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Training MLflow Advanced - Master MLOps in Production

Ref: BWO430
10 people max.
$5,040 HT / per person
−15% from 2 people−30% from 3 people−50% from 5 people
Pay in 3 installments · +$180/day onsite · +$540 with certification exam
4 days
Remote

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Learning objectives

  • Configure a scalable MLflow server with custom UI
  • Implement advanced experimental tracking and custom metrics
  • Manage the Model Registry for versioning and staging
  • Automate ML pipelines with MLflow and CI/CD
  • Deploy models in production via MLflow serving
  • Optimize performance and scaling with MLflow plugins
  • Analyze and reproduce large-scale experiments

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 advanced MLflow mastery, 70% of ML projects fail in production according to Gartner, wasting 2-3 months of R&D per non-reproducible model.

  • Manual tracking multiplies errors by 40%, doubling IT costs.

  • Non-versioned models cause 25% longer downtimes, losing 100k€/year in revenue.

  • Non-automated pipelines hinder scaling, blocking 50% of data innovations.

  • Risk obsolescence against MLOps-ready competitors?

  • Invest 28h for 10x ROI on your secure and scalable ML deployments.

Allan Busi
Allan Busi

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Advanced Tracking: MLflow Experiments and Custom Logging (Python, UI)

Dive into configuring an MLflow server, log parameters, metrics and artifacts via Python API, create interactive dashboards with MLflow UI, perform exercises on real datasets to compare runs, generate automated reports, while integrating TensorFlow for smooth and reproducible tracking that boosts your ML iterations.

Module 2Model Registry: Model Versioning and Governance (MLflow Models)

Master the MLflow Model Registry, version PyTorch/TensorFlow models, manage stages (Staging, Production), annotate with metadata, test automated transitions, apply practical exercises on business cases, produce deliverables like secure registries ready for audit, transform your workflows into reliable and scalable processes.

Module 3Pipelines and CI/CD: MLflow Automation (GitHub Actions, Airflow)

Integrate MLflow into CI/CD pipelines, automate tracking and registry with GitHub Actions/Airflow, code scripts for automated triggers, simulate end-to-end deployments on Kubernetes, solve complex exercises with large data, generate deliverables like Dockerized workflows, accelerate your ML deployments by 50% right away.

Module 4Deployment and Scaling: MLflow Serving, Monitoring (Advanced Plugins)

Deploy models via MLflow Serving REST/gRPC, integrate Prometheus monitoring, scale with Kubernetes plugins, optimize latency on real loads, conduct final project on concrete business case, produce performance dashboards and impact reports, master best practices for robust production, propel your ML projects into profitable operations.

Evaluation method

  • Technical quizzes, practical exercises, final MLflow project, peer feedback

Learning method

  • 70% hands-on coding, 30% applied theory, real business cases

Methods, materials and delivery

The Training MLflow Advanced - Master MLOps in Production 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 Advanced - Master MLOps in Production 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 Advanced - Master MLOps in Production 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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