay in control from kickoff to wrap-up.
You meet the trainer beforehand
30-minute video call between you and the selected trainer to validate the fit, adjust content and clear any final doubts.
- Live briefing on goals and team context.
- Veto right — we swap the trainer for free if needed.
Content tailored to your context
No recycled slides. The syllabus is reworked from your real cases: tools, constraints, vocabulary, ongoing projects.
- Hands-on cases drawn from your stack and projects.
- Program co-written then validated by your team.
Continuous quality follow-up
Live evaluations, 30/90/180-day check-ins and a consolidation plan. If the impact misses the mark, we rework it.
- NPS, knowledge quizzes and skills self-assessment.
- Satisfaction guarantee: fully satisfied or free rework.
A simple promise: you don't pay to discover the trainer on day one. Everything is validated upfront, by you.
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FAQ
Frequently asked questions
How much does the TensorFlow Training - Optimizing and Deploying Advanced DL Models training cost?+
The individual price is $4,620 (USD). A detailed quote is sent within one business day.
How long is the TensorFlow Training - Optimizing and Deploying Advanced DL Models training?+
The training lasts 4 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Mastery of Python, solid knowledge of basic TensorFlow, supervised/unsupervised machine learning, linear algebra, and frameworks like Keras.
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
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