Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Corporate Training
Learn how your teams select, deploy, and govern AI workloads across the three leading cloud AI platforms.
Build enterprise AI capability across the cloud platforms your teams already run. Edstellar's Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) corporate training program helps your cloud and AI engineering teams compare the three leading platforms, select the right model services, and deploy workloads with cost control and governance. This hybrid program blends instructor-led sessions with hands-on labs tailored to your cloud landscape.
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 cloud-AI-ready engineering team
THE EDSTELLAR CLOUD AI CAPABILITY MODEL
Map cloud AI readiness
Skill checks reveal how your teams use cloud AI services today, before module one begins.
One platform playbook
Align engineers on how models are selected, deployed, and governed on each platform.
Practice in hands-on labs
Deploy models, build pipelines, and control cost in guided labs with instructor coaching.
Prove the lift
Re-run skill checks and capstone reviews so leadership sees cloud AI capability rise.
Scale across teams
Roll out one platform standard across engineering, data, architecture, and operations.
Need more than one program?Get a group quote
Trusted as a Leading Corporate Training Provider by
Program Overview: What is Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Corporate Training?
Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) is Edstellar's instructor-led program for engineers, architects, and platform teams choosing where their AI workloads should live. Over 20 hours, participants compare the three hyperscaler AI platforms hands-on: Azure OpenAI, AWS Bedrock, and Google Vertex AI, covering model catalogs, APIs, fine-tuning, grounding, and the cost, performance, and governance trade-offs of each. No vendor pitch; working code on all three.
Participants build the same AI features on each platform in guided labs, chat, retrieval-augmented generation, and agents, so differences in developer experience, latency, and cost become concrete rather than theoretical. Instructors help teams map platform capabilities to their own constraints: data residency, existing cloud commitments, and compliance needs. Five practice sessions compare, benchmark, and break things before the capstone.
Platform choice for AI is usually made on slideware: whichever vendor's rep visited last, or wherever the credits are. Six months later the team discovers the model's data handling violates policy, or inference costs 10x the estimate. This program replaces guesswork with evidence, your team will know what each platform actually does, costs, and constrains.
The capstone is a platform deployment plan: a justified recommendation of which platform (or combination) the team's AI workload should run on, with architecture, cost model, and governance controls spelled out. A multi-thousand-dollar platform decision defended with data instead of opinions: that is what they can now deliver.
Engineering teams face fragmented cloud AI services, unpredictable costs, and pressure to ship AI features safely across platforms. This program shows where each cloud platform fits, how to evaluate model services on capability and cost, and how to set guardrails that keep every deployment observable and compliant.
The result: teams that choose cloud AI platforms with confidence, deploy models efficiently, and keep AI workloads secure, governed, and cost-effective.
What's Included in this Workshop for Your Enterprise Teams
- 1
Platform foundations that stick
Core concepts your cloud teams apply daily.
- 2
Hands-on deployment labs
Deploy and manage AI services live.
- 3
Model selection toolkits
Templates for comparing model services.
- 4
Expert practitioner trainers
Learn from applied-AI cloud practitioners.
- 5
Capstone deployment plan
Leave with a plan for your AI workloads.
- 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
Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Course Topics and Program Outline
A six-module technical program covering cloud AI platform selection, managed AI services, cost and performance management, and governance. Participants practice in hands-on labs and finish with a capstone deployment plan.
Built by Edstellar with applied-AI trainers
Built by practitioners in applied AI and cloud platforms, with lab feedback on every exercise your team completes.
Baseline each participant's cloud AI platform readiness before module 01 begins.
Build strong cloud AI platform foundations in instructor-led sessions for your team.
Reinforce skills through guided hands-on labs across Azure OpenAI, Bedrock, and Vertex AI.
