Machine Learning on Microsoft Azure Corporate Training
Help your teams build, train, and deploy machine learning models on Microsoft Azure.
Give your enterprise teams the cloud ML capability to turn Microsoft Azure into a dependable machine learning platform. Edstellar's Machine Learning on Microsoft Azure corporate training program helps your engineers prepare data, choose and train models, and ship them into production with confidence. Every session is instructor-led and tailored to your cloud stack, roles, and goals, with hands-on labs and measurable skill growth.
Instructor-led group trainingVirtual / on-site / off-site10 languagesDelivered inEnglish, Spanish, Mandarin, German, Arabic, Portuguese, Hindi, French, Japanese, Italian100+ countriesWe have delivered inUnited States, United Kingdom, India, Canada, Germany, France, Netherlands, UAE, Saudi Arabia, Singapore, Australia, Japan, Brazil, South Africa and 90+ more countries
How we build a ML-on-Azure-ready team
THE EDSTELLAR CLOUD ML CAPABILITY MODEL
Gauge the ML baseline
Skill checks show where your teams stand with ML concepts and Azure today.
One ML-on-cloud standard
Set common patterns for data prep, training, evaluation, and deployment.
Practice in hands-on labs
Prepare data and train models in guided Azure labs, with trainer coaching.
Prove the lift
Re-run skill checks and pipeline reviews so leadership sees progress clearly.
Scale across teams
Roll out one cloud ML standard across teams, batches, and locations.
Need more than one program?Get a group quote
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Program Overview: What is Machine Learning on Microsoft Azure Corporate Training?
Machine Learning on Microsoft Azure is an instructor-led program for ML engineers and data scientists building machine learning on Azure. It covers ML foundations on Azure, data preparation and feature engineering, model training and tuning, evaluation and experiment tracking, and deployment and monitoring, grounded in Azure-native services throughout.
Learners work inside real Azure environments: preparing data, training and tuning models, tracking experiments, and deploying endpoints in guided labs on cloud infrastructure. Each exercise uses the services production teams actually run, and instructors review architectures and configurations, coaching participants on cost, scalability, and reliability. Feedback is hands-on and specific. The program ends with the Azure ML Pipeline Build capstone, a complete pipeline delivered on Azure.
Cloud ML projects stall when teams cannot navigate the service maze: oversized instances, untracked experiments, and deployments that break. Lift-and-shift thinking wastes cloud budgets fast. This program teaches participants to build machine learning the Azure way: the right services, disciplined experimentation, and deployments that scale and stay observable.
The Azure ML Pipeline Build capstone has each learner deliver a complete, working ML pipeline on Azure: prepared data, tuned models, tracked experiments, and a deployed endpoint with monitoring. They finish with a documented Azure build they can reference on real projects and a clear sense of how Azure ML services fit together.
ML pilots stall when teams lack shared cloud patterns: training runs crawl, experiments go untracked, and promising models never reach production. This program builds one reliable workflow: prepare data well, train deliberately, evaluate honestly, and deploy with monitoring in place.
The result: engineers who deliver machine learning on Azure end to end, from data pipelines to monitored models, supporting faster delivery and safer ML use across your organization.
What's Included in this Workshop for Your Enterprise Teams
- 1
Cloud ML foundations
Core ML and Azure ideas your team uses daily.
- 2
Hands-on Azure ML labs
Train and deploy models live with coaching.
- 3
Deployment playbooks
Repeatable patterns for shipping ML on Azure.
- 4
Expert trainers
Learn from practitioners in cloud ML.
- 5
Capstone ML pipeline
Ship an end-to-end ML pipeline for your use case.
- Training delivered at any of your offices globally
- 10+ languages
- Schedule around your calendar
- Group size set along with you
- Run as one cohort or split into batches
- Instructor-led (virtual/onsite/hybrid)
- Recurring and annual options
- Trainer sourcing, matching and logistics
Build your team's skills. Anywhere in the world.
Real-world use cases
from day one
Instructor-led training
for your teams
Delivered globally
in 100+ countries
Enterprise Training Solutions Customized for Your Company
One partner for instructor-led corporate training, delivered the way your teams work.
