Training TensorFlow Expert - Deploy AI in Production

Very demanding course · Expertise required

$3,150

  • 2 days
  • Onsite
  • 10 people max.
Ref: FTT108
10 people max.
$3,150
In-company rate: from $5,723 for the whole teamExpertise rate: $1,575 per day, reflecting the difficulty level
On-site and certification exam on quote
2 days
Onsite

Don't let this gap widen

Why this program matters

  • Without advanced TensorFlow expertise, your models underperform: inference 3x slower, cloud costs inflated by 50% (IDC 2023), 65% deployment failures (Gartner), estimated business losses 200k€/year per project.

  • Competitors scale in hours, you stagnate for weeks.

  • Non-optimized models crash in production, ruining AI reputation.

  • Invest 14h, gain x8 efficiency, avoid 80% downtime, immediate ROI on first deployments.

Allan BUSI, Learni trainer · AI expert
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction
Flag of the United StatesBuilt for US teams
Payment by wire transferEnhanced confidentialityPerfect for companies with 50 to 500 employeesScheduling to fit any time zone

Trusted by — schools, companies and industrial groups

41,182 programs · 25 countries

  • Aptar
  • ArcelorMittal
  • Equans
  • EDF
  • Ubisoft
  • Microsoft
  • Atos

Every program built with a dedicated engineer

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Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.

TensorFlow Expert training objectives

  • Optimize TensorFlow models for maximum performance
  • Deploy ML pipelines with TensorFlow Extended (TFX)
  • Master advanced profiling and debugging in production
  • Implement horizontal scaling via Kubernetes
  • Analyze inference metrics for optimal ROI
  • Integrate TensorFlow with secure hybrid clouds

Program

TensorFlow Expert curriculum: 2 modules over 2 days

Module 1 : Advanced Optimization: Profiling, Quantization, Pruning (TensorRT, real datasets)

Identify bottlenecks with TensorFlow Profiler on your CNN/RNN models, apply quantization and pruning to accelerate inference x5, perform practical exercises on ImageNet/Text datasets, test TensorRT for edge deployment, produce production-ready optimized deliverables that slash GPU costs by 60%, experience the expertise that transforms your AI projects.

Module 2 : Production Deployment: TFX Pipelines, Serving, Kubernetes Scaling

Build end-to-end pipelines with TFX for data validation and serving, deploy models via TensorFlow Serving on Kubernetes, simulate real loads for auto-scaling, integrate Prometheus/Grafana monitoring, generate high-availability Dockerized deliverables, master A/B testing for inference, boost your deployments with no downtime, become an operational AI leader.

How TensorFlow Expert skills are assessed

  • Interactive quizzes, evaluated final projects, certifying feedback

How the TensorFlow Expert course is taught

  • Real projects 70% of time, live coaching, unlimited replays

TensorFlow Expert training: access and practical arrangements

The Training TensorFlow Expert - Deploy AI in Production 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 Training TensorFlow Expert - Deploy AI in Production 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 Training TensorFlow Expert - Deploy AI in Production 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.

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.

Our method

How Learni guarantees the quality of your TensorFlow Expert training

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.

Step 1

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.
Step 2

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.
Step 3

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.
Step 4

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.

A child walking to school with a backpack
Our social commitment

A school kit donated to a child for every training booked

To fight inequalities in access to education, Learni donates a complete school kit to a child in need for every training booked. You build your skills, a child heads back to school.

  • Backpack, notebooks and essential supplies
  • Distributed through our partner charities
  • Included, at no extra cost to you
Enroll and offer a kit
FAQ

TensorFlow Expert: frequently asked questions

Which tools are used in TensorFlow Expert training?+
Hands-on work runs on tensorRT and real datasets. Those tools come from the TensorFlow Expert program itself, delivered on site over 2 days, so participants practise on the stack they will use at work.
What can participants do after TensorFlow Expert training?+
They can optimize TensorFlow models for maximum performance, deploy ML pipelines with TensorFlow Extended (TFX) and master advanced profiling and debugging in production. Those are the stated objectives of the TensorFlow Expert program at Expert level, checked by the end-of-session assessment.
What does the TensorFlow Expert program cover, day by day?+
Day 1: Advanced Optimization: Profiling, Quantization, Pruning (TensorRT, real datasets). Day 2: Production Deployment: TFX Pipelines, Serving, Kubernetes Scaling. The TensorFlow Expert program runs over 2 days, delivered on site.
How is TensorFlow Expert training assessed?+
Assessment relies on interactive quizzes, evaluated final projects, certifying feedback. Learni issues a completion certificate together with the individual assessment report at the end of the TensorFlow Expert program, which an L&D team can file as evidence.
How much does TensorFlow Expert training cost per participant?+
TensorFlow Expert training costs $3,150 per participant. For an in-company team, the flat rate starts at $5,723 for the whole group, up to 8 people. The detailed quote, sent within one business day, adjusts the amount to the format and how far the syllabus is tailored to your team.
How long does TensorFlow Expert training take?+
2 days, available live online (US time zones) or on-site at your offices. Half-day tracks spread over several weeks are possible so the team stays operational.
Who pays for TensorFlow Expert training?+
Most US teams pay directly through their company L&D or training budget. Learni invoices in US dollars and accepts bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
What do participants need to know before TensorFlow Expert training?+
Advanced Python mastery, intermediate TensorFlow/Keras, ML deployment experience, Docker/Kubernetes.
Is a certificate issued after TensorFlow Expert training?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report. Learni is a certified training provider and prepares for third-party certifications, but does not award them itself.
Does Learni provide the equipment for TensorFlow Expert training?+
No. A computer and a stable internet connection are required for each participant. Learni provides the learning platform, the trainer and all course materials.
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