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Masterclass: Securing the Cloud [SEC]
CQure Virtual English
ma 7 dec. 2026
OVERVIEW
Securing the Cloud is an advanced 5-day, 35-hour course focused on securing Microsoft cloud and hybrid environments. Participants will develop the skills required to monitor, protect, and govern identities, resources, and workloads across Microsoft Entra ID, Azure, and Microsoft 365.
The course combines security best practices from both blue team and red team perspectives with hands-on labs and real-world scenarios. Participants will gain practical experience in threat detection, log analysis, incident response, identity and access management, cloud security, and governance using Microsoft’s cloud security stack, including Defender, Sentinel, and Cloud App Security.
Updated August 2026
OBJECTIVES
By the end of the course, participants will be able to:
Monitor, protect, and govern identities, resources, and workloads across Microsoft Entra ID, Azure, and Microsoft 365.
Detect threats, analyze logs, and respond to security incidents using Microsoft cloud security tools.
Apply security best practices to strengthen identity and access controls.
Secure cloud resources and implement governance at scale.
Investigate security incidents and improve the security posture of cloud and hybrid environments.
Apply both blue team and red team approaches to cloud security.
AUDIENCE
- Security architects, Entra ID administrators, security administrators, and security auditors
- Enterprise administrators, infrastructure architects, security professionals, systems engineers, network administrators, IT professionals, and security consultants
CERTIFICATION
Participants receive a CQURE lifelong certification upon completion of the course.
NEXT STEP
None
CONTENT
This Live Virtual Class consists of 6 Modules in terms of Securing the Cloud. They include essential theory combined with individual practice during the exercises as well as loads of hands-on tools and real-case scenarios.
Module 1: Monitoring Operations in Entra ID
1. Entra ID Operations and Logs
2. Entra ID Roles
3. Identity Protection – Roles, Review access, alerts, Discovery and Insights
4. How to deal with Audit Log
5. Challenging Entra ID settings in Azure and Office from red team perspective
6. Privileged Identity Management – JITA, Discover and Monitor
7. Office Management API – Logs around Office 365
8. Microsoft Azure Policies – getting started, compliance, remediation, assignments, blueprints
9. Labs
Module 2: Microsoft 365 Security
1. Secure Score and Security Center
2. Best Practices for Improving Your
3. Secure Score
4. Azure Defender for Servers
5. Security Benchmark Policy
6. Labs
7. STIG & CIS – cloud security baseline
Module 3: Secure Resources and Identities in Azure
1. Secure identity and access (PIM, Identity Protection)
2. Secure Networking (NSG, Azure Firewall, APIM)
3. Secure Compute
4. Secure Storage
5. Secure Databases
6. Defender for Cloud
7. Labs
Module 4: Governance in Azure
1. Security Baseline
2. Azure Policy
3. Azure Role-Based Access Control (RBAC)
4. Management Groups
5. Resource Graph
6. Tagging in Azure
7. Labs
Module 5: Extended Detection and Response with Sentinel
1. Sentinel 101 – Azure Sentinel Dashboards, Connectors
2. Understanding Normalization in Azure Sentinel
3. Cloud & on-prem architecture
4. Workbooks deep dive – Visualize your security threats and hunts
5. Incidents
6. KQL intro (KQL hands-on lab exercises) and Optimizing Azure Sentinel KQL queries performance
7. Auditing and monitoring your Azure Sentinel workspace
8. Sentinel configuration with Microsoft Cloud stack and MCAS
9. Streamlining your SOC Workflow with Automated Notebooks
10. Customizing Azure Sentinel with Python
11. Best Practices for Converting Detection Rules from Splunk, QRadar, and ArcSight to Azure Sentinel Rules
12. Deep Dive into Azure Sentinel Innovations
13. Investigating Azure Security Center alerts using Azure Sentinel
14. Customizable Anomalies and How to Use Them
15. Introduction to Monitoring GitHub with Azure Sentinel for Security Professionals
16. Hunting in Sentinel
17. Deep Dive on Threat Intelligence
18. End-to-End SOC scenario with Sentinel
Module 6: Microsoft Cloud App Security
1. Intro do MCAS
2. Enabling Secure Remote Work
3. App Discovery and Log Collector Configuration
4. Extending real-time monitoring & controls to any app
5. Connecting 3rd party Applications
6. Automation and integration with Microsoft Flow
7. Conditional Access App Control
8. Threat detection
9. Information Protection
10. Labs: Protect Your Environment Using MCAS
11. DLP in Microsoft stack – how to deploy and monitor using MCAS and Sentinel
€3.500
Klassikaal
max 16
Masterclass: Threat Hunting with AI [TAI]
CQure Virtual English
wo 16 dec. 2026
OVERVIEW
Our Threat Hunting with AI Support course is designed for Security Analysts, IT Administrators, Incident Responders and Threat Hunters. During the course participants will learn modern attack techniques, local privilege escalation methods, and identity infrastructure attacks, as well as ways these attacks can be detected and mitigated. This knowledge is enhanced with case studies demonstrating how real-world attacks occur using the methods learned. Additionally, participants will be introduced to solutions that, with AI support, can enhance the threat hunting process. The course concludes by showcasing how threat hunting and threat detection design can be performed using both manual and automated methods.
