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Learn how to integrate AI tools like Amazon Bedrock into AWS DevOps workflows. This specialization covers CI/CD pipelines, infrastructure as code, and AI-driven deployment strategies — essential skills for modern DevOps engineers.
Master AI-powered operations with AWS. This course teaches how to use AI for anomaly detection, predictive analytics, and incident management in DevOps — key skills for modern infrastructure teams.
Advanced Google Cloud architecture focusing on AI, security, and operations. Learn Kubernetes, IaC (Terraform), generative AI agents, and cloud-native architecture for production systems.
This intermediate video helps ML Engineers in Tech learn How I use LLMs with a practical focus on understanding modern LLM workflows, tool use, and practical AI engineering patterns. By the end, learners will have a clearer understanding of how to use LLMs, AI Tools, ChatGPT to save time, improve quality, and complete common ML Engineer tasks with more confidence.
This intermediate crash course teaches QA Engineers how to use ChatGPT for test case drafting, test data generation, Selenium support, and API testing prompts. You move through a practical tester workflow with checkpoints after prompt setup, test design, automation drafting, and review so you can track progress as you go. The course also includes a pass/fail skills check built around real QA tasks: produce usable test cases, generate realistic data, improve automation drafts safely, and verify AI output before it reaches production. By the end, you will know where ChatGPT speeds up testing work and where human judgment still matters.
Design enterprise security with zero-trust principles. Master identity management, multi-factor authentication, Azure AD, and AI-enabled security for cloud infrastructure.
This intermediate video helps Systems Administrators in Tech learn Linux Troubleshooting with AI: Practical Tips Every Admin Should Know with a practical focus on troubleshooting systems, understanding technical concepts, and automating admin tasks. Strong practical focus on real sysadmin workflows. By the end, learners will have a clearer understanding of how to use AI, Linux, Troubleshooting to save time, improve quality, and complete common Systems Administrator tasks with more confidence.
This intermediate course helps DevOps Engineers in Tech learn Get started with AI-assisted development with a practical focus on applying GitHub Copilot, Code Completion, Unit Testing to everyday work. Official Microsoft Learn content, 2026. By the end, learners will have a clearer understanding of how to use GitHub Copilot, Code Completion, Unit Testing to save time, improve quality, and complete common DevOps Engineer tasks with more confidence.
This hands-on Microsoft Learn module teaches account executives and B2B sales professionals how to use Microsoft 365 Copilot to accelerate deal cycles from research to close. You will learn to analyze competitors with Copilot's Researcher agent, conduct market research with Copilot Chat, and draft compelling sales documents in Word with personalized outreach emails in Outlook. Two exercises walk through building a full RFP response workflow—creating reusable response templates in Word, analyzing historical bid data in Excel, and automating sales document drafts with a Copilot Studio agent. By the end, you will have a repeatable AI-assisted process for turning competitive intelligence into winning bids faster.
Learn how to use ChatGPT to speed up onboarding plans, QBR prep, renewal notes, and customer research. This resource is useful for customer success managers who need better follow-up, clearer communication, and faster ways to turn customer notes into action.
Master infrastructure-as-code with Terraform and cloud-native patterns. Learn Kubernetes hardening, DevSecOps, and generative AI integration for modern cloud deployments.
A 63-minute end-to-end tutorial showing how to use ChatGPT and GitHub Copilot throughout a real data science project. You'll follow along as an experienced data scientist builds a full workflow — from setting up a Conda environment and loading data, through cleaning, visualization, feature engineering and selection, to training and evaluating machine learning models. By the end, you'll have a practical, AI-assisted approach you can apply immediately to your own data science work.
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