AI in Production: Gen AI and Agentic AI at scale

[0dayddl]

AI in Production: Gen AI and Agentic AI at scale
2025-09-19
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 13.39 GB | Duration: 18h 37m​

Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable.

What you'll learn
Deploy SaaS LLM apps to production on Vercel, AWS, Azure, and GCP, using Clerk
Design cloud architectures with Lambda, S3, CloudFront, SQS, Route 53, App Runner and API Gateway
Integrate with Amazon Bedrock and SageMaker, and build with GPT-5, Claude 4, OSS, AWS Nova and HuggingFace
Rollout to Dev, Test and Prod automatically with Terraform and ship continuously via GitHub Actions
Deliver enterprise-grade AI solutions that are scalable, secure, monitored, explainable, observable, and controlled with guardrails.
Create Multi-Agent systems and Agentic Loops with Amazon Bedrock AgentCore and Stands Agents

Requirements
While it's ideal if you can code in Python and have some experience working with LLMs, this course is designed for a very wide audience, regardless of background. I've included a whole folder of self-study labs that cover foundational technical and programming skills. If you're new to coding, there's only one requirement: plenty of patience!
The course runs best if you have a small budget for APIs and Cloud Providers of a few dollars. But we monitor expenses at every point, and it's always a personal choice.

Description
This is the course that more of my students have asked for than any other course - put together.One student called it:"The missing course in AI."This course is for:EntrepreneursEnterprise engineers.and everyone in between.It's not just about RAG - although we'll work with RAG.It's not just about Agents - but there will be many Agents.It's not just about MCP - but yes, there will be plenty of MCP too.This course is about:RAG, Agents, MCP, and so much more. deployed to production.Live.Enterprise-grade.Scalable, resilient, secure, monitored - and explained.You'll ship real-world, production-grade AI with LLMs and agents across Vercel, AWS, GCP, and Azure, going deepest on AWS.Across four weeks you'll take four products to production:Week 1You'll launch a Next.js SaaS product on Vercel and AWS,with AWS App Runner and Clerk for user management and subscriptions. Week 2You'll become an AI platform engineer on AWS,deploying serverless infrastructure using:Lambda, Bedrock, API Gateway, S3, CloudFront, Route 53Write Infrastructure as Code with TerraformSet up CI/CD pipelines with GitHub Actions- for hands-free deployments and one-click promotions.Week 3You'll gain broad industry skills for GenAI in productioneploy a Cyber Security Analyst agent with MCP to Azure & GCPStand up SageMaker inferenceBuild data ingest to S3 vectorsDeploy a Researcher Agent using OpenAI OSS models on Bedrock + MCPWeek 4You'll go fully agentic in production:Architect multi-agent systems with:Aurora Serverless, Lambda, SQSJWT-authenticated CloudFront frontendsLangFuse observabilityOverview of AWS Agent CoreBy the end, you'll know how toick the right architectureLock down securityMonitor costsDeliver continuous updatesEverything needed to run scalable, reliable AI apps in production.Course sections (Weeks & Projects)Week 1SaaS App Live in Production with Vercel, AWS, Next.js, Clerk, App RunnerProject: SaaS Healthcare AppWeek 2AI Platform Engineering on AWS with Bedrock, Lambda, API Gateway, Terraform, CI/CDProject: Digital Twin Mk IIWeek 3Gen AI in Production with Azure, GCP, AWS SageMaker, S3 Vectors, MCPProject: Cybersecurity AnalystWeek 4Agentic AI in Production: Build and deploy a Multi-Agent System on AWS (Aurora Serverless, Lambda, SQS),with LangFuse and Bedrock AgentCoreCapstone Project: SaaS Financial Planner

Who this course is for:
If you're excited about the idea of deploying Gen AI and Agents live in production - then this course is for you.

For More Courses Visit & Bookmark Your Preferred Language Blog
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[0dayddl]

AI in Production: Gen AI and Agentic AI at scale
2025-09-19
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 13.39 GB | Duration: 18h 37m​

Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable.

