Development100% OFF

Generative AI Engineer Masterclass

Udemy Instructor

0(2 students)

Self-paced

All Levels

About this course

Build a practical foundation in generative AI engineering by creating Python applications that combine language models, document retrieval, tools, and image inputs. The Generative AI Engineer Masterclass is designed for beginners, students, aspiring AI engineers, and developers who want hands-on experience building AI applications. No previous AI experience is required.

The course begins with development setup and Python essentials before introducing the components of a generative AI application. You will learn how to write clear prompts, guide responses with examples, and validate structured outputs. You will explore document chunking, embeddings, vector storage, and retrieval-augmented generation, then build a document question-answering assistant that provides source references and handles missing evidence.

As you progress, you will add a calculator tool, validate its arguments, and understand how bounded agent workflows control execution. You will also prepare image inputs, explore image-description workflows, and learn to distinguish visible observations from uncertain interpretations. The course covers how to choose between prompting, RAG, and fine-tuning.

You will practice preparing consistent training examples, separating datasets, and checking for duplicate prompts without submitting a paid training job. Through the course’s practical exercises, you will:Build a simple chatbot and structured information extractor. Retrieve relevant passages from course documents.

Generate answers supported by available evidence. Validate calculator operations and image inputs. Evaluate answers and investigate failures.

Connect features into a final assistant project. Explore web interfaces, environment configuration, and deployment considerations. Learning resources include runnable Python reference code, guided labs, quizzes, worksheets, and an HTML hands-on workbook covering all ten sections.

Core exercises run offline without paid API access. These examples use scripted chatbot responses, lexical retrieval, and recorded image fixtures to make application behavior easy to inspect. Optional live exercises introduce model-powered workflows and may incur API charges.

By the end of the course, you will be able to explain, build, test, and extend the core workflows of a beginner generative AI assistant.

Skills you'll gain

Data ScienceEnglish

Available Coupons

Course Information

Level: All Levels

Suitable for learners at this level

Duration: Self-paced

Total course content

Instructor: Udemy Instructor

Expert course creator

This course includes:

📹Video lectures
📄Downloadable resources
📱Mobile & desktop access
🎓Certificate of completion
♾️Lifetime access

$0$98.99

Save $98.99 today!

Enroll Now - Free

Redirects to Udemy • Limited free enrollments

Share this course

https://freecourse.io/courses/generative-ai-engineer-masterclass

This course contains the use of artificial intelligence.The Complete AI Engineer Bootcamp 2026 helps you take your first steps into AI engineering through clear explanations, practical examples, and guided projects. No previous AI or Python experience is required.You’ll begin with the Python essentials needed to build AI applications, including variables, functions, file handling, and JSON. Then you’ll explore how large language models generate responses, what tokens and context windows mean, and why fluent answers still need verification.As you progress, you’ll learn to write effective prompts, extract information into structured formats, and connect Python applications to model APIs. You’ll also practice managing conversation history and handling common errors.The course introduces retrieval-augmented generation, or RAG, through a document-based assistant. You’ll learn how to split documents into chunks, retrieve relevant passages, understand embeddings, and display source references that users can inspect.Next, you’ll add a calculator tool with input validation and controlled execution. You’ll test your assistant using supported questions, missing-information cases, and invalid inputs before connecting it to a simple local web interface.Your final project brings these skills together in a study assistant that answers questions from supplied notes, shows supporting passages, and performs explicit calculations.The course includes seven guided labs, downloadable Python projects, practice starters, completed reference code, quizzes, prompt templates, and a final-project checklist. Free offline demonstrations let you practice without an API key. Optional live API exercises require your own account and key, and usage may incur separate charges.With approximately 4 hours and 40 minutes of planned lessons and guided demonstrations, this bootcamp gives beginners a focused introduction to building and evaluating AI applications. Independent practice and quizzes take additional time.

