Training MLflow Advanced - Deploy ML Models in Production

Specialist course · Minimum level required

$3,150

  • 2 days
  • Onsite
  • 10 people max.
Ref: MKY956
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 MLflow mastery, 70% of ML projects fail in production according to Gartner, wasting 50k€/project in engineer time and rework.

  • Non-reproducible models cause 40% more frequent errors, x3 deployment delays, revenue losses up to 20% from faulty AI.

  • Avoid data/ops silos, critical downtime, missed audits: train for 5x ML ROI in 6 months, secure scaling and GDPR compliance.

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

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

MLflow Advanced training objectives

  • Configure advanced tracking and scalable MLflow servers.
  • Automate logging of metrics, parameters, and artifacts.
  • Deploy models to production via MLflow Model Registry.
  • Manage model versions and pipeline reproducibility.
  • Integrate MLflow with CI/CD, Kubernetes, and cloud providers.
  • Analyze performance and optimize MLOps workflows.
  • Secure MLflow pipelines in enterprise environments.

Program

MLflow Advanced curriculum: 2 modules over 2 days

Module 1 : Advanced Tracking and Experiment Management: MLflow UI, auto logging, reports (PostgreSQL, MinIO)

Install and configure MLflow Tracking Server with robust backend, practice automatic logging of metrics/hyperparameters via PyTorch/TensorFlow exercises, explore interactive UI to compare runs, generate custom reports on real optimization cases, integrate Git artifacts, everything to scale your ML experiments in a dynamic team.

Module 2 : Production Deployment and MLOps: Model Registry, serving, CI/CD (Kubernetes, Docker)

Master Model Registry for secure versioning, deploy models via MLflow Serving on Kubernetes, simulate CI/CD pipelines with GitHub Actions, test A/B testing and drift monitoring, solve concrete production cases like GPU scaling, deliverables: enterprise-ready deployable scripts, boost your ML workflows independently.

How MLflow Advanced skills are assessed

  • Interactive quizzes, practical exercises, final MLflow deployment project, certifying feedback.

How the MLflow Advanced course is taught

  • 70% hands-on practice, 30% applied theory, real business cases.

MLflow Advanced training: access and practical arrangements

The Training MLflow Advanced - Deploy ML Models 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 MLflow Advanced - Deploy ML Models 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 MLflow Advanced - Deploy ML Models 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 MLflow Advanced 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

MLflow Advanced: frequently asked questions

Which tools are used in MLflow Advanced training?+
Hands-on work runs on postgreSQL, minIO, kubernetes and docker. Those tools come from the MLflow Advanced program itself, delivered on site over 2 days, so participants practise on the stack they will use at work.
What can participants do after MLflow Advanced training?+
They can configure advanced tracking and scalable MLflow servers, automate logging of metrics, parameters, and artifacts and deploy models to production via MLflow Model Registry. Those are the stated objectives of the MLflow Advanced program at Advanced level, checked by the end-of-session assessment.
What does the MLflow Advanced program cover, day by day?+
Day 1: Advanced Tracking and Experiment Management: MLflow UI, auto logging, reports (PostgreSQL, MinIO). Day 2: Production Deployment and MLOps: Model Registry, serving, CI/CD (Kubernetes, Docker). The MLflow Advanced program runs over 2 days, delivered on site.
How is MLflow Advanced training assessed?+
Assessment relies on interactive quizzes, practical exercises, final MLflow deployment project, certifying feedback. Learni issues a completion certificate together with the individual assessment report at the end of the MLflow Advanced program, which an L&D team can file as evidence.
How much does MLflow Advanced training cost per participant?+
MLflow Advanced 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 MLflow Advanced 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 MLflow Advanced 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 MLflow Advanced training?+
Proficiency in Python, ML frameworks (TensorFlow/PyTorch), beginner MLflow, Docker, Kubernetes basics.
Is a certificate issued after MLflow Advanced 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 MLflow Advanced 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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