What you'll learn
Deploy SaaS LLM apps to production on Vercel, AWS, Azure, and GCP, using Clerk
Design cloud architectures with Lambda, S3, CloudFront, SQS, Route 53, App Runner and API Gateway
Integrate with Amazon Bedrock and SageMaker, and build with GPT-5, Claude 4, OSS, AWS Nova and HuggingFace
Rollout to Dev, Test and Prod automatically with Terraform and ship continuously via GitHub Actions
Deliver enterprise-grade AI solutions that are scalable, secure, monitored, explainable, observable, and controlled with guardrails.
Create Multi-Agent systems and Agentic Loops with Amazon Bedrock AgentCore and Stands Agents

Requirements
While it's ideal if you can code in Python and have some experience working with LLMs, this course is designed for a very wide audience, regardless of background. I've included a whole folder of self-study labs that cover foundational technical and programming skills. If you're new to coding, there's only one requirement: plenty of patience!
The course runs best if you have a small budget for APIs and Cloud Providers of a few dollars. But we monitor expenses at every point, and it's always a personal choice.

Description
This is the course that more of my students have asked for than any other course - put together.One student called it:"The missing course in AI."This course is for:EntrepreneursEnterprise engineers.and everyone in between.It's not just about RAG - although we'll work with RAG.It's not just about Agents - but there will be many Agents.It's not just about MCP - but yes, there will be plenty of MCP too.This course is about:RAG, Agents, MCP, and so much more. deployed to production.Live.Enterprise-grade.Scalable, resilient, secure, monitored - and explained.You'll ship real-world, production-grade AI with LLMs and agents across Vercel, AWS, GCP, and Azure, going deepest on AWS.Across four weeks you'll take four products to production:Week 1You'll launch a Next.js SaaS product on Vercel and AWS,with AWS App Runner and Clerk for user management and subscriptions. Week 2You'll become an AI platform engineer on AWS,deploying serverless infrastructure using:Lambda, Bedrock, API Gateway, S3, CloudFront, Route 53Write Infrastructure as Code with TerraformSet up CI/CD pipelines with GitHub Actions- for hands-free deployments and one-click promotions.Week 3You'll gain broad industry skills for GenAI in productioneploy a Cyber Security Analyst agent with MCP to Azure & GCPStand up SageMaker inferenceBuild data ingest to S3 vectorsDeploy a Researcher Agent using OpenAI OSS models on Bedrock + MCPWeek 4You'll go fully agentic in production:Architect multi-agent systems with:Aurora Serverless, Lambda, SQSJWT-authenticated CloudFront frontendsLangFuse observabilityOverview of AWS Agent CoreBy the end, you'll know how toick the right architectureLock down securityMonitor costsDeliver continuous updatesEverything needed to run scalable, reliable AI apps in production.Course sections (Weeks & Projects)Week 1SaaS App Live in Production with Vercel, AWS, Next.js, Clerk, App RunnerProject: SaaS Healthcare AppWeek 2AI Platform Engineering on AWS with Bedrock, Lambda, API Gateway, Terraform, CI/CDProject: Digital Twin Mk IIWeek 3Gen AI in Production with Azure, GCP, AWS SageMaker, S3 Vectors, MCPProject: Cybersecurity AnalystWeek 4Agentic AI in Production: Build and deploy a Multi-Agent System on AWS (Aurora Serverless, Lambda, SQS),with LangFuse and Bedrock AgentCoreCapstone Project: SaaS Financial Planner

Who this course is for:
If you're excited about the idea of deploying Gen AI and Agents live in production - then this course is for you.

For More Courses Visit & Bookmark Your Preferred Language Blog
From Here:--------

RapidGator

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[0dayddl]

