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 Azure Machine Learning - Deploying Scalable AI Models
Trusted by — schools, companies and industrial groups
Every program built with a dedicated engineer
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
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 mastery of Azure Machine Learning, 75% of enterprise AI projects fail in the deployment phase, wasting up to 200k€ per initiative according to Gartner.
Teams lose 50% more time on manual training without AutoML, exposing them to non-scalable models that drive up compute costs.
In 2024, 68% of data scientists without cloud ML are passed over for senior roles.
Every quarter without Azure skills widens the competitive gap, risking loss of market share to automated, predictive competitors.
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, 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
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 Azure Machine Learning - Deploying Scalable AI Models training cost?+
The price is $5,775 (USD) per participant. A detailed quote is sent within one business day.
How long is the Training Azure Machine Learning - Deploying Scalable AI Models 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, fundamentals of supervised/unsupervised machine learning, active Azure account
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.
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