Machine Learning on AWS Corporate Training
Turn cloud infrastructure into your team's machine learning advantage with hands-on, instructor-led training.
Give your enterprise teams the machine learning skills that turn AWS into a working ML platform. Edstellar's Machine Learning on AWS corporate training equips your engineers with practical techniques to prepare data, select and train models, and deploy them reliably on the cloud. Delivered live by experienced trainers, onsite or virtually, each session is tailored to your stack, roles, and goals, with measurable outcomes you can report to leadership.
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 an ML-on-AWS-ready team
THE EDSTELLAR CLOUD ML CAPABILITY MODEL
Assess cloud ML baseline
Skill checks reveal where your team stands with ML concepts and AWS today.
One cloud ML standard
Set shared patterns for data prep, training, and deployment across your team.
Practice in hands-on labs
Build, train, and deploy models in guided labs with instructor coaching.
Prove the lift
Re-run skill checks and model reviews so leadership sees capability rise.
Scale across teams
Roll out one ML-on-cloud 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 AWS Corporate Training | Edstellar?
Machine Learning on AWS is an instructor-led program for ML engineers and data scientists building machine learning on Amazon Web Services. It covers ML foundations on the cloud, data preparation and feature engineering, model selection and training, evaluation and experiment tracking, and deployment and monitoring, grounded in AWS-native services throughout.
Learners work inside real AWS environments: preparing data, training 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 end-to-end ML pipeline capstone, a production-style ML system running on AWS.
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 AWS way: the right services, disciplined experimentation, and deployments that scale and stay observable.
The capstone, an end-to-end ML pipeline on AWS, has each learner build and deploy a complete machine learning workflow on AWS infrastructure: data prep, training, experiment tracking, and a monitored endpoint. What they take back is a working reference architecture and the confidence to run ML on AWS without guesswork.
Teams pilot ML models fast, but without shared cloud patterns, training jobs stall, costs creep, and models never reach production. This program builds one clear standard: prepare data well, select models deliberately, tune with discipline, and deploy with monitoring from day one.
The result: engineers who ship reliable ML on AWS, from data pipelines to monitored production 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 cloud ideas your team uses daily.
- 2
Hands-on ML labs on AWS
Train and deploy models live with coaching.
- 3
Deployment toolkits
Reusable patterns for ML rollout on AWS.
- 4
Expert trainers
Learn from practitioners in cloud ML.
- 5
Capstone ML pipeline
Ship an end-to-end ML pipeline for a 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 AWS Course Topics and Program Outline
A six-module technical program on machine learning on AWS: ML foundations, data preparation, model selection and training, 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 AWS, with lab feedback on every exercise your team completes.
Baseline each participant's ML-on-AWS readiness before module 01 begins
Build strong machine learning foundations on AWS in instructor-led sessions
Reinforce skills through guided hands-on labs on SageMaker and ML services
Apply ML skills to real-world scenarios and a capstone deployment 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: SageMaker labs 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)Machine learning workflow overview
b)Cloud concepts for ML workloads
c)Environment setup and data access
d)Baseline skills check
a)Ingesting and profiling datasets
b)Cleaning and transforming data
c)Feature engineering patterns
d)Data lab: prepare a dataset
a)Choosing algorithms for use cases
b)Training jobs and compute options
c)Hyperparameter tuning strategies
d)Training lab: fit and tune a model
a)Metrics for classification and regression
b)Cross-validation and overfitting checks
c)Tracking experiments and versions
d)Evaluation lab: compare models
a)Packaging models for deployment
b)Serving predictions at scale
c)Monitoring drift and performance
d)Deployment lab: ship an endpoint
a)Integrate skills across modules
b)Build a full ML pipeline on AWS
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 machine learning models on AWS, from data preparation and model selection to tuning, deployment, and monitoring.
Prepare ML datasets on AWS
Ingest, clean, and engineer features from enterprise data on the cloud.
Select the right ML model
Match algorithms to business use cases using data characteristics.
Train and tune models at scale
Run training jobs and tune hyperparameters for strong model quality.
Evaluate models rigorously
Apply proper metrics and validation so models perform in production.
Deploy models to production
Package and serve models with endpoints that scale to demand.
Monitor models in production
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 AWS 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 ML workloads and pipelines they actually own.
Ship production ML on AWS faster.
ML engineering teams learn to train, tune, and deploy models on AWS with shared patterns for data prep, evaluation, and monitoring.
