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.
1 seat held for the next session
30:00Held for another
Request a call back and an advisor reaches you within 24 business hours, Monday to Saturday.
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning 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
Our social commitment
A school kit donated to a child for every training
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
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.
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.
Program
Module 1Introduction 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 2Advanced 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 3Deployment 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.
Evaluation method
- Continuous assessment through practical exercises, interactive quizzes, and a final MLflow project delivery.
Learning method
- 30/70 theory/practice alternation, concrete projects, individualized feedback.
Methods, materials and delivery
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.
Our method
Training quality, guaranteed at every step
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.
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.
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.
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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FAQ
Frequently asked questions
How much does the Training MLflow - Master Reproducible ML Tracking training cost?+
The individual price is $3,675 (USD). The team / on-site group package is shown on the course page. A detailed quote is sent within one business day.
How long is the Training MLflow - Master Reproducible ML Tracking training?+
The training lasts 3 journées, available live online (US time zones) or on-site at your offices.
How is this training paid for?+
Most US teams pay directly through their company (L&D or training budget). We invoice in US dollars and accept bank transfer (ACH/wire) or card, with volume pricing for teams. A purchase order is welcome.
Are there any prerequisites?+
Python basics, machine learning fundamentals.
Is a certificate delivered at the end?+
Yes. A Learni completion certificate is issued, along with the individual evaluation report.
Does Learni provide the equipment?+
No. A computer and stable internet connection are required for the participant. Learni provides the educational platform, the trainer and all course materials.
On-site & remote
This training across cities
Available on-site and remotely. Pick your city to see the local training center.
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