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.
Training MLflow - Automate ML Tracking and Deployment
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
Learning 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
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, 40% of data scientists' time is wasted on manual tracking, leading to non-reproducible models and 25% errors in production.
Imagine deploying a model that drifts silently, causing 15% revenue losses on a faulty churn model.
Teams waste 200 hours/year on chaotic versioning, delaying launches by 2 months.
Avoid these pitfalls: low reproducibility triples rework costs, internal audits fail without traceability, competitors with MLOps outpace you by 30% in iteration speed.
Invest now to secure multiplied ML ROI.
Program
Module 1MLflow 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 2MLflow 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 3MLflow 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 4MLflow 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 5Advanced 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.
Evaluation method
- Daily interactive quizzes, evaluated practical projects, final certification with MCQ + real case study.
Learning method
- 70% hands-on with real cases, 30% applied theory, pair exercises.
Methods, materials and delivery
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.
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 - Automate ML Tracking and Deployment training cost?+
The price is $5,775 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training MLflow - Automate ML Tracking and Deployment training?+
The training lasts 5 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?+
Proficiency in Python, basics in scikit-learn or TensorFlow, knowledge of Machine Learning.
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.
Your professional training, anywhere
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