14.53 GB | 12min 39s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 - Day 1 - Instant AI Deployment Your First Production App on Vercel in Minutes.mp4 (147.38 MB)
2 - Day 1 - From Zero to Live Deploying Your First AI-Powered SaaS on Vercel.mp4 (94.35 MB)
3 - Day 1 - From AI Concepts to Cloud Deployment Navigating the DevOps Landscape.mp4 (174.27 MB)
4 - Day 1 - Course Overview Building Production AI Systems Across 4 Weeks.mp4 (130.71 MB)
5 - Day 1 - Deploy Your First Live AI App with OpenAI and Vercel Integration.mp4 (129.48 MB)
6 - Day 1 - Managing API Costs and Environment Setup for Production AI Systems.mp4 (201.52 MB)
7 - Day 1 - Course Expectations and Community Support for Production AI.mp4 (92.82 MB)
8 - Day 2 - Building Full-Stack AI Apps Frontend-Backend Architecture for LLMs.mp4 (129.37 MB)
9 - Day 2 - Building Full-Stack AI Apps with React, FastAPI, and NextJS.mp4 (215.49 MB)
10 - Day 2 - Building Your First Full-Stack AI SaaS with NextJS and FastAPI.mp4 (105.21 MB)
11 - Day 2 - Building Your First FastAPI Backend for Production LLM Deployment.mp4 (100.13 MB)
12 - Day 2 - Deploying Full-Stack AI Apps with Next js Frontend and FastAPI Backend.mp4 (116.51 MB)
13 - Day 2 - Adding Real-Time Streaming and Professional UI to Your LLM App.mp4 (126.55 MB)
14 - Day 3 - Adding User Authentication to Your Production AI Application.mp4 (143.74 MB)
15 - Day 3 - Adding User Authentication to Production AI Apps with Clerk.mp4 (112.57 MB)
16 - Day 3 - Adding Subscription Billing to Your Production AI SaaS Application.mp4 (87.84 MB)
17 - Day 3 - Adding Authentication and Billing to Production AI Applications.mp4 (135.28 MB)
18 - Day 4 - Building Your First Commercial AI App From Prototype to Business.mp4 (111.82 MB)
19 - Day 4 - Building Healthcare AI Apps with FastAPI and Structured Prompts.mp4 (92.22 MB)
20 - Day 4 - Deploying Your Complete AI Healthcare App to Production on Vercel.mp4 (73.92 MB)
21 - Day 4 - Building a Production Healthcare AI SaaS with Streaming LLMs.mp4 (93.3 MB)
22 - Day 5 - AWS Setup and IAM for Production AI Your First Cloud Deployment.mp4 (154.01 MB)
23 - Day 5 - Setting Up AWS Cost Monitoring for Production AI Deployments.mp4 (117.94 MB)
24 - Day 5 - Setting Up Secure IAM Users for Production AI Deployments on AWS.mp4 (141.03 MB)
25 - Day 5 - Containerizing AI Apps with Docker for Cloud Deployment.mp4 (159.82 MB)
26 - Day 5 - Migrating Your AI App from Vercel to AWS for Production Scale.mp4 (104.91 MB)
27 - Day 5 - Containerizing Your AI App Docker Images for Production Deployment.mp4 (106.59 MB)
28 - Day 5 - Deploying Dockerized AI Apps to AWS with ECR and App Runner.mp4 (145.38 MB)
29 - Day 5 - Deploying Your AI App Live on AWS App Runner with Auto-Scaling.mp4 (68.9 MB)
30 - Day 5 - From Vercel to AWS Deploying Production LLM Apps at Scale.mp4 (97.7 MB)
1 - Day 1 - AWS Foundations for Production AI From Console to Infrastructure.mp4 (185.49 MB)
2 - Day 1 - Cloud Deployment Architectures for Production AI Applications.mp4 (130.52 MB)
3 - Day 1 - AWS Cloud Components for Production AI S3, Lambda, and Bedrock.mp4 (115.95 MB)
4 - Day 1 - Building Your Digital Twin AWS Lambda + Bedrock Architecture Setup.mp4 (196.36 MB)
5 - Day 1 - Building Your AI Digital Twin Production Setup with NextJS App Router.mp4 (118.09 MB)
6 - Day 1 - Building Your First Full-Stack AI App with FastAPI and React.mp4 (132.8 MB)
7 - Day 1 - Building Conversational Memory for Production AI Chat Applications.mp4 (66.22 MB)
8 - Day 2 - Building Production-Ready AI Agents with AWS Lambda and S3.mp4 (171.75 MB)
9 - Day 2 - Migrating AI Chat Apps from Local Storage to AWS S3 and Lambda.mp4 (129.58 MB)
10 - Day 2 - Deploying Your First Production LLM API on AWS Lambda.mp4 (116 MB)
11 - Day 2 - Configuring AWS Lambda and S3 for Production LLM Memory Storage.mp4 (94.17 MB)
