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Training Azure Machine Learning - Deploying Scalable AI Models

Ref: TGT424
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

  • Master Azure Machine Learning to develop professional, certifying pipelines
  • Prepare and train datasets with AutoML in an enterprise environment
  • Design reproducible and production-optimized ML experiments
  • Deploy scalable AI models via Azure Kubernetes endpoints
  • Implement monitoring and model lifecycle management in a DevOps workflow
  • Optimize costs and performance of Azure ML workloads in a business context
  • Develop certifying skills to integrate AI in the enterprise

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 this upskilling, your team accumulates a technological gap that translates directly into productivity loss.

  • Organizations that don't train their talents on key topics see their competitiveness drop.

  • Every quarter without training is a gap widening with competitors who invest.

  • The cost of inaction quickly exceeds that of well-targeted training.

Fouzi Benzidane
Fouzi Benzidane

Learni Trainer · Expert

73%productivity gap
×3cost of inaction

Program

Module 1Azure Machine Learning Fundamentals: workspace and compute configuration (CLI, Studio)

Installation and setup of a dedicated Azure Machine Learning workspace for professional projects, exploration of the Studio interface for intuitive onboarding, creation of scalable compute instances and clusters, practical exercises on managing virtual environments with conda and pip, launching initial experiments on real enterprise datasets, validation of setups through code review for a solid and secure foundation.

Module 2Data Preparation in Azure Machine Learning: ingestion, cleaning, feature engineering (Databricks, SQL)

Ingestion of large-scale data via Azure Data Factory and Blob Storage directly into Azure Machine Learning, automated cleaning with Python and Spark preprocessing pipelines, advanced feature engineering on structured/unstructured datasets, use of Designer for rapid visual workflows, practical cases on real customer data to detect anomalies, production of training-ready datasets with automatic versioning for enterprise traceability.

Module 3Model Training in Azure Machine Learning: AutoML, custom experiments (hyperparameter tuning)

Launching AutoML experiments for classification and regression on professional datasets, customization of training scripts with scikit-learn and TensorFlow in Jupyter notebooks, hyperparameter tuning via automated sweeps and Bayesian optimization, comparison of metrics (AUC, RMSE) on interactive leaderboards, exercises on business cases like churn prediction, generation of optimal models ready for deployment with integrated SHAP explanations.

Module 4Deployment in Azure Machine Learning: real-time endpoints, batch inference (AKS, ACI)

Deployment of models to secure REST endpoints via Azure Kubernetes Service for scalability, configuration of batch inference on compute clusters for large volumes, integration with API Management for traffic monitoring, end-to-end testing with real enterprise validation data, management of model versions and automatic rollback, practical exercises on a red thread project to simulate production, documentation of deployments for DevOps teams.

Module 5Optimization and MLOps in Azure Machine Learning: monitoring, drift detection, CI/CD (GitHub Actions)

Implementation of advanced monitoring with Application Insights for real-time data drift and model decay detection, automation of MLOps pipelines via Azure DevOps and GitHub Actions, cost optimization with spot instances and auto-scaling, concrete cases on automatic retraining of production models, security and GDPR compliance audits, finalization of the red thread project with a complete report, preparation for certification to showcase enterprise skills.

Evaluation method

  • Certifying quiz on Azure Machine Learning at the end of the training
  • Continuous assessment via practical exercises and notebooks
  • Defense of the red thread project deployed in a live endpoint

Learning method

  • Courses led by an active Microsoft-certified expert trainer
  • Practical exercises on real Azure enterprise cases
  • Progressive red thread project from A to Z in Azure ML
  • Complete course materials and unlimited access to cloud labs

Methods, materials and delivery

The Training Azure Machine Learning - Deploying Scalable AI Models 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 Azure Machine Learning - Deploying Scalable AI Models 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 Azure Machine Learning - Deploying Scalable AI Models 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
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« cool, j'ai appris des trucs »

TomFormation AWS — Cloud Practitioner
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« 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
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« 😊👍 »

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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