Request a Training Quote- Customized Training Delivery
- Scale Your Training: Small to Large Teams
- In-person Onsite, Live Virtual or Hybrid Training Modes
- Plan from 2000+ Industry-ready Training Programs
- Hands-On Labs on Live Infrastructure, Led by Industry Experts
Machine Learning on Microsoft Azure Course Topics and Program Outline
A six-module technical program on ML on Microsoft Azure: foundations, data preparation, model training and tuning, evaluation, deployment and monitoring. Five guided hands-on labs plus a capstone pipeline build.
Built by Edstellar with cloud ML trainers
Built by practitioners in machine learning on Azure, with lab feedback on every exercise your team completes.
Baseline each participant's Azure ML readiness before module 01 begins
Build strong Azure ML foundations in instructor-led sessions for your team
Reinforce skills through guided hands-on labs on Azure ML Studio, pipelines, and deployments
Apply Azure ML skills to real-world scenarios and a capstone pipeline project
A short pre-training assessment sets each participant's entry level, so the program starts where your team actually is. The rest is weighted toward hands-on practice: Azure ML Studio labs, model pipelines, and realistic scenarios drawn from your team's work, not slides.
Delivered as
Entry level set by the assessment. Depth and schedule customized to your team.
a)How ML projects run on Azure
b)Azure essentials for ML workloads
c)Setting up your ML environment
d)Baseline skills check
a)Loading and profiling datasets
b)Cleaning and reshaping data
c)Feature engineering techniques
d)Data lab: prepare a dataset
a)Picking algorithms for your use case
b)Running training jobs on Azure
c)Hyperparameter search strategies
d)Training lab: fit and tune a model
a)Choosing the right metrics
b)Cross-validation and overfitting checks
c)Tracking experiments and versions
d)Evaluation lab: compare models
a)Packaging models for release
b)Serving predictions at scale
c)Watching for drift and degradation
d)Deployment lab: ship an endpoint
a)Integrate skills across modules
b)Build an ML pipeline on Azure
c)Present the pipeline for review
d)Leave with a production plan
Customize this program tailored to your team?
Tell us your training needs: what your team already works with, the level they’re at, and the topics you want them to focus on. We’ll adjust the modules, duration and hands-on labs, and send you a customized outline for your group.
Key Skills and Outcomes Your Team Will Gain
By the end of this program, your engineers will build, train, and deploy ML models on Microsoft Azure, from data prep and model selection to tuning, evaluation, deployment, and monitoring.
Prepare datasets for ML
Ingest, clean, and engineer features from enterprise data on Azure.
Choose the right model
Match algorithms to use cases using data and business needs.
Train and tune on Azure
Run training jobs and search hyperparameters for strong quality.
Evaluate models rigorously
Apply proper metrics and validation so models hold up in production.
Deploy models to production
Package and serve models behind endpoints that scale to demand.
Monitor models live
Track drift and performance to keep predictions reliable over time.
Ship end-to-end pipelines
Connect data, training, and deployment into repeatable pipelines.
Teams that Benefit from this Machine Learning on Microsoft Azure Group Training
This program is built for engineers and technical teams who run ML on the cloud: ML engineers, data engineers, software developers, and platform teams. Content adapts to role and experience, so every cohort practices on the Azure ML workloads and pipelines they actually own.
Ship ML on Azure with confidence.
ML engineering teams learn to train, tune, and deploy models on Azure with shared patterns for data prep, evaluation, and monitoring.
Workflow friction
- Training runs stall or fail
- Experiments are hard to repeat
- Models drift after launch
How the program helps
- Train reliably on Azure
- Reproduce every experiment
- Catch drift before damage
Practice use cases
- Training labs
- Pipeline reviews
- Monitoring drills
Pipelines that feed good models.
Data teams learn to build ingestion and feature pipelines on Azure that keep model inputs fresh, consistent, and well-governed.
Workflow friction
- ML inputs are inconsistent
- Feature logic keeps changing
- Lineage is hard to trace
How the program helps
- Standardize ML data flows
- Stabilize feature logic
- Trace lineage clearly
Practice use cases
- Ingestion labs
- Feature builds
- Data audits
Production services with ML inside.
