Udemy Amazon Bedrock with Amazon Q Developer - Zero to Hero Python

[0dayddl]

3.55 GB | 00:19:04 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 -Introduction.mp4 (23.9 MB)
1 -Introduction to Bedrock.mp4 (29.4 MB)
2 -PartyRock - Experience Bedrock like ChatGPT.mp4 (122.16 MB)
3 -Understanding UI.mp4 (76.29 MB)
4 -Enabling Model Access.mp4 (46.59 MB)
5 -Post Model Access.mp4 (13.74 MB)
6 -Can Amazon Q really help me in coding - HandsOn.mp4 (72.01 MB)
7 -Using Chat Playground.mp4 (46.12 MB)
8 -Using Text Playground.mp4 (32.76 MB)
9 -Using Image Playground.mp4 (28.11 MB)
1 -Understanding Q Developer.mp4 (26.12 MB)
2 -Functionality of Q Developer in Industry.mp4 (25.49 MB)
3 -Chatting with Amazon Q.mp4 (34.3 MB)
4 -Can we ask for code directly in Lambda.mp4 (81.2 MB)
1 -How to use Amazon Q in Lambda with Bedrock.mp4 (21.43 MB)
2 -Bedrock with Text Model - Amazon Titan HandsOn.mp4 (90.2 MB)
3 -Bedrock with Text Model - Claude Sonnet HandsOn.mp4 (95.64 MB)
4 -Bedrock with Text Model - Llama HandsOn.mp4 (82.36 MB)
5 -How to troubleshoot errors if occurred in code.mp4 (56.71 MB)
6 -Bedrock with Image Model - Titan Image HandsOn.mp4 (179.16 MB)
7 -Bedrock with Image Model - Stability ai.mp4 (77.05 MB)
1 -What is AI.mp4 (41.26 MB)
2 -What is Generative AI.mp4 (24.76 MB)
3 -Opportunities available in GenAI.mp4 (24.33 MB)
4 -AI, GenAI, ML, Deep Learning Relation.mp4 (33.08 MB)
5 -FM vs LLM.mp4 (29.81 MB)
6 -Understanding Prompt Engineering.mp4 (25.36 MB)
7 -Types of Prompt Engineering.mp4 (13.09 MB)
1 -Understanding Inference Parameters.mp4 (51.6 MB)
10 -Top K Code With Q developer - II HandsOn.mp4 (58.22 MB)
11 -Most Probable Solution Hands-On.mp4 (52 MB)
12 -Troubleshooting Scenario- Access Denied Error.mp4 (36.12 MB)
13 -Troubleshooting Scenario- Timeout Error.mp4 (56.31 MB)
14 -Troubleshooting Scenario- Validation Exception.mp4 (61.49 MB)
15 -Troubleshooting Scenario- ModelAccess Error.mp4 (72.75 MB)
2 -Temperature Hands-On.mp4 (43.65 MB)
3 -Temperature Code With Q developer - I HandsOn.mp4 (66.85 MB)
4 -Temperature Code With Q developer - II HandsOn.mp4 (62.38 MB)
5 -Top P Hands-On.mp4 (38.04 MB)
6 -Top P Code With Q developer - I HandsOn.mp4 (83.02 MB)
7 -Top P Code With Q developer - II HandsOn.mp4 (82.37 MB)
8 -Top K Hands-On.mp4 (39.41 MB)
9 -Top K Code With Q developer - I HandsOn.mp4 (67.7 MB)
1 -Additional Configurations.mp4 (41.67 MB)
2 -System Prompt.mp4 (31.82 MB)
3 -Code for System Prompt with Amazon Q Dev.mp4 (72.04 MB)
4 -Maximum Length.mp4 (23.63 MB)
5 -Code for Maximum Length with Amazon Q Dev.mp4 (89.15 MB)
6 -Stop Sequence.mp4 (24.33 MB)
7 -Code for Stop Sequence with Amazon Q Dev.mp4 (64.82 MB)
1 -Understanding Safeguards.mp4 (30.39 MB)
2 -Guardrails SetUp.mp4 (64.71 MB)
3 -Using Guardrails.mp4 (23.74 MB)
4 -Watermark Detection.mp4 (34.33 MB)
1 -Understanding Builder Tools.mp4 (29.68 MB)
10 -Defining Agents Manually.mp4 (75.96 MB)
11 -Adding Action Groups & Guardrails in Agents.mp4 (51.14 MB)
12 -Understanding Prompt Flow.mp4 (42.46 MB)
13 -Prompt Flow with KB.mp4 (44.59 MB)
14 -PromptFlow with Prompt Management.mp4 (30.7 MB)
15 -PromptFlow with Prompt Creation & KB.mp4 (70.57 MB)
16 -PromptFlow with Condition.mp4 (49.56 MB)
2 -Prompt Management.mp4 (65.76 MB)
3 -Knowledge Base - Using Root User.mp4 (65.06 MB)
4 -IAM Users & Roles.mp4 (42.93 MB)
5 -IAM Policy.mp4 (26.69 MB)
6 -Knowledge Base - Using IAM User.mp4 (71.83 MB)
7 -Understanding Agents.mp4 (24.53 MB)
8 -Agents Walkthrough.mp4 (34.88 MB)
9 -Creating Agents using Assistant.mp4 (81.66 MB)

