Yesterday, 09:47 PM
[center]![[Image: 890c2a67500e81bd93216d82092daea9.jpg]](https://i128.fastpic.org/big/2026/0724/a9/890c2a67500e81bd93216d82092daea9.jpg)
Cloud + Ai Infrastructure On Aws, Azure & Gcp
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 0m | Size: 449.74 MB [/center]
Build, deploy, secure, and cost-control production AI on Amazon Bedrock, Azure OpenAI, and Google Vertex AI
What you'll learn
Compare AWS, Azure, and GCP AI platforms and choose the right one for a given workload, budget, and team
Provision and right-size GPU/TPU compute, networking, and storage for AI training and inference
Build on Amazon Bedrock: call foundation models, add Knowledge Bases for RAG, and deploy scalable endpoints
Build on Azure OpenAI and Azure ML with content safety, private networking, and enterprise identity
Build on Google Vertex AI: Gemini, Model Garden, grounding, pipelines, and custom training
Apply MLOps: containerise, serve, version, CI/CD-deploy, and roll back models in production
Monitor models for drift, latency, and reliability, and autoscale inference under real load
Secure AI infrastructure with IAM, secrets management, network isolation, and responsible-AI governance
Control AI spend with FinOps - tame the GPU and token bill and prove cost per inference
Design a production-grade, multi-cloud AI platform and map your path through AWS, Azure, and GCP AI certifications
Requirements
Basic familiarity with at least one cloud (an account on AWS, Azure, or GCP you can experiment in helps)
Comfort with the command line and basic scripting; deep ML or data-science background is NOT required
General understanding of what large language models do - everything cloud-specific is taught from the ground up
A real or imagined AI workload in mind - you will design infrastructure for it in the capstone
Description
This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
Anyone can call an AI model from a notebook. Running AI in production - for thousands of users, on a budget your CFO will approve, without leaking data or melting a GPU bill - is an entirely different skill, and it is the one cloud teams are hiring and paying a premium for in 2026.This course teaches that skill across all three major clouds: Amazon Web Services, Microsoft Azure, and Google Cloud. It is built for cloud engineers, ML and platform engineers, DevOps and SRE practitioners, solution architects, and anyone preparing for an AWS, Azure, or GCP AI certification who wants the cert to mean they can actually build. You will not just learn what Amazon Bedrock, Azure OpenAI, and Google Vertex AI are - you will provision the compute, deploy foundation models, wire up retrieval-augmented generation, serve and autoscale inference, lock the whole thing down with identity and networking, and put a real number on what it costs.
We go end to end: the AI cloud landscape and how the three hyperscalers truly differ; GPUs, TPUs, and the networking and storage that feed them; hands-on builds on Bedrock, Azure OpenAI, and Vertex AI; MLOps from a notebook to a versioned, monitored, CI/CD-deployed service on Kubernetes; and the part most courses skip entirely - security, governance, and FinOps for AI, where the real production money and risk live. Every cost and design decision comes with one global, dollar-based example and one India, rupee-based example, so the trade-offs feel real wherever you work. I have spent more than twenty years putting AI and automation into real operations, living with the budgets, the outages, and the audits this course is about. You will finish able to design and defend a production AI platform on the cloud - and walk into the certification exam, or the architecture interview, knowing you have actually done it. Enrol now and stop being someone who can only call the model.