Workflow friction
- Training jobs stall and fail
- No shared deployment patterns
- Models drift in production
How the program helps
- Train models reliably
- Deploy with shared patterns
- Catch drift early
Practice use cases
- Training labs
- Deploy builds
- Drift drills
Feed models with clean data.
Data teams learn to build ingestion and feature pipelines on AWS that keep model inputs fresh, consistent, and well-governed.
Workflow friction
- Pipeline data is messy
- Features drift over time
- No versioning in place
How the program helps
- Build robust ML data flows
- Keep features consistent
- Version data clearly
Practice use cases
- Ingestion labs
- Feature builds
- Lineage reviews
Add ML features with confidence.
Software teams learn to integrate trained models into products with clean serving patterns, strong testing, and fast debugging.
Workflow friction
- Model APIs behave unpredictably
- No shared serving patterns
- Debugging ML is slow
How the program helps
- Integrate models cleanly
- Reuse proven serving patterns
- Debug ML faster
Practice use cases
- API labs
- Serving builds
- Debug sessions
Run ML workloads at scale.
Platform teams learn to provision compute, manage costs, and govern ML workloads on AWS so engineering teams ship safely.
Workflow friction
- GPU costs spiral upward
- ML jobs hog resources
- Governance gaps in ML
How the program helps
- Control ML compute costs
- Allocate resources fairly
- Govern ML workloads
Practice use cases
- Cost labs
- Quota reviews
- Policy drills
Each cohort works through role-relevant cloud 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 AWS
Learners start with ML foundations on the cloud and data preparation with feature engineering, then move into practice modules on model selection, training, evaluation, and experiment tracking using SageMaker. The arc closes with deploying and monitoring models on AWS and a capstone that ships an end-to-end ML pipeline in the cloud.
Teams grow from understanding AWS ML services to running complete, monitored machine learning pipelines on AWS 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 cloud stack 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 with instructor coaching. Best for engineering teams with flexible schedules and real build work.
- Live instruction across onsite and virtual sessions
- Guided 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 with coaching. Best for co-located teams wanting maximum interaction.
- Instructor delivers at your facility
- Labs built around your 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 cloud 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, cloud maturity, and timelines before sharing a plan.
Yes. Live virtual sessions run instructor-led on your preferred platform, with hands-on cloud ML labs, breakout exercises, and real-time feedback.
Yes. We deliver onsite at your offices, with the agenda and labs adapted to your team's cloud 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 AWS brings together two foundations your team builds first: how AWS works and how machine learning works. With both in place, building and deploying models on SageMaker and related services is straightforward, and MLOps then keeps those AWS models versioned, monitored, and updated.
Why Choose Edstellar as Your Machine Learning on AWS Corporate Training Provider
As your training partner, we make comparing providers simple: share your training needs and we shape a Machine Learning on AWS 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 SageMaker training and deployment use cases, mapped to your real machine learning on AWS work.
Proven Methodology
5+ years delivering corporate training to ML and data engineering teams across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds end-to-end ML pipelines on AWS with SageMaker with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your AWS ML, with a full trainer profile shared before your program is booked.
Global Scale
One ML on AWS program across your offices worldwide, in hybrid, onsite, or live virtual formats.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your ML on AWS capability lift visible.
Build AWS Machine Learning Workflows With Reproducible Training and Deployment
Edstellar develops cloud ML capability through an AWS pipeline that connects prepared data with a monitored prediction endpoint. Participants compare models, tune training jobs, and track experiments before examining deployment choices. The final system review focuses on evaluation rigor, version traceability, resource assumptions, and the evidence supporting reliability and scaling decisions.
- Step 01
Consult
We assess Python and ML foundations, AWS storage and access familiarity, and pipeline responsibilities to identify support needed for cloud data preparation, training, and endpoint deployment.
- Step 02
Plan
We establish the AWS workflow and data access before preparation and features, then connect training jobs, tracked evaluation, and endpoint monitoring in the complete pipeline.
- Step 03
Align
We align AWS ML engineering expertise with ML, data, software, and cloud teams, confirming a suitable learning environment and appropriate data and compute access for exercises.
- Step 04
Customize
We adapt sample prediction tasks and datasets to familiar use cases, specifying workload and resource assumptions so participants can compare training and serving configurations meaningfully.
- Step 05
Execute
Participants prepare data, tune cloud training jobs, compare experiments, and deploy endpoints, receiving feedback on validation, versioning, cost awareness, and monitoring for performance changes.
- Step 06
Evaluate
We assess the AWS pipeline for reproducible data and model stages, defensible evaluation, working endpoint behavior, and a production plan that explains scaling and monitoring choices.
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 AWS 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 AWS 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.