Software teams learn to integrate trained models into applications with clean serving patterns, solid testing, and quick debugging.
Workflow friction
- ML endpoints behave oddly
- No shared serving patterns
- Debugging ML takes long
How the program helps
- Serve models predictably
- Reuse proven patterns
- Debug issues faster
Practice use cases
- Serving labs
- Integration builds
- Debug sessions
Azure estates that run ML well.
Platform teams learn to provision compute, manage cost, and govern ML workloads on Azure so engineering teams ship safely.
Workflow friction
- ML compute costs grow
- Resources get hogged by jobs
- Governance has blind spots
How the program helps
- Control ML spend
- Allocate compute fairly
- Close governance gaps
Practice use cases
- Cost labs
- Capacity reviews
- Policy checks
Each cohort works through role-relevant Azure ML labs, then leaves with reusable patterns for day-to-day work.
Prerequisites: This program benefits network engineers, RF designers, planners, and wireless researchers most. Familiarity with basic wireless communications concepts helps, but is not required: foundations are taught from the ground up. No prior radar or 6G experience is needed, and labs adapt to each cohort's tools, whether teams work in simulation or on lab hardware.
From cloud ML concepts to production ML on Microsoft Azure
Learners start with ML foundations on Azure and data preparation with feature engineering, then move into practice modules on model training, tuning, evaluation, and experiment tracking with Azure Machine Learning. The arc closes with deploying and monitoring models on Azure and a capstone that builds a full Azure ML pipeline.
Teams grow from understanding Azure ML services to running complete, monitored machine learning pipelines on Azure infrastructure.
Mixed cohort, or several teams at once?
We can tailor the training to different teams, roles, and experience levels within the same program.
Certificate of Training Completion.
On successful completion, employees receive an Edstellar course completion certificate, recognizing their commitment to ongoing learning and professional development. It validates the skills they have built and motivates them to keep growing and contribute to organizational success.
Earned, not attended. Awarded on completion of all hands-on labs and the capstone project.
Training Delivery Modes: Virtual, Onsite and Hybrid
Every mode is instructor-led and tailored to your Azure setup before delivery. The curriculum, hands-on labs, assessments, and capstone stay the same across hybrid, onsite, and live virtual formats, so capability is consistent however you run it.
Hybrid Training
Live instruction plus supervised hands-on labs, across onsite and virtual sessions. Your team prepares data, trains, and deploys models on Azure with coaching. Best for engineering teams with flexible schedules and real build work.
- Live instruction across onsite and virtual sessions
- Guided Azure ML labs with instructor coaching on your roles
- Train and deploy models in real time
- Best for flexible schedules with real hands-on practice
Onsite Training
Your instructor delivers at your facility with labs set up for your team. Every engineer trains and deploys models on Azure with coaching. Best for co-located teams wanting maximum interaction.
- Instructor delivers at your facility
- Labs built around your Azure ML use cases
- Build and deploy models together with coaching
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions run instructor-led on your preferred platform. Your team joins Azure ML labs, breakout exercises, and real-time code reviews with the instructor. Ideal for distributed teams needing hands-on practice without travel.
- Live instructor-led sessions on your platform
- Guided labs with breakout troubleshooting
- Screen-shared code reviews in real time
- Ideal for distributed teams across locations
Need a training format that works for your team?
Full days, half days across weeks, or split by module. Tell us the delivery window and we schedule the cohort around it.
Frequently Asked Questions
Share team size, preferred dates, and delivery mode in the lead form, or book a short discovery call first. We reply with a tailored proposal covering scope, schedule, and pricing for your group.
Yes. Book a short discovery call and we will map your training needs, Azure maturity, and timelines before sharing a plan.
Yes. Live virtual sessions run instructor-led on your preferred platform, with hands-on Azure ML labs, breakout exercises, and real-time feedback.
Yes. We deliver onsite at your offices, with the agenda and labs adapted to your Azure setup, use cases, and workflows.
