Training TensorFlow - Mastering MLOps Deployment in the Enterprise

Specialist course · Minimum level required

$6,300

  • 4 days
  • Onsite
  • 10 people max.
Ref: XKX449
10 people max.
$6,300
In-company rate: from $10,710 for the whole teamExpertise rate: $1,575 per day, reflecting the difficulty level
On-site and certification exam on quote
4 days
Onsite

Don't let this gap widen

Why this program matters

  • Companies that fail to master advanced TensorFlow lose 40% of their ML projects in production according to recent studies.

  • Without strong MLOps skills, deployment timelines stretch and costs spiral out of control.

  • Professionals stall in their careers against certified, operational competitors.

  • Acting now prevents these losses and secures your position in the company.

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

41,183 programs · 25 countries

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

Every program built with a dedicated engineer

  • OpenAI
  • Anthropic
  • Mistral AI
  • NVIDIA
  • Google
  • Amazon Web Services
  • Microsoft Dynamics 365
  • Salesforce
  • ServiceNow
  • SAP
  • Oracle
  • Cisco
  • Fortinet
  • Palo Alto Networks
  • Aruba
  • VMware
  • Red Hat
  • Databricks
  • Snowflake
  • OVHcloud
  • Notion
  • AMD
  • Apple

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

TensorFlow training objectives

  • Master advanced TensorFlow architectures for complex professional projects.
  • Develop certified MLOps skills with TensorFlow Extended in an enterprise setting.
  • Design scalable and monitored production pipelines for your models.
  • Implement optimization and continuous deployment strategies in real-world conditions.
  • Optimize TensorFlow model performance while ensuring robustness.
  • Integrate PyTorch and TensorFlow into unified enterprise MLOps workflows.

Program

TensorFlow curriculum: 4 modules over 4 days

Module 1 : Theme: Advanced TensorFlow and Complex Architectures (Keras, Custom Models, GPU Optimization)

Participants explore advanced TensorFlow features to build complex models. They practice creating custom layers, using distribution strategies, and GPU optimization. Hands-on exercises with enterprise datasets help master best practices for code structuring. Each learner delivers an optimized model by the end of the day.

Module 2 : Theme: TensorFlow Extended for MLOps Pipelines (TFX, Components, Orchestration)

This day focuses on building complete pipelines with TensorFlow Extended. Learners configure TFX components for data ingestion, validation, and transformation. They deploy a pipeline in a simulated enterprise environment. Practical cases illustrate artifact tracking and training reproducibility.

Module 3 : Theme: TensorFlow Deployment in Production (TensorFlow Serving, Docker, Monitoring)

Participants deploy TensorFlow models using TensorFlow Serving and optimized Docker containers. They integrate monitoring and auto-scaling solutions tailored to enterprise needs. Real-world production scenarios test latency and reliability. A deployed service deliverable concludes the session.

Module 4 : Theme: Advanced Integration of TensorFlow and PyTorch in MLOps (CI/CD, A/B Testing)

The final day covers interoperability between TensorFlow and PyTorch within an MLOps ecosystem. Learners implement CI/CD workflows with automated testing and A/B testing strategies. Hands-on workshops on enterprise projects strengthen model governance skills. Each participant presents their finalized pipeline.

How TensorFlow skills are assessed

  • Evaluated practical project of a complete pipeline
  • Daily technical quizzes on key concepts
  • Oral presentation of the final deliverable

How the TensorFlow course is taught

  • Practical workshops on real business cases
  • Guided pair work with expert feedback
  • Live demonstrations of MLOps tools
  • Access to a post-training resource repository

TensorFlow training: access and practical arrangements

The Training TensorFlow - Mastering MLOps Deployment in the Enterprise 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 - Mastering MLOps Deployment in the Enterprise 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 - Mastering MLOps Deployment in the Enterprise 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 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: frequently asked questions

Which tools are used in TensorFlow training?+
Hands-on work runs on keras, custom Models, GPU Optimization, TFX, components and orchestration. Those tools come from the TensorFlow program itself, delivered on site over 4 days, so participants practise on the stack they will use at work.
What can participants do after TensorFlow training?+
They can master advanced TensorFlow architectures for complex professional projects, develop certified MLOps skills with TensorFlow Extended in an enterprise setting and design scalable and monitored production pipelines for your models. Those are the stated objectives of the TensorFlow program at Advanced level, checked by the end-of-session assessment.
What does the TensorFlow program cover, day by day?+
Day 1: Theme: Advanced TensorFlow and Complex Architectures (Keras, Custom Models, GPU Optimization). Day 2: Theme: TensorFlow Extended for MLOps Pipelines (TFX, Components, Orchestration). Day 3: Theme: TensorFlow Deployment in Production (TensorFlow Serving, Docker, Monitoring). Day 4: Theme: Advanced Integration of TensorFlow and PyTorch in MLOps (CI/CD, A/B Testing). The TensorFlow program runs over 4 days, delivered on site.
How is TensorFlow training assessed?+
Assessment relies on evaluated practical project of a complete pipeline, daily technical quizzes on key concepts and oral presentation of the final deliverable. Learni issues a completion certificate together with the individual assessment report at the end of the TensorFlow program, which an L&D team can file as evidence.
How much does TensorFlow training cost per participant?+
TensorFlow training costs $6,300 per participant. For an in-company team, the flat rate starts at $10,710 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 training take?+
4 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 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 training?+
Proficiency in Python, hands-on experience in machine learning with TensorFlow or PyTorch, solid foundations in Docker and Git.
Is a certificate issued after TensorFlow 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 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.
Your professional training, anywhere

Let's build your TensorFlow learning path.

30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.

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