Who this course is for
Cloud engineers and architects who want to add production AI infrastructure to their toolkit
ML, platform, and MLOps engineers moving models from notebooks into reliable, scalable services
DevOps and SRE practitioners now responsible for AI workloads, GPUs, and their cost and uptime
Solution architects designing AI systems across AWS, Azure, or Google Cloud
Engineers preparing for AWS, Azure, or GCP AI/ML certifications who want hands-on depth, not just exam facts
Technical leads and team leads evaluating multi-cloud AI strategy, lock-in, and spend
Cloud + Ai Infrastructure On Aws, Azure & Gcp
Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 0m | Size: 449.74 MB [/center]
Build, deploy, secure, and cost-control production AI on Amazon Bedrock, Azure OpenAI, and Google Vertex AI
What you'll learn
Compare AWS, Azure, and GCP AI platforms and choose the right one for a given workload, budget, and team
Provision and right-size GPU/TPU compute, networking, and storage for AI training and inference
Build on Amazon Bedrock: call foundation models, add Knowledge Bases for RAG, and deploy scalable endpoints
Build on Azure OpenAI and Azure ML with content safety, private networking, and enterprise identity
Build on Google Vertex AI: Gemini, Model Garden, grounding, pipelines, and custom training
Apply MLOps: containerise, serve, version, CI/CD-deploy, and roll back models in production
Monitor models for drift, latency, and reliability, and autoscale inference under real load
Secure AI infrastructure with IAM, secrets management, network isolation, and responsible-AI governance
Control AI spend with FinOps - tame the GPU and token bill and prove cost per inference
Design a production-grade, multi-cloud AI platform and map your path through AWS, Azure, and GCP AI certifications
Requirements
Basic familiarity with at least one cloud (an account on AWS, Azure, or GCP you can experiment in helps)
Comfort with the command line and basic scripting; deep ML or data-science background is NOT required
General understanding of what large language models do - everything cloud-specific is taught from the ground up
A real or imagined AI workload in mind - you will design infrastructure for it in the capstone
Description
This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
Anyone can call an AI model from a notebook. Running AI in production - for thousands of users, on a budget your CFO will approve, without leaking data or melting a GPU bill - is an entirely different skill, and it is the one cloud teams are hiring and paying a premium for in 2026.This course teaches that skill across all three major clouds: Amazon Web Services, Microsoft Azure, and Google Cloud. It is built for cloud engineers, ML and platform engineers, DevOps and SRE practitioners, solution architects, and anyone preparing for an AWS, Azure, or GCP AI certification who wants the cert to mean they can actually build. You will not just learn what Amazon Bedrock, Azure OpenAI, and Google Vertex AI are - you will provision the compute, deploy foundation models, wire up retrieval-augmented generation, serve and autoscale inference, lock the whole thing down with identity and networking, and put a real number on what it costs.
We go end to end: the AI cloud landscape and how the three hyperscalers truly differ; GPUs, TPUs, and the networking and storage that feed them; hands-on builds on Bedrock, Azure OpenAI, and Vertex AI; MLOps from a notebook to a versioned, monitored, CI/CD-deployed service on Kubernetes; and the part most courses skip entirely - security, governance, and FinOps for AI, where the real production money and risk live. Every cost and design decision comes with one global, dollar-based example and one India, rupee-based example, so the trade-offs feel real wherever you work. I have spent more than twenty years putting AI and automation into real operations, living with the budgets, the outages, and the audits this course is about. You will finish able to design and defend a production AI platform on the cloud - and walk into the certification exam, or the architecture interview, knowing you have actually done it. Enrol now and stop being someone who can only call the model.
Who this course is for
Cloud engineers and architects who want to add production AI infrastructure to their toolkit
ML, platform, and MLOps engineers moving models from notebooks into reliable, scalable services
DevOps and SRE practitioners now responsible for AI workloads, GPUs, and their cost and uptime
Solution architects designing AI systems across AWS, Azure, or Google Cloud
Engineers preparing for AWS, Azure, or GCP AI/ML certifications who want hands-on depth, not just exam facts
Technical leads and team leads evaluating multi-cloud AI strategy, lock-in, and spend
Code:
https://nitroflare.com/view/A1DB6CF196682BB/Cloud___AI_Infrastructure_on_AWS%2C_Azure_%26amp%3B_GCP.rar
https://rapidgator.net/file/8618c6c6ee56a4c6760e08f932ac4b2e/Cloud___AI_Infrastructure_on_AWS,_Azure_&_GCP.rar.html
https://www.uploadcloud.pro/t1y6bg5p4ick/Cloud___AI_Infrastructure_on_AWS__Azure__amp__GCP.rar.html