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Training Azure Machine Learning - Deploying Scalable ML Pipelines

Ref: QXK409
10 people max.
From $5,775 HT / per person
On-site on request · +$540 with certification exam
5 days
Remote

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Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
Equans
EDF
Ubisoft
Microsoft
Aptar
ArcelorMittal
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Learning objectives

  • Master Azure ML Studio for developing professional certifiable models
  • Configure secure workspaces and environments in enterprise settings
  • Implement automated data pipelines with Azure ML
  • Optimize scalable ML model training on Azure GPUs
  • Deploy and monitor reliable ML endpoints in production
  • Integrate Azure ML into DevOps workflows for advanced skills
A child walking to school with a backpack
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 Azure Machine Learning mastery, data teams waste 50% of their time on non-scalable manual trainings, with cloud costs inflated up to 30k€ annually per poorly optimized project.

  • Unmonitored models generate 25% errors in production, causing 15% losses in predictive revenue.

  • In 2024, 68% of companies fail their AI initiatives due to lack of cloud ML expertise, risking obsolescence against competitors 3x faster.

  • Each quarter without Azure skills widens an unrecoverable competitive gap.

Allan BUSI
Allan BUSI

Learni trainer · AI expert

73%productivity gap
×3cost of inaction

Program

Module 1Fundamentals: configure Azure Machine Learning workspaces and studios (CLI, SDK, UI)

Dive into Azure Machine Learning by creating your first secure workspace via the Azure portal and ML Studio interface, install the Python SDK to automate setups, explore compute instances and GPU clusters for fast training, complete practical exercises on resource management, and produce a ready-to-use environment for professional projects with full documentation.

Module 2Data: prepare datasets and datastores with Azure Machine Learning (ingestion, cleaning)

Import and clean large datasets from Blob Storage or Data Lake via Azure ML pipelines, use built-in tools for EDA exploration with Pandas and MLflow tracking, apply automated transformations on real enterprise data, test dataset versioning for traceability, and generate interactive visual reports along with an optimized datastore for ML training.

Module 3Models: train and tune algorithms with Azure Machine Learning (AutoML, hyperparameters)

Launch distributed training jobs on Azure ML with scikit-learn, TensorFlow, and PyTorch, enable AutoML to identify the best models in hours instead of days, configure hyperparameter tuning via Bayesian sweeps on GPU clusters, evaluate performance with ROC-AUC metrics and cross-validation, and produce a leading model with MLflow logs for immediate production integration.

Module 4Pipelines: automate end-to-end workflows with Azure Machine Learning (MLOps, Designer)

Design CI/CD pipelines with Azure ML Designer and Python SDK for ingestion-enrichment-training-deployment, integrate Git for versioning code and models, test serverless orchestrations with Azure Functions, simulate fault-tolerant scenarios on large data volumes, and deliver a reproducible pipeline with automatic scheduling for continuous enterprise deployments.

Module 5Production: deploy and monitor Azure Machine Learning endpoints (inference, scaling)

Deploy models to real-time ACI/AKS endpoints with secure scoring via Azure AD tokens, configure auto-scaling for traffic spikes, implement monitoring with Application Insights and drift detection, test inference on concrete business cases like churn prediction, generate dashboards and alerts for proactive supervision, and finalize with a deployed and certified red thread project.

Evaluation method

  • Technical quiz on Azure ML at the end of the training
  • Practical evaluation via pipelines and deployments
  • Defense of end-to-end project before experts

Learning method

  • Sessions led by active Microsoft certified trainers
  • Hands-on exercises on real enterprise Azure cases
  • Red thread project Azure ML from workspace to monitoring
  • Complete pedagogical support and accessible cloud resources

Methods, materials and delivery

The Training Azure Machine Learning - Deploying Scalable ML Pipelines 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 ML Pipelines 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 ML Pipelines 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.

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.

FAQ

Frequently asked questions

How much does the Training Azure Machine Learning - Deploying Scalable ML Pipelines 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 ML Pipelines 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?+
Mastery of Python for data science, basics of machine learning with scikit-learn, 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

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