12 - Day 2 - Setting Up S3 Buckets and API Gateway for Production AI Apps.mp4 (154.17 MB)
13 - Day 2 - Deploying AI Frontend Through CloudFront for Global Distribution.mp4 (110.13 MB)
14 - Day 2 - Testing Your Live AI Agent and Configuring CORS for Production.mp4 (75.52 MB)
15 - Day 3 - Setting Up Amazon Bedrock for Production LLM Deployment on AWS.mp4 (145.07 MB)
16 - Day 3 - Migrating from OpenAI to AWS Bedrock for Cost-Effective LLM Deployment.mp4 (113.26 MB)
17 - Day 3 - Deploying Bedrock LLMs to AWS Lambda and Testing Production APIs.mp4 (64 MB)
18 - Day 3 - Monitoring Production AI with CloudWatch and Bedrock Metrics.mp4 (105.57 MB)
19 - Day 4 - Infrastructure as Code for AI Deploying LLM Apps with Terraform.mp4 (188.09 MB)
20 - Day 4 - Infrastructure as Code Automating AI Deployments with Terraform.mp4 (150.18 MB)
21 - Day 4 - Automating AI Deployments with Terraform and Shell Scripts.mp4 (111.34 MB)
22 - Day 4 - Automating Full-Stack AI Deployment with Terraform and AWS.mp4 (100.21 MB)
23 - Day 4 - Multi-Environment AI Deployments Dev, Test, and Production Setup.mp4 (135.12 MB)
24 - Day 4 - Testing Production AI Deployments and Terraform Cleanup Workflows.mp4 (123.44 MB)
25 - Day 5 - Automating AI Infrastructure Deployments with GitHub Actions CICD.mp4 (159.69 MB)
26 - Day 5 - Setting Up Git and GitHub Actions for AI Production Deployments.mp4 (113.77 MB)
27 - Day 5 - Setting Up GitHub Actions for Automated AI Model Deployment.mp4 (111.57 MB)
28 - Day 5 - Setting Up GitHub Actions for Automated AI Infrastructure Deployment.mp4 (105.13 MB)
29 - Day 5 - Setting Up GitHub Actions for Automated AI Agent Deployments.mp4 (110.68 MB)
30 - Day 5 - Live CICD Pipeline Deploy From Git Push to Production AI Agent.mp4 (136.62 MB)
31 - Day 5 - Automated CICD Pipelines for Production AI Apps with Git Deploy.mp4 (93.38 MB)
32 - Day 5 - Resource Management and Cost Control for Production AI Systems.mp4 (182.45 MB)
1 - Day 1 - Multi-Cloud AI Deployment Azure, GCP & Cybersecurity Agent Setup.mp4 (195.81 MB)
2 - Day 1 - Building AI Security Agents with MCP Servers and Semgrep Integration.mp4 (114.78 MB)
3 - Day 1 - Containerizing AI Agents with Docker for Cloud Deployment.mp4 (133.57 MB)
4 - Day 1 - Setting Up Azure Infrastructure for Production AI Container Deployment.mp4 (112.93 MB)
5 - Day 1 - Deploying AI Apps to Azure with Terraform Infrastructure as Code.mp4 (115.46 MB)
6 - Day 1 - Deploying AI Agents with MCP Servers to Azure Container Apps.mp4 (123.87 MB)
7 - Day 2 - Setting Up GCP Infrastructure for Production AI Agent Deployment.mp4 (135.29 MB)
8 - Day 2 - Setting Up Google Cloud CLI for Production AI Container Deployment.mp4 (32.33 MB)
9 - Day 2 - Deploying AI Agents to GCP Cloud Run with Terraform Infrastructure.mp4 (81.43 MB)
10 - Day 2 - Deploying AI Agents Across GCP and Azure with Container Services.mp4 (144.06 MB)
11 - Day 3 - Building ALEX Multi-Agent Financial AI System on AWS Infrastructure.mp4 (175.42 MB)
12 - Day 3 - Setting Up AWS Permissions and SageMaker for Production AI Agents.mp4 (107.74 MB)
13 - Day 3 - SageMaker vs Bedrock Deploying Custom AI Models in Production.mp4 (151.29 MB)
14 - Day 3 - Deploying SageMaker Embedding Models for Production RAG Systems.mp4 (112.7 MB)
15 - Day 3 - Exploring SageMaker AI's Full Platform for Production ML Workflows.mp4 (36.21 MB)
16 - Day 4 - Building Vector Data Pipelines with SageMaker and S3 for AI Memory.mp4 (143.71 MB)
17 - Day 4 - Building Cost-Effective Vector Storage with S3 and Lambda Ingestion.mp4 (77.46 MB)
18 - Day 4 - Setting Up Secure AI Ingestion Pipelines with Terraform and AWS.mp4 (90.16 MB)
19 - Day 4 - Testing Your AWS Lambda Vector Ingest Pipeline End-to-End.mp4 (74.34 MB)
20 - Day 5 - Building AI Research Agents with MCP Servers and Data Pipelines.mp4 (92.71 MB)