The standard program runs over two full days. Duration can be adjusted to fit your schedule, from focused one-day sessions to extended multi-day formats.
Question not answered here?
Put it in the request. A training specialist answers it directly, within one business day.
Where this Training Fits in Your Team's learning path.
Machine learning on Azure follows two separate foundations: Azure fundamentals and general machine learning. Once your team knows the platform and the modelling concepts, Azure Machine Learning becomes a practical tool rather than a learning curve, and MLOps then carries those models into steady, governed production use.
Why Choose Edstellar as Your Machine Learning on Microsoft Azure Corporate Training Provider
As your training partner, we make comparing providers simple: share your training needs and we shape a Machine Learning on Microsoft Azure program around your stack, timelines, and delivery mode. You get a matched trainer profile, deep cloud ML expertise, and a plan your leaders can approve with confidence.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your machine learning and Azure ML use cases, so every session maps to your real machine learning needs.
Proven Methodology
5+ years delivering corporate training to ML engineers and data engineers across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds end-to-end Azure ML pipelines with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your Azure ML stack, with a full trainer profile shared before your program is booked.
Global Scale
One machine learning on Microsoft Azure program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your ML on Azure capability lift visible.
Develop Azure ML Pipelines Through Data, Training, and Operational Review
Edstellar develops Azure ML capability by connecting each stage of a cloud modelling project. Participants prepare datasets, run training and tuning, and evaluate versioned experiments before releasing an endpoint. The final pipeline review tests the consistency of these stages, with feedback on reproducibility, deployment configuration, degradation monitoring, and the resulting operational plan.
- Step 01
Consult
We review Python and machine-learning readiness, Azure resource familiarity, and current training or release challenges to identify the cohort's cloud pipeline development needs.
- Step 02
Plan
We establish Azure ML workflows and environment setup before data transformation, then combine training, hyperparameter search, experiment comparison, and monitored serving in the pipeline build.
- Step 03
Align
We align Azure ML engineering instruction with ML, software, data, and platform roles, confirming suitable storage, compute, and access arrangements within the learning environment.
- Step 04
Customize
We adapt sample prediction datasets and release scenarios to familiar business tasks, setting explicit evaluation and resource constraints for comparing models and deployment options.
- Step 05
Execute
Participants profile data, train and tune models, compare tracked runs, and package endpoints, receiving feedback on metric choices, version consistency, and detection of drift or degradation.
- Step 06
Evaluate
We assess the Azure pipeline and operating plan for repeatable stages, justified model selection, verified endpoint results, and monitoring decisions supported by the workload and evaluation evidence.
Also from Edstellar
Talent assessments
Psychometric, behavioral, leadership and 360-degree assessment.
→Training needs analysis
Needs analysis, gap identification, and a training roadmap.
→L&D consulting
Learning strategy, content, technology and ROI measurement.
→OD consulting
Org design, succession planning and cultural change.
→Managed training services
Training outsourcing, vendor management, logistics and administration.
→Coaching solutions
Executive and manager coaching that keeps skills in use.
Start an RFP shaped to your needs.
Answer three quick questions about team size, scope, and how often you need the program run, then send the request from the last step. Everything you choose travels with it, so you only fill this in once.
How many team members need training?
Machine Learning on Microsoft Azure Training Packages & Pricing
Where L&D holds an annual budget, license packages price the whole year at once instead of raising a purchase order per workshop. Hours and licenses are pooled across teams, programs and locations, so unused capacity is not stranded in one department.
Starter
120
trainee licenses64 hours of group training
Tailored for SMBs
Growth
320
trainee licenses160 hours of group training
Ideal for growing SMBs
Enterprise
800
trainee licenses400 hours of group training
Designed for large corporations
Custom
Unlimited
trainee licensesDuration set by the rollout
Multi-region and multi-year
Training Packages & Pricing
Choose the training format that works for your team. Pricing is customized based on your requirements, cohort size, and delivery needs.
Request Machine Learning on Microsoft Azure Training for Your Team
Tell us your team size, engineering roles, and preferred delivery mode. We will confirm availability and respond with a tailored training plan covering scope, schedule, and pricing.