Apply cloud AI platform skills to real-world scenarios and a capstone governance 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: 5 hands-on labs across 6 modules 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)What cloud AI platforms cover
b)Where platform choice fits the AI lifecycle
c)Platform roles and responsibilities
d)Baseline skills check
a)Azure OpenAI service overview
b)Deploying models in the cloud
c)Prompt and chat workloads
d)Access and quota management
a)Bedrock model selection
b)Agents and knowledge bases
c)Guardrails for production use
d)Lab: build a Bedrock workflow
a)Vertex AI model garden
b)Training and tuning pipelines
c)Prediction and serving options
d)Lab: deploy a Vertex endpoint
a)Tracking AI spend across clouds
b)Latency and throughput tuning
c)Security and access controls
d)Monitoring and audit trails
a)Deployment scope and use case
b)Platform and model selection
c)Cost and governance design
d)Final deployment plan presentation
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 team will deploy AI workloads across the leading cloud platforms with confidence. Each competency is practiced in guided labs with instructor feedback.
Compare cloud AI platforms
Evaluate Azure OpenAI, AWS Bedrock, and Vertex AI on capability, cost, and fit.
Select the right model service
Match models to use cases using performance, cost, and feature trade-offs.
Deploy models in the cloud
Launch managed AI services with safe configurations and access controls.
Build AI workflows
Wire model services into pipelines, agents, and applications with APIs.
Manage AI cost and performance
Track spend, tune latency and throughput, and set budgets that hold.
Govern AI workloads
Apply access controls, monitoring, and audit trails across every platform.
Present a deployment plan
Defend platform and model choices so leaders approve the rollout.
Teams that Benefit from this Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Group Training
This program is built for cloud, platform, AI engineering, architecture, and data teams who build and run AI workloads. No deep cloud-certification background is needed: content adapts to role and experience, so each cohort practices cloud AI on the workflows they actually own.
Ship AI features across cloud platforms.
Cloud and platform teams learn to deploy managed AI services reliably. They practice model rollout, scaling, and cost control across platforms.
Workflow friction
- Platform choice feels arbitrary
- AI spend hard to predict
- Deployments need guardrails
How the program helps
- Deploy AI services reliably
- Control AI spend by design
- Add guardrails to every release
Practice use cases
- Deployment labs
- Cost control drills
- Multi-cloud setup
Run models in production with confidence.
AI engineering teams learn to serve models on cloud platforms and wire them into products. They practice evaluation, monitoring, and iteration.
Workflow friction
- Model serving is fragile
- Vendor lock-in concerns
- Production issues surface late
How the program helps
- Serve models with confidence
- Compare platforms on merit
- Catch issues before users
Practice use cases
- Serving labs
- Evaluation practice
- Monitoring setup
Choose cloud AI platforms on merit.
Architecture leaders learn to evaluate cloud AI platforms on capability, cost, and risk. They practice building platform standards teams can follow.
Workflow friction
- Too many platform options
- No shared selection criteria
- Risk hard to quantify
How the program helps
- Evaluate platforms on merit
- Set standards teams follow
- Quantify platform risk
Practice use cases
- Selection workshops
- Standards design
- Cost modeling labs
Put cloud AI behind your data work.
Data teams learn to call cloud AI services from analytics pipelines and build AI-assisted data products. They practice safe, governed integration.
Workflow friction
- AI feels separate from data
- Integration patterns unclear
- Data governance gaps
How the program helps
- Connect AI to data pipelines
- Apply clear integration patterns
- Keep AI governed end to end
Practice use cases
- Integration labs
- Pipeline practice
- Governance drills
Each cohort works through role-relevant labs, then leaves with reusable platform patterns for day-to-day AI 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 single-model experiments to multi-cloud AI platforms
Learners start with cloud AI platform foundations and working with Azure OpenAI, then practice building on AWS Bedrock and running Google Vertex AI workflows. Cost, performance, and governance optimization lead into a capstone platform deployment plan.
Engineers progress from platform basics to deploying governed AI workloads across Azure, AWS, and Google clouds.
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 landscape 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 deploys models, builds AI workflows, and manages cost with instructor coaching. Best for technical practice with scheduling flexibility.