21 - Day 5 - Building AI Research Agents with Bedrock and OpenAI SDK on AWS.mp4 (91.53 MB)
22 - Day 5 - Deploying AI Research Agents with Docker, ECR, and App Runner.mp4 (115.48 MB)
23 - Day 5 - Testing End-to-End AI Agent Workflows from Research to Vector Storage.mp4 (92.15 MB)
24 - Day 5 - Automating AI Agent Workflows with AWS EventBridge Scheduling.mp4 (107.5 MB)
25 - Day 5 - Week 3 Wrap-Up Assignment Options & Production AI Next Steps.mp4 (152.86 MB)
1 - Day 1 - Multi-Agent vs Single-Agent Architectures for Production AI Systems.mp4 (122.89 MB)
2 - Day 1 - Building Multi-Agent Financial AI Database Architecture & AWS Setup.mp4 (175.28 MB)
3 - Day 1 - Database Architecture for Production AI Aurora Serverless for LLM Apps.mp4 (44.11 MB)
4 - Day 1 - Setting Up Aurora Serverless Database for Multi-Agent AI Systems.mp4 (120.33 MB)
5 - Day 1 - Setting Up Aurora Database Infrastructure for Production AI Apps.mp4 (74.86 MB)
6 - Day 1 - Setting Up Production Database Architecture for AI Agent Systems.mp4 (68.09 MB)
7 - Day 2 - Building Multi-Agent Financial AI Systems with Context Engineering.mp4 (110.88 MB)
8 - Day 2 - Setting Up AWS Bedrock Models and Enterprise APIs for AI Agents.mp4 (90.08 MB)
9 - Day 2 - Exploring Multi-Agent Architecture Tools and Structured Outputs.mp4 (63.31 MB)
10 - Day 2 - Building Multi-Agent Financial Systems Code Review and Architecture.mp4 (154.03 MB)
11 - Day 2 - Testing Multi-Agent Systems Locally Before Lambda Deployment.mp4 (162.79 MB)
12 - Day 2 - Packaging and Deploying Multi-Agent AI Systems to AWS Lambda.mp4 (141.37 MB)
13 - Day 2 - End-to-End Testing of Multi-Agent Systems on AWS Lambda.mp4 (53.15 MB)
14 - Day 3 - Building the Frontend for Your Production AI Agent System.mp4 (125.44 MB)
15 - Day 3 - Running Full-Stack AI Apps Locally Before Production Deployment.mp4 (84.78 MB)
16 - Day 3 - When AI Code Generation Works vs Fails in Production Apps.mp4 (123.05 MB)
17 - Day 3 - Deploying AI-Generated APIs to Production with AWS Lambda & Terraform.mp4 (89.93 MB)
18 - Day 3 - Testing Your Multi-Agent Financial AI System Live in Production.mp4 (115.02 MB)
19 - Day 4 - Enterprise-Grade AI Monitoring, Security & Observability at Scale.mp4 (159.09 MB)
20 - Day 4 - Enterprise-Grade AI Scaling, Security, and Monitoring for Production.mp4 (138.35 MB)
21 - Day 4 - Monitoring AI Agents in Production with CloudWatch and Dashboards.mp4 (154.4 MB)
22 - Day 4 - Monitoring AI Systems and Building Guardrails for Production Agents.mp4 (159.77 MB)
23 - Day 4 - Advanced LLM Observability with Langfuse and Production Guardrails.mp4 (117.83 MB)
24 - Day 4 - LLM-as-a-Judge Pattern with Langfuse Observability in Production.mp4 (142.7 MB)
25 - Day 4 - Real-Time Agent Monitoring and the Security Risks of Production AI.mp4 (149.57 MB)
26 - Day 4 - Securing AI Agents Against Prompt Injection in Production Systems.mp4 (154.56 MB)
27 - Day 4 - Capstone Assignment Taking Your AI Financial Agent to Market.mp4 (118.94 MB)
28 - Day 5 - Enterprise AI Guardrails and Wrapping Your Production Agent System.mp4 (113.73 MB)
29 - Day 5 - Agent Platforms vs Custom Deployment When to Use Managed Solutions.mp4 (173.28 MB)
30 - Day 5 - Building Production AI Agents with Amazon Bedrock AgentCore.mp4 (192.66 MB)
31 - Day 5 - Setting Up AWS Bedrock Agent Core for Production AI Deployments.mp4 (91.97 MB)
32 - Day 5 - Building and Deploying Your First AI Agent to AWS in Minutes.mp4 (121.28 MB)
33 - Day 5 - Building Production AI Agents with Loop-Based Reasoning Systems.mp4 (142.46 MB)
34 - Day 5 - Adding Code Execution Tools and Observability to AWS Bedrock Agents.mp4 (110.06 MB)
35 - Day 5 - Course Wrap-Up From Zero to Production AI Expert in 4 Weeks.mp4 (151.34 MB)

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