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Agentic AI Playbook: Complete Guide for Tech Leaders

School of Devops

4.3(3.0K students)

Self-paced

All Levels

About this course

This course contains the use of artificial intelligence for generating the slides based content. Are you leading product teams or innovation initiatives and wondering how AI agents like AutoGPT, CrewAI, or LangGraph actually work—and how they apply to your world? This course offers a practical, non-technical guide designed specifically for product managers, tech leads, and strategy heads who want to turn AI buzzwords into real impact.

Agentic AI is more than just chatbots and copilots. It represents a major shift in how software operates—from reactive prompts to autonomous, goal-seeking agents that can plan, decide, and act across tools and systems. This course provides a strategic roadmap to help you understand, evaluate, and implement Agentic AI in your products, platforms, and workflows—without needing to write a single line of code.

In this six-week structured guide, you'll gain clarity on:What Agentic AI is and how it differs from traditional AI and LLM-based copilotsKey agent components: tools, memory, goals, reflectionDesign patterns: single-shot, reactive, planning-based, multi-agent systemsThe agent ecosystem: LangGraph, BeeAI, CrewAI, AutogenTool execution with plugins, APIs, and MCPsUnderstanding GPT-4o, Claude, and open-source or local LLM optionsNo-code and low-code platforms for agent buildingInternal agents for research, data synthesis, and roadmappingCustomer-facing agents for onboarding, demos, and supportAgents for DevOps, data monitoring, and documentationFrameworks like the Use Case Canvas to identify agent opportunitiesSingle vs multi-agent architectures and when to use themOrchestration models: parallel, sequential, hierarchicalAgent roles: researcher, coder, planner, criticIntegrations with tools like Slack, Notion, CRMs, GitHubPlanning pilot projects with measurable KPIsDefining team roles and readinessLegal, ethical, and governance frameworks for agentic systemsScaling your agentic stack responsiblyYou’ll walk away with a reusable playbook and templates to start building agent strategies for your team or product. This course is designed for strategic, cross-functional professionals who want to lead AI transformation, including:Product managers designing AI-powered features or workflowsTech leads aiming to incorporate agents into systems and architectureInnovation heads exploring scalable use casesFounders and entrepreneurs building AI-first startups or internal toolsConsultants and coaches helping organizations navigate the AI landscapeWhether you're experimenting or scaling, this course will help you think clearly, act effectively, and lead confidently. Unlike most technical AI courses, this one is designed specifically for decision-makers and product leaders who want to:Understand agentic AI without writing codeIdentify real use cases, not just watch demosMake smart build vs buy vs partner decisionsAlign agent capabilities with product, design, and business goalsBuild cross-functional alignment around risks, compliance, and scalingEach module is packed with clear visuals, frameworks, and strategic insights that you can immediately apply to product conversations, planning sessions, and stakeholder meetings.

No coding is required. No AI background is assumed. All you need is curiosity about the next frontier of AI, familiarity with how product or tech teams operate, and the desire to shape the future of intelligent products.

The course includes six module-based lessons, templates such as the Use Case Canvas and Agent Strategy Playbook, and a capstone project to create your own AI adoption strategy. Agentic AI is already reshaping how software is being designed and delivered. This course will help you prepare for it—and lead it.

Enroll now to start building your Agentic AI advantage.

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Level: All Levels

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Duration: Self-paced

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Instructor: School of Devops

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Learn Python From Scratch I’ve created thorough, extensive, but easy to follow content which you’ll easily understand and absorb. The course starts with the basics, including Python fundamentals, programming, and user interaction.The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer. We will start from the very beginning by teaching you Python basics and programming fundamentals, and then going into advanced topics and different career fields in Python so you can get real-life practice and be ready for the real world.The topics covered in this course are:Array implementationFile methodsKeywords and IdentifiersPython TuplesPython BasicsPython FundamentalsData StructuresObject-Oriented Programming with PythonFunctional Programming with PythonLambdasDecoratorsGeneratorsTesting in Python Debugging Error Handling Regular Expressions Comprehensions ModulesSee you inside the course!