- Live instruction across onsite and virtual sessions
- Guided labs with instructor coaching on your roles
- Practice deployment tasks 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 participant practices cloud AI deployment with coaching. Best for co-located teams wanting maximum interaction.
- Instructor delivers at your facility
- Labs built around your cloud landscape
- Practice deployment workflows together
- Best for co-located teams wanting maximum interaction
Live Virtual Training
Live virtual sessions run instructor-led on your preferred platform. Your team joins guided cloud labs, breakout exercises, and real-time 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 work 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 your 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 roles, and timelines before sharing a plan.
Yes. Live virtual sessions run instructor-led on your preferred platform, with guided hands-on cloud labs, breakout practice, and real-time feedback.
Yes. We deliver in-house at your offices globally, with labs built around your cloud landscape. Onsite suits co-located teams wanting maximum hands-on time.
The program runs 20 hours across six modules, typically delivered over three days. Scheduling is flexible and mutually agreed with you to fit your team's calendar.
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.
Cloud AI platforms sit after general cloud foundations and before building and costing applications. Your team needs accounts, networking, and identity in place first; then it uses Azure OpenAI, AWS Bedrock, or Vertex AI to build RAG solutions and keeps their spend under control.
Why Choose Edstellar as Your Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Corporate Training Provider
As your training partner, we make comparing providers simple: share your training needs and we shape a Cloud AI Platforms program around your cloud landscape, timelines and delivery mode. You get a matched trainer profile, deep AI expertise, and a plan your leaders can approve with confidence.
EXPERT TRAINERS
YEARS DELIVERING
ORGANIZATIONS TRAINED
CERTIFIED
Tailored
Content shaped around your Azure, AWS and Google AI use cases, so every session maps to your real cloud AI needs.
Proven Methodology
5+ years delivering corporate training to cloud and AI engineering teams across industries and regions.
Practice-Based Learning
5 hands-on labs across 6 modules. Your team builds multi-cloud AI deployments and governance playbooks with instructor guidance.
Expert Trainers
5,000+ certified trainers matched to your Azure, AWS and Google AI, with a full trainer profile shared before your program is booked.
Global Scale
One cloud AI platforms program across your offices worldwide, in hybrid, onsite, or live virtual.
Measurable Outcomes
Baseline-to-capstone measurement and Skills Matrix reporting keep your cloud AI capability lift visible.
Compare Cloud AI Platforms Through Practical Deployment and Governance Decisions
Edstellar connects cloud AI foundations with the distinct workflows of Azure OpenAI, AWS Bedrock, and Google Vertex AI. Participants examine model deployment, guarded workflows, and serving options before addressing costs and oversight. A final deployment plan tests whether platform choices match the use case, access needs, and operational constraints.
- Step 01
Consult
We review cloud identity, networking, API, and application familiarity to identify gaps in model deployment, platform comparison, and AI resource governance.
- Step 02
Plan
We establish cloud AI roles before Azure deployments, Bedrock workflows, and Vertex serving, then compare performance, cost, and controls in the deployment plan.
- Step 03
Align
We align cloud AI instruction with participants' API and coding readiness, confirming suitable service access and practice scope across the selected platforms.
- Step 04
Customize
We adapt chat workloads, knowledge-base examples, and endpoint scenarios to representative applications, using sample data and explicit resource and access constraints.
- Step 05
Execute
Participants develop Bedrock workflows and Vertex endpoints, examining Azure deployment choices with feedback on model selection, access controls, latency, and spend assumptions.
- Step 06
Evaluate
We review the platform deployment plan for use-case fit, justified model selection, explicit cost assumptions, and coherent security, monitoring, and governance decisions.
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?
Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Training 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 Cloud AI Platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) Training for Your Team
Tell us your team size, cloud roles, and preferred delivery mode. We will confirm availability and respond with a tailored training plan covering scope, schedule, and pricing.