Training Blended Learning - Optimising Mixed ML Learning

$5,775

  • 5 days
  • Onsite
  • 10 people max.
Ref: LDE930
10 people max.
$5,775
In-company rate: from $10,395 for the whole team
On-site and certification exam on quote
5 days
Onsite

Don't let this gap widen

Why this program matters

  • Without mastered blended learning, 70% of ML trainings fail to transfer skills to production, causing 25% productivity losses according to Gartner.

  • MLOps teams stagnate, with project timelines multiplied by 3 and model revision costs inflated by 40%.

  • Career risk: rapid obsolescence against AI competition.

  • Act for immediate ROI returns through effective mixed learning.

Youcef TAHRI, Learni trainer · Management expert
Youcef TAHRI

Learni trainer · Management 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

41,140 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.

Blended Learning training objectives

  • Master blended learning to effectively train Machine Learning skills in the enterprise
  • Develop mixed pathways integrating TensorFlow and PyTorch into certified training programs
  • Design MLOps modules adapted to professional hybrid learning
  • Implement blended tools to boost AI team upskilling
  • Optimise learner engagement through blended practices in an ML context
  • Evaluate the impact of blended training on enterprise MLOps performance

Program

Blended Learning curriculum: 5 modules over 5 days

Module 1 : Blended Learning Fundamentals: Principles and ML Integration (TensorFlow Intro)

Discover the basics of blended learning applied to Machine Learning, explore synchronous/asynchronous modalities with TensorFlow for practical exercises on simple datasets, design your first mixed pathway via LMS platforms, analyse successful enterprise cases, produce an initial blended plan with concrete deliverables and immediate expert feedback.

Module 2 : Blended Learning Design: PyTorch Modules and Interaction (Collaborative Tools)

Dive into creating PyTorch-specific blended content, integrate interactive videos, adaptive quizzes and live sessions, simulate mixed workshops on neural models, test engagement via gamification, develop reusable resources for MLOps training, generate a module prototype with learner tracking metrics.

Module 3 : Blended Learning Implementation: MLOps Pipelines (Hybrid Deployment)

Learn to deploy MLOps pipelines in blended mode, combine remote coding sessions with asynchronous reviews, practice on TensorFlow Serving and PyTorch Lightning, integrate monitoring tools like MLflow, solve real enterprise cases, produce an operational blended workflow with performance dashboards and impact reports.

Module 4 : Blended Learning Optimisation: Data-Driven ML (Advanced TensorFlow, PyTorch)

Optimise your blended pathways via learning analytics, analyse engagement data with TensorFlow and PyTorch for personalisation, adjust content in real-time based on feedback, explore AI for adaptive learning in MLOps, test A/B on real cohorts, deliver a quantified optimisation plan with projected enterprise ROI.

Module 5 : Evaluation and Scaling Blended Learning: MLOps in Production (Certification)

Finalise with 360° evaluation of ML blended learning, measure acquired skills via TensorFlow/PyTorch projects, scale for enterprise teams with MLOps, prepare Qualiopi certifications, strategic review of pathways, produce a complete portfolio and post-training action plan for immediate enterprise deployment.

How Blended Learning skills are assessed

  • Interactive quizzes and practical TensorFlow/PyTorch projects
  • Continuous assessments via blended MLOps portfolios
  • Qualiopi-certified attestation of acquired skills

How the Blended Learning course is taught

  • Theory applied to Machine Learning with real-world cases
  • Practical exercises on TensorFlow and PyTorch
  • Collaborative remote sessions for MLOps
  • Personalised feedback from experts for optimisation

Blended Learning training: access and practical arrangements

The Training Blended Learning - Optimising Mixed ML Learning 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 Blended Learning - Optimising Mixed ML Learning 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 Blended Learning - Optimising Mixed ML Learning 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 Blended Learning 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

Blended Learning: frequently asked questions

Which tools are used in Blended Learning training?+
Hands-on work runs on tensorFlow Intro, collaborative Tools, hybrid Deployment, advanced TensorFlow, pyTorch and certification. Those tools come from the Blended Learning program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.
What can participants do after Blended Learning training?+
They can master blended learning to effectively train Machine Learning skills in the enterprise, develop mixed pathways integrating TensorFlow and PyTorch into certified training programs and design MLOps modules adapted to professional hybrid learning. Those are the stated objectives of the Blended Learning program at Intermediate level, checked by the end-of-session assessment.
What does the Blended Learning program cover, day by day?+
Day 1: Blended Learning Fundamentals: Principles and ML Integration (TensorFlow Intro). Day 2: Blended Learning Design: PyTorch Modules and Interaction (Collaborative Tools). Day 3: Blended Learning Implementation: MLOps Pipelines (Hybrid Deployment). Day 4: Blended Learning Optimisation: Data-Driven ML (Advanced TensorFlow, PyTorch). Day 5: Evaluation and Scaling Blended Learning: MLOps in Production (Certification). The Blended Learning program runs over 5 days, delivered on site.
How is Blended Learning training assessed?+
Assessment relies on interactive quizzes and practical TensorFlow/PyTorch projects, continuous assessments via blended MLOps portfolios and qualiopi-certified attestation of acquired skills. Learni issues a completion certificate together with the individual assessment report at the end of the Blended Learning program, which an L&D team can file as evidence.
How much does Blended Learning training cost per participant?+
Blended Learning training costs $5,775 per participant. For an in-company team, the flat rate starts at $10,395 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 Blended Learning training take?+
5 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 Blended Learning 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 Blended Learning training?+
Basics in Machine Learning, notions of digital pedagogy, experience in corporate training.
Is a certificate issued after Blended Learning 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 Blended Learning 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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