Training TinyML Embedded - Sensor Inference for Machines

$4,620

  • 4 days
  • Remote
  • 10 people max.
Ref: JXL513
10 people max.
$4,620
In-company rate: from $7,865 for the whole team
On-site and certification exam on quote
4 days
Remote

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.

Célian Lebacle, Learni trainer · Web & mobile expert
Célian Lebacle

Learni trainer · Web & mobile 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

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

TinyML Embedded training objectives

  • Master extraction of vibratory descriptors computable on microcontrollers under memory constraints for professional machine monitoring.
  • Train a lightweight state classification model and quantify it to gain operational enterprise skills.
  • Deploy inference on target with an optimized compute kernel to reduce latency and power consumption.
  • Measure real-world power consumption, latency and memory footprint on rotating machines.
  • Decide what stays in the sensor and what is sent to the server to optimize autonomy.
  • Validate every diagnosis against physical teardown or independent measurement using normative references.

Program

TinyML Embedded curriculum: 4 modules over 4 days

Module 1 : Acquisition and descriptor extraction (Arduino, MEMS sensors)

Participants connect accelerometers to real rotating machines exhibiting imbalance, misalignment or degraded bearings. They implement feature extraction algorithms under RAM and Flash constraints, test multiple time windows and compare resulting memory footprints. Industrial case studies and first deliverables of optimized code.

Module 2 : Model training and quantization (TensorFlow Lite Micro)

Creation of labeled datasets from introduced defects, training of lightweight classification models, post-training quantization and accuracy-loss analysis. Each participant measures the impact on model size and inference performance before deployment.

Module 3 : Deployment and RTOS optimization (FreeRTOS, CMSIS-DSP)

Integration of the quantized model into FreeRTOS firmware, use of optimized CMSIS-DSP kernels, measurement of latency and power on real target. Task priority tuning and validation that chosen thresholds rely on machine history or recognized standards.

Module 4 : Field validation and inference strategy (MQTT, edge vs cloud)

Confrontation of predictions with physical machine teardown, battery autonomy calculation over multiple years, decision on data to upload. Final deliverable: complete sensor inference architecture and energy performance report.

How TinyML Embedded skills are assessed

  • Daily technical quiz on covered concepts
  • Capstone project evaluated at course end
  • Validation grid for power consumption and latency measurements

How the TinyML Embedded course is taught

  • Hands-on workshops on real machines
  • Code reviews and personalized feedback
  • Technical documentation provided
  • Access to the development environment

TinyML Embedded training: access and practical arrangements

The Training TinyML Embedded - Sensor Inference for Machines 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 TinyML Embedded - Sensor Inference for Machines 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 TinyML Embedded - Sensor Inference for Machines 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 TinyML Embedded 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.

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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

TinyML Embedded: frequently asked questions

Which tools are used in TinyML Embedded training?+
Hands-on work runs on arduino, MEMS sensors, tensorFlow Lite Micro, freeRTOS, CMSIS-DSP and MQTT. Those tools come from the TinyML Embedded program itself, delivered remotely over 4 days, so participants practise on the stack they will use at work.
What can participants do after TinyML Embedded training?+
They can master extraction of vibratory descriptors computable on microcontrollers under memory constraints for professional machine monitoring, train a lightweight state classification model and quantify it to gain operational enterprise skills and deploy inference on target with an optimized compute kernel to reduce latency and power consumption. Those are the stated objectives of the TinyML Embedded program at Advanced level, checked by the end-of-session assessment.
What does the TinyML Embedded program cover, day by day?+
Day 1: Acquisition and descriptor extraction (Arduino, MEMS sensors). Day 2: Model training and quantization (TensorFlow Lite Micro). Day 3: Deployment and RTOS optimization (FreeRTOS, CMSIS-DSP). Day 4: Field validation and inference strategy (MQTT, edge vs cloud). The TinyML Embedded program runs over 4 days, delivered remotely.
How is TinyML Embedded training assessed?+
Assessment relies on daily technical quiz on covered concepts, capstone project evaluated at course end and validation grid for power consumption and latency measurements. Learni issues a completion certificate together with the individual assessment report at the end of the TinyML Embedded program, which an L&D team can file as evidence.
How much does TinyML Embedded training cost per participant?+
TinyML Embedded training costs $4,620 per participant. For an in-company team, the flat rate starts at $7,865 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 TinyML Embedded 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 TinyML Embedded 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 TinyML Embedded training?+
Confirmed experience in C/C++ programming on microcontrollers, signal processing concepts and initial projects with MEMS sensors.
Is a certificate issued after TinyML Embedded 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 TinyML Embedded 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 TinyML Embedded learning path.

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

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