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[0dayddl]

Amazon Bedrock With Amazon Q Developer-Zero To Hero | Python
Last updated 11/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 2.67 GB | Duration: 7h 3m​

Code Amazon Bedrock with Amazon Q Developer in Lambda using Python (Boto3). Use Q as Co-Pilot to code. Novice to Expert

What you'll learn
Master the fundamentals of generative AI and its business applications
Navigate the Amazon Bedrock console with confidence
Master Amazon Q Developer setup and integration
Create custom images with AI using simple text prompts
Optimize AWS service utilization with Amazon Q
Develop effective code generation skills using AI
Optimize code performance with Amazon Q insights
Improve collaborative development workflows
Implement responsible AI practices and ethical considerations
Complete hands-on, real-world AI projects using Amazon Bedrock

Requirements
Basic Understanding of Coding - Python is required
From Basics to Details: We start from the fundamentals of AI/ GenAI/ AWS / Amazon Q, explaining core concepts and terminology in a clear and concise manner.
An AWS account
Basic understanding of cloud computing concepts (helpful but not mandatory)
Enthusiasm to learn about AI and its practical applications
Hands-On Learning: Hands-on labs and exercises are provided throughout the course to reinforce learning and allow you to practice what you've learned in a real-world environment.

Description
Master Generative AI Development with Amazon Bedrock & Amazon QCourse OverviewDive into the cutting-edge world of generative AI development using Amazon's latest tools - Amazon Bedrock and Amazon Q. This comprehensive course will teach you how to build, deploy, and optimize AI-powered applications using Amazon's most advanced AI services.What You'll LearnSet up and configure Amazon Bedrock for AI model deploymentIntegrate foundation models like Claude, Llama 2, and Amazon TitanDevelop with Amazon Q's AI-assisted coding capabilitiesBuild production-ready applications using AWS AI servicesImplement best practices for prompt engineering and AI safetyCreate scalable and cost-effective AI solutionsCourse ContentSection 1: Getting Started with Amazon BedrockIntroduction to Amazon Bedrock architectureSetting up your development environmentUnderstanding foundation models and their capabilitiesAPI integration and authenticationSection 2: Building with Foundation ModelsText generation and completionImage generation and manipulationCode generation and optimizationFine-tuning models for specific use casesSection 3: Amazon Q Developer ExperienceAI-assisted code developmentCode review and optimizationDocumentation generationSecurity best practices implementationSection 4: Inference Parameters Code with Q for BedrockBuilding a code with AI assistantCreating an AI-powered content generatorDeveloping an image generation applicationImplementing a code refactoring systemSection 5: Additional Configuration for ModelsSystem PromptsMax LengthStop SequenceGuardrails and Builder ToolsPrerequisitesBasic understanding of Python programmingFamiliarity with AWS servicesAWS account with appropriate permissionsWho This Course is ForSoftware developers looking to integrate AI into their applicationsCloud engineers wanting to expand their AWS AI expertiseTechnical leads evaluating AI solutions for their organizationsDevOps engineers interested in AI infrastructure

Who this course is for:
For Beginners: Individuals with little to no experience in AI/ GenAI/ AWS / Amazon Q. Fresh graduates or students looking to kickstart their careers in AI/ GenAI/ AWS / Amazon Q or software development.,Everyone can pick up this course, at their own pace.,For Intermediate Users: Professionals who have some familiarity with AI but want to deepen their understanding and skills. Developers or sysadmins who have worked with AI in basic capacities but seek to expand their knowledge and capabilities.,For Advanced Users: Experienced AI engineers, software architects, or team leads who want to refine their AI skills and stay updated with the latest best practices.,For Career Changers: Individuals transitioning from other IT roles (such as system administration, software development, or quality assurance) to AI / GenAI / Amazon Q

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