Forward Deployed Architect: Enterprise Architecture

Udemy Instructor

“This course contains the use of artificial intelligence”Become a Forward Deployed Architect (FDA) capable of designing modern, scalable, secure, and production-ready enterprise solutions. This comprehensive course teaches you how to work at the intersection of business strategy, enterprise technology, solution architecture, data, cloud, security, AI, and engineering. You will learn how architects work directly with customers and engineering teams to understand complex business problems, evaluate existing technology environments, make critical architecture decisions, and translate business requirements into practical technical solutions.The role of a Forward Deployed Architect goes beyond creating architecture diagrams. Forward-deployed architects work closely with customers, stakeholders, engineers, product teams, security teams, and business leaders throughout the entire solution lifecycle. In this course, you will learn how to conduct customer discovery, identify business objectives, gather functional and non-functional requirements, analyze technical constraints, assess existing systems, and design architectures that can successfully move from concept to production.You will begin by understanding the fundamentals of Forward Deployed Architecture and how the role differs from a Forward Deployed Engineer, Solutions Architect, Enterprise Architect, and Software Architect. You will explore the complete architecture lifecycle, including discovery, requirements gathering, environment assessment, architecture design, technical validation, prototyping, MVP development, production deployment, adoption, and continuous improvement.The course then builds a strong foundation in Enterprise Architecture and modern enterprise technology environments. You will learn how enterprise applications, operational systems, databases, APIs, SaaS platforms, data warehouses, data lakes, lakehouses, cloud infrastructure, legacy systems, and AI platforms work together. You will also learn important architecture principles such as separation of concerns, loose coupling, modularity, abstraction, stateless and stateful design, synchronous and asynchronous communication, microservices, monoliths, and event-driven architecture.A major part of the course focuses on architecture discovery and requirements engineering. You will learn how to conduct stakeholder interviews, understand current workflows, identify pain points, document assumptions and constraints, and translate business needs into technical requirements. You will explore performance, scalability, availability, security, compliance, integration, and data requirements, along with techniques for defining acceptance criteria and prioritizing competing requirements.You will also learn how to perform a structured current-state architecture assessment. This includes creating system inventories, mapping application landscapes, documenting integrations and data flows, identifying system dependencies, recognizing bottlenecks and technical debt, evaluating architecture risks, and performing gap analysis between the current environment and the desired future state.From there, you will move into end-to-end solution architecture design. You will learn how to translate a business problem into a complete technical architecture covering the application, data, integration, security, and infrastructure layers. You will learn how to define system boundaries, assign component responsibilities, design target-state architectures, and evaluate architectural alternatives.Because architecture is fundamentally about making informed decisions, the course provides extensive coverage of architecture tradeoffs and decision-making. You will examine decisions involving cost versus performance, speed versus maintainability, build versus buy, cloud versus on-premises, batch versus real-time, SQL versus NoSQL, and monoliths versus microservices. You will also learn how to document important decisions using Architecture Decision Records (ADRs) and communicate the reasoning behind those decisions.The course provides dedicated coverage of Data Architecture and Integration Architecture. You will learn about operational and analytical data systems, databases, warehouses, data lakes, lakehouses, data products, data domains, metadata, lineage, ownership, and data quality. You will then explore modern data pipeline architecture, including ETL, ELT, batch processing, streaming, orchestration, transformation layers, validation, data contracts, monitoring, and pipeline reliability.You will also develop a strong understanding of API and enterprise integration architecture. Topics include REST APIs, API gateways, authentication, synchronous and asynchronous integrations, message queues, event buses, webhooks, enterprise integration patterns, legacy system integration, and integration failure handling. These concepts will help you design architectures that connect complex enterprise systems reliably and securely.Modern architects must understand the cloud, so the course covers essential Cloud Architecture concepts, including IaaS, PaaS, SaaS, compute, storage, networking, managed services, containers, serverless computing, hybrid architecture, and multi-cloud environments. You will learn how to design scalable and reliable systems using horizontal and vertical scaling, load balancing, caching, replication, redundancy, fault tolerance, disaster recovery, and resilience patterns.Security is integrated throughout the architecture lifecycle. You will learn the principles of Security by Design, including