Training MLflow - Master Reproducible ML Tracking

$3,465

  • 3 days
  • Onsite
  • 10 people max.
Ref: UWQ728
10 people max.
$3,465
In-company rate: from $6,290 for the whole team
On-site and certification exam on quote
3 days
Onsite

Don't let this gap widen

Why this program matters

  • Without MLflow, 70% of ML projects fail due to lack of reproducibility, according to Gartner.

  • Lose 15-20h per week on manual tracking via scattered Jupyter notebooks, struggle to compare your 50+ monthly runs, deploy unstable models causing 30% errors in production.

  • Avoid project delays, team frustrations, hidden costs of 10k€/year in rework.

  • With this training, track everything in 5 min, deploy in 1 click, boost ML ROI x3 from the first month.

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

25,807 programs · 25 countries

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

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 and Microsoft.

MLflow training objectives

  • Discover the key features of MLflow for ML
  • Install and configure MLflow in a Python environment
  • Track ML experiments and metrics in real-time
  • Manage models, artifacts, and versions with MLflow
  • Deploy ML models via MLflow Serving
  • Integrate MLflow into a complete MLOps workflow

MLflow curriculum: 3 modules over 3 days

Module 1 : Introduction to MLflow: installation and basic tracking (Python, MLflow UI, initial exercises)

Dive into MLflow by installing the tool via pip, launch your first ML experiment tracking with mlflow.start_run(), explore the UI interface to visualize metrics and parameters, complete practical exercises on simple datasets like Iris, generate your first automated logs, and produce a deliverable experiment report to observe immediate reproducibility of your runs.

Module 2 : Advanced management: MLflow models and artifacts (logging, registry, real-world cases)

Advance to expert management by logging models and artifacts with mlflow.log_model(), configure the Model Registry to version your best models, apply to real-world cases like customer churn prediction, chain collaborative exercises in small groups, compare runs via the UI, and create a deployable model registry that boosts your productivity from the next day.

Module 3 : Deployment and integration: MLOps with MLflow (serving, pipelines, final project)

Master deployment by launching MLflow Serving to expose your models via REST API, integrate MLflow into pipelines with MLflow Projects, simulate an end-to-end workflow on a real Kaggle project, test predictions live, optimize hyperparameters via automated tracking, and finalize with a deliverable: a deployed model ready to scale in production.

How MLflow skills are assessed

  • Continuous assessment through practical exercises, interactive quizzes, and a final MLflow project delivery.

How the MLflow course is taught

  • 30/70 theory/practice alternation, concrete projects, individualized feedback.

MLflow training: access and practical arrangements

The Training MLflow - Master Reproducible ML Tracking 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 - Master Reproducible ML Tracking 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 - Master Reproducible ML Tracking 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 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: frequently asked questions

Which tools are used in MLflow training?+
Hands-on work runs on python, MLflow UI, initial exercises, logging, registry and real-world cases. Those tools come from the MLflow program itself, delivered on site over 3 days, so participants practise on the stack they will use at work.
What can participants do after MLflow training?+
They can discover the key features of MLflow for ML, install and configure MLflow in a Python environment and track ML experiments and metrics in real-time. Those are the stated objectives of the MLflow program at Beginner level, checked by the end-of-session assessment.
What does the MLflow program cover, day by day?+
Day 1: Introduction to MLflow: installation and basic tracking (Python, MLflow UI, initial exercises). Day 2: Advanced management: MLflow models and artifacts (logging, registry, real-world cases). Day 3: Deployment and integration: MLOps with MLflow (serving, pipelines, final project). The MLflow program runs over 3 days, delivered on site.
How is MLflow training assessed?+
Assessment relies on continuous assessment through practical exercises, interactive quizzes, and a final MLflow project delivery. Learni issues a completion certificate together with the individual assessment report at the end of the MLflow program, which an L&D team can file as evidence.
How much does MLflow training cost per participant?+
MLflow training costs $3,465 per participant. For an in-company team, the flat rate starts at $6,290 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 training take?+
3 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 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 training?+
Python basics, machine learning fundamentals.
Is a certificate issued after MLflow 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.
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