Building Data Warehouse with Snowflake, Python and SQL

Christ Raharja

This course contains the use of artificial intelligenceDisclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.Welcome to Building Data Warehouse with Snowflake, Python and SQL course. This is a comprehensive project based course where you will learn how to build a fully functioning data warehouse using Snowflake and implement dimensionality modelling. This course is a perfect combination between Snowflake and Python, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in data engineering. In the introduction session, you will learn the basic fundamentals of data warehouse and snowflake, such as getting to know about data warehouse architecture and learning about the difference between online transaction processing and online analytic processing. Then, in the next section, we will download grocery inventory and restaurant delivery datasets from Kaggle, it is a platform that offers many high quality datasets from various industries. Afterward, we will set up a Snowflake account, learn how to navigate the Snowflake dashboard, and load the grocery inventory datasets. Then, after that, we are going to clean the dataset by removing missing values and duplicates. The objective is to make sure the data is valid and ready to use. Following that, we will learn about basic sql concepts needed for data warehousing, for example, we are going to learn how to use select, aggregate functions, where, having, order by, case, group by, distinct, window functions, and common table expressions. Then, we will run these SQL queries to analyze grocery inventory data and extract meaningful insights from the data. Then, in the next section, we are going to transform and load data using SQL, where we will prepare and organize the data before building the data warehouse. In addition, we will also perform DataFrame operations using Snowpark like filtering, selecting, grouping, and transforming data. Then, after that we are going to perform dimensionality modelling, specifically we will create fact tables, dimension tables, and define relationships between those tables. The objective is to organize our data efficiently for reporting, analysis, and business intelligence. Once the data is ready, we are going to build a basic grocery inventory data warehouse using Snowflake and SQL. This data warehouse can be used for analyzing inventory levels, product availability, stock movements, and overall inventory performance. Then, in the next section, we will learn about data warehouse security and access control, specifically we will learn how to manage user roles, permissions, and access privileges in Snowflake. Additionally, we are also going to build a real world project, where we are going to build a restaurant delivery data warehouse. We will use restaurant delivery data to analyze orders, delivery performance, customer behavior, and sales trends. Lastly, at the end of the course, we are going to build a machine learning model to predict delivery time using data from our data warehouse.First of all, before getting into the course, we need to ask these questions to ourselves, why should we build data warehouse and why should we use Snowflake? Well, here is my answer, By having a data warehouse, it will help us to centralize and organize data from multiple sources, making it easier to analyze and generate insights. In addition, Snowflake enables us to efficiently store, process, and analyze large amounts of data in a cloud based environment.Below are things that you can expect to learn from this course:Learn the basic fundamentals of data warehouse and SnowflakeLearn how to find and download datasets from KaggleLearn how to create Snowflake account and load dataset into SnowflakeLearn how to clean data by removing missing values and duplicatesLearn how to use select, where, distinct , order by, aggregate functions in SQLLearn how to use group by, case, having, common table expression, and window function in SQLLearn how to perform data transformation using SQLLearn how to perform DataFrame operation using SnowparkLearn about dimensional modelling and star schema conceptsLearn how to build grocery inventory data warehouse using SnowflakeLearn how to test grocery inventory data warehouseLearn how to manage data warehouse security and access controlLearn how to load and transform restaurant delivery using SnowparkLearn how to build restaurant delivery data warehouse using Snowflake;Learn how to analyze restaurant delivery data using SQLLearn how to build machine learning model for predicting delivery time

AWS Certified DevOps Engineer Prep

Udemy Instructor

Prepare for the AWS Certified DevOps Engineer - Professional exam with 600 scenario-based practice questions spread across six full-length tests, one for each official exam domain.This course goes beyond simple definitions. Every question is built around a realistic, workplace-style scenario - the kind of applied reasoning the actual exam demands - and every answer choice comes with its own explanation, so you understand exactly why an option is right or wrong, not just which one to pick.What's covered:Test 1 - SDLC Automation: CodePipeline, CodeBuild, CodeDeploy, CodeCommit, deployment strategies, and CI/CD pipeline design.Test 2 - Configuration Management & Infrastructure as Code: CloudFormation, Systems Manager, AWS Config, container orchestration with ECS and EKS, and secrets management.Test 3 - Resilient Cloud Solutions: Auto Scaling, load balancing, Route 53 routing, multi-region disaster recovery, and chaos engineering.Test 4 - Monitoring & Logging: CloudWatch, CloudTrail, X-Ray, centralized logging, and proactive, machine-learning-driven observability.Test 5 - Incident & Event Response: EventBridge, SNS, Systems Manager Incident Manager, automated remediation, and blameless postmortems.Test 6 - Security & Compliance: IAM, KMS, GuardDuty, Security Hub, and continuous, automated compliance verification.Each test builds toward the applied, tradeoff-driven thinking the exam actually rewards - not rote memorization. Whether you're prepping for exam day or deepening your day-to-day DevOps practice on AWS, this course gives you the realistic repetition to build genuine, lasting confidence.Enroll today and start building the judgment this exam - and the job - actually tests.

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Agentic AI Playbook: Complete Guide for Tech Leaders

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