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Training MLflow - Master ML Tracking in 5 Days

Ref: FVF590
10 people max.
5250 € HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$170/day onsite · +$500 with certification exam
5 journées
distanciel

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Learning objectives

  • Install and configure MLflow in a local environment
  • Launch tracking experiments to monitor ML runs
  • Log metrics, parameters, and artifacts automatically
  • Manage models via the Model Registry for quick deployment
  • Orchestrate workflows with MLflow Projects and Recipes
  • Deploy models in local and cloud serving

The Learni story

Founded by passionate learning and innovation experts, Learni's mission is to make professional training accessible to everyone, anywhere in the world. Our team operates in major hubs — London, New York, Boston — and internationally, to support talents and organizations in upskilling.

Don't let this gap widen

Why this program matters

  • Without MLflow, 70% of ML projects fail due to lack of reliable tracking, wasting 20-30% of time recreating lost experiments.

  • Imagine relaunching 10 runs manually each week, risking human errors on hyperparameters, outdated model versions in production causing 15% costly false positives.

  • Lose opportunities to agile competitors, with teams frustrated by unmanageable notebooks.

  • Invest 35h to avoid these pitfalls: recover 100h/year, boost ML ROI x3, secure promotions by demonstrating mastery of pro tools.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Introduction to MLflow: installation and first tracking (CLI tools, UI)

Discover MLflow by installing the tool via pip, launch your first ML run with scikit-learn, configure the local tracking server, log your metrics live, explore the intuitive UI to visualize results and artifacts, complete a practical exercise on the Iris dataset to track accuracy and execution time, leave with an operational setup that boosts your productivity from tomorrow.

Module 2Advanced Tracking: metrics, params, and artifacts (MLflow Tracking API)

Dive into the Tracking API to automatically log hyperparameters, ROC curves, and model files, compare multiple runs via the UI, organize experiments into structured projects, apply to a concrete binary classification case with XGBoost, generate automated reports, master tags for efficient filtering and searching, experience the simplicity that transforms your chaotic notebooks into reproducible pipelines.

Module 3Model Management: Model Registry and versioning (MLflow Models)

Register your best models in the centralized Registry, version them with staging and production stages, test transitions via UI, integrate with PyTorch or TensorFlow on a regression case, export to ONNX formats for compatibility, simulate a complete MLOps workflow, produce a team-ready deliverable, feel the power of total control that prevents critical version losses.

Module 4MLflow Projects: orchestration and reproducibility (MLflow Projects)

Structure your MLflow projects with YAML files, execute remote pipelines via CLI, integrate Git for automatic versioning, test on a Kaggle prediction dataset, scale with Docker in one click, collaborate frictionlessly in teams, generate executable deliverables, adopt a method that accelerates your iterations by 50% and makes your code instantly shareable.

Module 5Deployment and best practices: serving and integrations (MLflow Deployments)

Deploy models as REST API serving locally or on AWS cloud, monitor in production with Prometheus, integrate Kubeflow for scaling, review security and performance best practices, finalize with a personal capstone project on churn prediction, receive ready-to-use templates, certify your skills with a concrete portfolio, launch into production confidently and save precious hours each week.

Evaluation method

  • Daily interactive quizzes
  • Complete MLflow final project
  • Self-assessment of skills before/after

Learning method

  • Active pedagogy 70% hands-on
  • Alternating real exercises/theory
  • Post-training video support

Methods, materials and delivery

The Training MLflow - Master ML Tracking in 5 Days program is delivered onsite or remote (blended-learning, e-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 ML Tracking in 5 Days 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 ML Tracking in 5 Days 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

Learni programs are available inter-company and intra-company, onsite or remote. Enrollments are possible up to 48 business hours before the program starts. Our programs are eligible for corporate funding paths. Contact us to discuss your training project and funding options.

Verified reviews

What our learners

4.9 · +100 verified reviews
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
★★★★★

« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
★★★★★

« j'etais perdu au debut mais Ramy Saharaoui m'a pas laché, il a pris le temps. merci vraiment »

Eva CarpentierFormation LLM en Entreprise — Claude, ChatGPT, Mistral
★★★★★

« la formation dev etait intense mais grave bien. merci Anthony Khelil »

NolanDWWM - Développeur Web et Web Mobile
★★★★★

« 😊👍 »

AmbreDWWM - Développement Web & Mobile React
★★★★★

« bien 👍 »

Léo BlanchardFormation AWS — DevOps Engineer Professional
★★★★★

« Allan Busi t'es au top, continue comme ça. formation géniale »

MargotFormation Claude & ChatGPT — Comparatif et Cas d'Usage
Read all reviews
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.

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.

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