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.
Training MLflow - Manage your ML experiments effectively
Trusted by — schools, companies and industrial groups
25,751 programs · 25 countries
Every program built with a dedicated engineer
Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft and Microsoft.
Learning objectives
- Install and configure MLflow in a local environment
- Create and organize ML tracking experiments
- Log parameters, metrics, and artifacts automatically
- Use the UI interface to visualize and compare runs
- Deploy ML models with MLflow Model Registry
- Integrate MLflow into an end-to-end workflow
- Apply best practices for reproducible projects
Program
Module 1Introduction to MLflow: installation and first runs (CLI tools, UI Tracking Server)
Discover MLflow by installing the tool via pip, configure your first local tracking server, run simple Python scripts to log basic parameters, perform practical exercises on Iris datasets, visualize your runs in the intuitive UI, and produce your first automated reports that transform your trials into actionable insights.
Module 2Advanced Tracking: logging metrics and artifacts (MLflow Tracking API, autologging)
Dive into logging with the MLflow API, record precise metrics like accuracy and loss on Scikit-learn models, upload artifacts like graphs and CSV files, enable autologging for TensorFlow, perform collaborative exercises on real classification cases, and generate dynamic dashboards that accelerate your iterations.
Module 3Visualization and Comparison: MLflow UI and Experiments (run search, sorting)
Master the MLflow UI to navigate experiments, compare parallel runs via interactive graphs, filter by key metrics on real projects like churn prediction, perform advanced search exercises, export visualizations as PNG, and learn to identify the best models in a few clicks to boost your productivity.
Module 4Model Management: MLflow Models and Registry (packaging, staging)
Move to model management by packaging your trainings with mlflow.pyfunc, register them in the Model Registry, test staging and production via REST API, deploy on concrete regression cases, perform A/B testing exercises, and validate deployable deliverables that secure your ML pipelines in the enterprise.
Module 5Integrated Projects and Best Practices: end-to-end workflows (Git integration, CI/CD)
Synthesize in a complete project a reproducible ML pipeline with MLflow, integrate Git for versioning, simulate CI/CD with Docker, optimize via best practices like nested runs, collaborate in a team on a real business case, produce a personal portfolio, and leave with ready-to-scale templates for your future ML projects.
Evaluation method
- Continuous formative evaluation through practical exercises
- MCQ at the end of the module
- Final project with oral presentation
- Internal MLflow certification quiz
Learning method
- 30% theory, 70% hands-on alternation
- Individual and pair exercises
- Real business case studies
- Post-training video support
Methods, materials and delivery
The Training MLflow - Manage your ML experiments effectively 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 - Manage your ML experiments effectively 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 - Manage your ML experiments effectively 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
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.
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
FAQ
Frequently asked questions
Which tools are used in MLflow training?+
Hands-on work runs on CLI tools, UI Tracking Server, MLflow Tracking API, autologging, run search and sorting. Those tools come from the MLflow program itself, delivered on site over 5 days, so participants practise on the stack they will use at work.
What can participants do after MLflow training?+
They can install and configure MLflow in a local environment, create and organize ML tracking experiments and log parameters, metrics, and artifacts automatically. 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 first runs (CLI tools, UI Tracking Server). Day 2: Advanced Tracking: logging metrics and artifacts (MLflow Tracking API, autologging). Day 3: Visualization and Comparison: MLflow UI and Experiments (run search, sorting). Day 4: Model Management: MLflow Models and Registry (packaging, staging). Day 5: Integrated Projects and Best Practices: end-to-end workflows (Git integration, CI/CD). The MLflow program runs over 5 days, delivered on site.
How is MLflow training assessed?+
Assessment relies on continuous formative evaluation through practical exercises, MCQ at the end of the module and final project with oral presentation. 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 is priced on quote. The amount depends on headcount, format and how far the syllabus is tailored to your team. A detailed quote is sent within one business day, and team pricing covers the whole group from the second participant.MLflow 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 MLflow 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 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, introductory Machine Learning concepts, development environment installed.
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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