authentication, authorization, Identity and Access Management (IAM), Role-Based Access Control (RBAC), least privilege, secrets management, encryption, network security, Zero Trust, audit logging, and security boundaries. You will also explore enterprise governance and compliance, including data governance, privacy, data classification, retention, regulatory requirements, auditability, architecture controls, and risk assessment.A major focus of this course is modern AI Solution Architecture. You will learn the foundations of enterprise AI/ML architecture, including training and inference, model serving, feature pipelines, model APIs, AI platforms, human-in-the-loop workflows, and AI monitoring. You will then explore Generative AI architecture, including Large Language Models (LLMs), LLM APIs, prompt layers, embeddings, vector databases, Retrieval-Augmented Generation (RAG), document ingestion, retrieval systems, LLM gateways, guardrails, and enterprise knowledge systems.The course also introduces the rapidly evolving field of Agentic AI Architecture. You will learn how AI agents interact with tools and enterprise systems through concepts such as function calling, agent workflows, memory, state, Model Context Protocol (MCP), agent orchestration, multi-agent systems, human approval mechanisms, and agent security. The emphasis is on understanding how these capabilities fit into larger enterprise architectures rather than treating AI as an isolated technology.Designing an architecture is only part of the job. You must also understand how to move solutions from prototype to production. You will learn about MVP architecture, production readiness, environment strategies, configuration management, CI/CD, Infrastructure as Code (IaC), release strategies, rollback mechanisms, and production readiness checklists. You will also study observability and reliability, including logs, metrics, traces, monitoring, alerting, health checks, SLIs, SLOs, error budgets, incident response, and operational dashboards.Strong communication is one of the most important skills for a Forward Deployed Architect. This course teaches you how to create and communicate architecture diagrams using context diagrams, container diagrams, component diagrams, the C4 Model, data-flow diagrams, sequence diagrams, integration diagrams, and deployment diagrams. You will learn how to present architecture differently to technical teams, executives, customers, and business stakeholders, explain tradeoffs, communicate risks, handle objections, conduct architecture walkthroughs, and present technical recommendations clearly.You will also learn how to produce professional architecture documentation, including Solution Architecture Documents, Architecture Decision Records, requirements traceability, interface documentation, data contracts, assumptions, constraints, risk documentation, and operational runbooks. The goal is to ensure architecture decisions remain understandable and useful throughout the lifecycle of a solution.Because Forward Deployed Architects operate close to customers and implementation teams, you will develop practical skills for customer-facing technical leadership. You will learn how to build trust with stakeholders, translate between business and technology, collaborate with engineers and product teams, work with security organizations, manage competing requirements, negotiate technical decisions, handle escalations, and establish clear decision ownership.You will then examine how architecture evolves throughout the delivery lifecycle, from discovery and initial design through prototyping, MVP implementation, testing, production deployment, adoption, and continuous improvement. You will learn how to conduct architecture reviews, design reviews, security reviews, and performance reviews, identify technical risks and dependencies, develop mitigation strategies, assess architecture readiness, and support informed go/no-go decisions.Finally, you will bring everything together through an end-to-end capstone project that simulates a real Forward Deployed Architect engagement. You will receive a fictional enterprise customer scenario and develop the architecture from initial discovery through final proposal. You will create a business problem definition, stakeholder map, system inventory, functional requirements, non-functional requirements, constraints, success criteria, current-state architecture, target-state architecture, data architecture, integration architecture, security architecture, AI architecture, and deployment architecture.For the final deliverable, you will develop a complete Solution Architecture proposal and implementation roadmap. This will include architecture diagrams, key architecture decisions, Architecture Decision Records, a risk register, MVP architecture, production architecture, implementation phases, an architecture roadmap, and an executive architecture presentation. By completing this process, you will understand not only individual architecture concepts but how they connect during a realistic enterprise engagement.By the end of this course, you will have a structured framework for approaching complex enterprise architecture problems from discovery to production. Whether you are preparing for a role as a Forward Deployed Architect, Solutions Architect, Enterprise Architect, Cloud Architect, Data Architect, AI Architect, Technical Consultant, or senior technical leader, this course will help you build the architecture knowledge, decision-making framework, communication skills, and delivery mindset required to design and guide modern enterprise solutions.

