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 - Automate ML Tracking and Deployment
Trusted by — schools, companies and industrial groups
41,258 programs · 25 countries
Every program built with a dedicated engineer
Companies: Aptar, ArcelorMittal, Equans, EDF, Ubisoft, Microsoft and Atos.
MLflow training objectives
- Install and configure MLflow in a production environment
- Track experiments and metrics for optimal reproducibility
- Manage the Model Registry to version your ML models
- Deploy models via MLflow Serving and cloud integrations
- Automate CI/CD pipelines with MLflow Projects
- Analyze performance via the MLflow UI and advanced APIs
- Apply best practices to scale your ML projects
Program
MLflow curriculum: 5 modules over 5 days
Module 1 : MLflow Installation: Tool and UI Configuration (Python, Docker)
Dive into quick MLflow installation, configure tracking servers and intuitive UI, test logging of first experiments on real datasets like Iris, create your first run with hyperparameters, export artifacts via practical exercises, leave with a ready-to-scale ML project environment in the blink of an eye.
Module 2 : MLflow Experiment Tracking: Metrics, Parameters, Artifacts
Master automatic logging of metrics, parameters, and models, compare runs via interactive dashboards, optimize hyperparameters on real cases like binary classification, generate custom charts, integrate MLflow with Jupyter for a seamless workflow, boost your productivity from the first collaborative exercise.
Module 3 : MLflow Model Registry: Model Versioning and Staging
Manage model lifecycles in the Registry, transition from Staging to Production, annotate versions with rich metadata, test automated transitions on real projects, integrate with Git for full traceability, produce audit-ready deliverables, transform chaos into professional ML governance.
Module 4 : MLflow Deployment: Serving, Kubernetes/AWS Integrations
Deploy models as REST APIs via MLflow Serving, scale with Docker and Kubernetes, monitor drift and performance live, integrate with AWS SageMaker or GCP AI Platform, simulate real loads in workshops, generate production-ready endpoints, launch your ML services without frustrating downtime.
Module 5 : Advanced MLflow Pipelines: Projects, MLOps Best Practices
Automate end-to-end pipelines with MLflow Projects, CI/CD via GitHub Actions, apply MLOps to real enterprise cases like churn prediction, customize plugins and callbacks, review projects in pairs, conclude with a deliverable portfolio, integrate MLflow into your daily stack for immediate gains.
How MLflow skills are assessed
- Daily interactive quizzes, evaluated practical projects, final certification with MCQ + real case study.
How the MLflow course is taught
- 70% hands-on with real cases, 30% applied theory, pair exercises.
MLflow training: access and practical arrangements
The Training MLflow - Automate ML Tracking and Deployment 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 - Automate ML Tracking and Deployment 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 - Automate ML Tracking and Deployment 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.
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
MLflow: frequently asked questions
Which tools are used in MLflow training?+
Hands-on work runs on python and docker. 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 production environment, track experiments and metrics for optimal reproducibility and manage the Model Registry to version your ML models. Those are the stated objectives of the MLflow program at Intermediate level, checked by the end-of-session assessment.
What does the MLflow program cover, day by day?+
Day 1: MLflow Installation: Tool and UI Configuration (Python, Docker). Day 2: MLflow Experiment Tracking: Metrics, Parameters, Artifacts. Day 3: MLflow Model Registry: Model Versioning and Staging. Day 4: MLflow Deployment: Serving, Kubernetes/AWS Integrations. Day 5: Advanced MLflow Pipelines: Projects, MLOps Best Practices. The MLflow program runs over 5 days, delivered on site.
How is MLflow training assessed?+
Assessment relies on daily interactive quizzes, evaluated practical projects, final certification with MCQ + real case study. 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 $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?+
Proficiency in Python, basics in scikit-learn or TensorFlow, knowledge of Machine Learning.
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.
Does Learni provide the equipment for MLflow 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.
Your professional training, anywhere
Let's build your MLflow learning path.
30 minutes with a learning advisor. No commitment. No sales pitch dressed up as a demo.
Reply within 24h · Vetted experts · USD invoicing · W-9 available
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