AI-Powered SOC Analyst Bootcamp: AI Security Operations

Udemy Instructor

“This course contains the use of artificial intelligence”Step into the future of cybersecurity with AI-Powered SOC Analyst Bootcamp, a practical, hands-on course designed to teach you how Artificial Intelligence is transforming modern Security Operations Centers (SOCs).Cybersecurity teams today face an overwhelming volume of security alerts, logs, phishing attempts, suspicious activity, vulnerabilities, and potential incidents. Traditional manual investigation methods can be slow and resource-intensive. Modern SOC analysts are increasingly using Generative AI and AI-assisted workflows to analyze information faster, reduce repetitive work, improve investigations, and make better security decisions.In this course, you'll learn how to combine essential SOC Analyst skills with powerful AI tools such as ChatGPT, Claude, Gemini, and Microsoft Copilot. You'll discover how AI can act as a security copilot—helping you analyze logs, investigate alerts, research threats, correlate indicators, document incidents, build playbooks, and accelerate common SOC workflows.The course begins with the foundations of Security Operations. You'll learn how a SOC works, understand the responsibilities of Tier 1, Tier 2, and Tier 3 SOC Analysts, explore the security monitoring lifecycle, and understand how analysts move from an incoming alert to investigation and incident response.You'll then build a strong foundation in security logs and event analysis. You'll work with concepts involving Windows Event Logs, Linux authentication logs, firewall logs, DNS logs, web server logs, authentication events, and cloud audit logs. You'll learn how analysts identify suspicious patterns and correlate events across different data sources.You'll also explore SIEM (Security Information and Event Management) fundamentals, including log collection, correlation rules, alert generation, dashboards, and security monitoring. The course introduces major SIEM technologies such as Splunk, Microsoft Sentinel, Elastic Security, and IBM QRadar, giving you the foundational knowledge needed to understand how enterprise security monitoring environments operate.From there, you'll move into AI-assisted security investigations.You'll learn how to use AI to analyze large collections of security events, identify failed login patterns, suspicious authentication activity, privilege escalation, lateral movement, and other indicators of compromise. You'll also learn how to design effective security prompts that provide AI systems with the right context while maintaining appropriate human oversight.A major part of the course focuses on phishing investigation. You'll examine concepts including email headers, SPF, DKIM, DMARC, suspicious URLs, attachments, sender behavior, and social-engineering indicators. You'll use AI to help organize evidence, summarize suspicious emails, and develop structured risk assessments.You'll then explore Threat Intelligence and Threat Hunting. You'll learn about Indicators of Compromise (IOCs), CVEs, malware families, threat actors, and the MITRE ATT&CK framework. You'll discover how AI can accelerate threat research, generate hunting hypotheses, correlate evidence, organize investigation timelines, and assist analysts during complex investigations.The course also takes you through a complete AI-assisted incident investigation workflow. You'll learn how analysts perform alert validation, root-cause analysis, timeline reconstruction, user activity analysis, system-impact assessment, containment, eradication, recovery, and lessons learned.You'll put these skills into practice through a ransomware investigation project, where you'll approach the incident as a SOC analyst and use AI as an investigation assistant.But investigating incidents is only part of the job.Modern security teams must also communicate their findings effectively. You'll learn how AI can assist with incident documentation, technical reports, executive summaries, evidence organization, incident timelines, and MITRE ATT&CK mapping. You'll build an AI-assisted Incident Report Generator capable of transforming investigation findings into structured security documentation.You'll also learn how to build reusable AI-powered Incident Response Playbooks. These playbooks can help analysts standardize investigation procedures, decision points, response actions, documentation requirements, and escalation processes.One of the most exciting sections of the course focuses on building your own AI SOC Analyst Assistant.You'll learn how to design an AI assistant around real SOC workflows using prompt engineering, structured investigation templates, knowledge bases, reusable prompts, and security procedures. Instead of simply asking an AI chatbot random cybersecurity questions, you'll learn how to design a structured assistant that supports repeatable SOC activities.You'll then take the concept further by exploring Multi-Agent AI Security Operations. You'll design specialized AI roles such as a Tier 1 AI Analyst, Threat Hunter Agent, Malware Analyst Agent, Incident Commander Agent, Report Writer Agent, and Executive Briefing Agent. You'll explore how specialized agents can collaborate across different stages of a security investigation.The course also addresses one of the most important topics surrounding enterprise AI adoption: AI Governance and Security.You'll learn about data privacy, secure prompting, AI policies, AI risk management, compliance, sensitive-data handling, human oversight, and responsible AI usage. You'll understand why AI-generated security analysis should be validated rather than blindly trusted and why human-in-the-loop decision making remains essential in security operations.Finally, you'll learn how organizations measure SOC effectiveness using metrics such as Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), false-positive rates, alert fatigue, detection quality, and AI performance metrics. You'll also explore emerging concepts such as Agentic SOCs, autonomous detection, AI security platforms, and the future of AI-powered cyber defense.Throughout the course, you won't just learn theory. You'll complete hands-on projects and practical investigations designed to simulate the types of tasks performed by real SOC and Blue Team professionals.By the end of the bootcamp, you'll have built a portfolio that can include an AI SOC Analyst Assistant, AI-powered log analysis workflows, phishing investigation toolkit, threat-hunting prompt library, automated incident report generator, AI incident-response playbooks, and a Multi-Agent AI SOC Team.Whether you're an aspiring SOC Analyst, cybersecurity student, IT professional, Blue Team practitioner, Incident Responder, or an experienced security professional interested in Generative AI, this course will help you understand how to combine traditional cybersecurity skills with modern AI capabilities.The goal isn't to replace the security analyst with AI. It's to teach you how to become an AI-enabled SOC Analyst—someone who understands cybersecurity fundamentals, knows how to investigate threats, and can use AI responsibly to work faster, smarter, and more effectively in the modern Security Operations Center.

0.0•327•Self-paced

FREE$92.99

Enroll

Generative AI Engineer Masterclass

About this course

Skills you'll gain

Available Coupons

Course Information

This course includes:

Share this course

You May Also Like

The Complete AI Engineer Bootcamp 2026

Forward Deployed Architect: Enterprise Architecture

AI-Powered SOC Analyst Bootcamp: AI Security Operations

Generative AI Engineer Masterclass

About this course

Skills you'll gain

Available Coupons

Course Information

This course includes:

Share this course

You May Also Like

The Complete AI Engineer Bootcamp 2026

Forward Deployed Architect: Enterprise Architecture

AI-Powered SOC Analyst Bootcamp: AI Security Operations