OBJECTIVES
Understand modern attack techniques and how they are executed and detected
Identify and investigate privilege escalation and identity-based attacks
Understand Windows authentication architecture and common exploitation paths
Apply structured investigation methods using real-world case studies
Use Microsoft Defender for Endpoint (EDR) for threat detection and hunting
Detect and analyze attacks on identity infrastructure
Perform basic network, memory, and disk forensic analysis
Leverage Microsoft Sentinel and Security Copilot in threat detection and investigation
Combine manual and AI-assisted methods to improve threat hunting and response
AUDIENCE
This course is designed for security professionals across offensive, defensive, and hybrid roles. Analysts, hunters, SOC teams, and incident responders will learn to enhance investigations with AI-driven workflows. Red and purple teamers will strengthen adversary emulation and detection validation, while engineers and developers gain hands-on experience building AI-powered tools, pipelines, and multi-agent systems. Security leaders and architects will benefit from practical insights into securing AI systems and addressing emerging vulnerabilities.
CERTIFICATION
After completing the course, participants will receive a CQURE Certificate of Completion and will also be eligible for CPE points.
CONTENT
Module 1: Modern Attack Techniques and Tracing Them
Module 2: Local Privilege Escalation Techniques and Tracing Them
Module 3: Case Study – Investigating In-Place Attacks
Module 4: Windows Authentication Architecture & Cryptography
Module 5: Case Study – Investigating Identity Theft
Module 6: Attacks on Identity Infrastructure and Tracing Them
Module 7: Microsoft 365 Defender for Endpoint (EDR)
Module 8: Security Operations with Microsoft EDR (Defender for Endpoint) – Advanced Threat Hunting with Defender
Module 9: Microsoft Security Copilot
Module 10: Case Study – Detecting a Complex Threat with Microsoft Sentinel and Microsoft Copilot for Security
Module 11: Network Forensics and Monitoring
Module 12: Memory Dumping and Analysis
Module 13: Disk Dumping and Analysis
€2.575
Klassikaal
max 16
Masterclass: Web Application Pentesting [WEB]
CQure Virtual English
ma 21 dec. 2026
OVERVIEW
In this 3-day course, you will develop essential cybersecurity knowledge and skills with a focus on Web Application Pentesting.
This course covers techniques and strategy concepts for performing professional web applications penetration testing in a highly secure environment. Our course has been developed around professional penetration testing, web applications development and security awareness in the business and IT fields.
Our goal is to show you all the most important aspects of web application penetration testing. Together we will look for vulnerabilities and exploit them in practice in CQURE’s custom-built training environment. During the exercises, we will use industry-standard tools such as the Kali Linux, Burp Suite, Bloodhound, Metasploit and the Wireshark.
Virtual Learning
This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.
OBJECTIVES
Gain understanding of the penetration tester’s perspective, including web penetration testing methodologies, reconnaissance, and core concepts of web application security.
Understand web application security fundamentals including browser security mechanisms, authentication and authorization models, and secure testing approaches.
Develop knowledge of common web application vulnerabilities including Cross-Site Scripting (XSS), injections, insecure file handling, and insecure inclusions.
Gain practical skills in identifying and exploiting vulnerabilities across web applications through structured testing techniques.
Understand and apply techniques for testing APIs and bypassing security controls in modern web application environments.
Implement hands-on penetration testing practices across all stages of testing using industry-standard tools in a realistic lab environment.
AUDIENCE
This bootcamp is designed for you if you are a:
- Penetration tester
- Security analyst
- IT administrator
- Cybersecurity professional
- & a geek with IT background who wants to start an adventure in the cybersecurity pentesting field
CERTIFICATION
After finishing the course, you will be granted a CQURE Certificate of Completion. Please note that after completing the course you will also be eligible for CPE points!
CONTENT
The Web Application Pentesting agenda consists of 10 Modules that will be covered during 3 Days.
The training will allow you to understand the penetration tester’s perspective on security, and learn crucial tools and concepts needed for everyone considering developing their career in penetration testing or cybersecurity in general.
Module 1: Introduction to Web Penetration Testing
Module 2: Reconnaissance
Module 3: Introduction to Web Application testing
Module 4: Browser’s security mechanisms
Module 5: Cross Site Scripting
Module 6: Injections
Module 7: Authentication and Authorization
Module 8: Insecure file handling
Module 9: Insecure inclusions
Module 10: Testing API
€2.575
Klassikaal
max 16
Python: Building AI Agents with LangChain (English)
Nieuwegein
wo 24 feb. 2027
en 3 andere data
Lesmethode :
Klassikaal
Algemeen :
AI agents combine a language model with tools, allowing an application to reason about a task, choose suitable actions and work step by step towards a result. Unlike a chatbot, an agent can call Python functions, retrieve information, process data and interact with external systems. In this two-day, hands-on course, you learn how to build AI agents in Python with LangChain. You learn the difference between a language-model application and an AI agent and work with the agent loop of model, tool and result.
You learn how LangChain's create_agent assembles this agent loop on top of the LangGraph runtime. You connect a language model, turn Python functions into tools with the @tool decorator, and define clear tool names, descriptions and input schemas so an agent can choose between multiple tools. You also work with system prompts, messages and agent state for short-term memory and structured output. In addition, you learn how to handle failing tools and invalid results, apply stop conditions and iteration limits, and use tracing to inspect agent decisions and tool calls.
During the course, you spend most of your time writing code, with explanations kept short. You invoke agents, process their results and finish by building a working end-to-end agent that uses multiple tools to carry out a practical task. This gives you direct experience building agentic AI applications in Python.
Doel :
After this course, you can build tool-using AI agents in Python with LangChain, including tools, prompts, conversation state and structured output. You can also handle failures, apply execution limits, trace agent decisions and build an end-to-end agent application. The course focuses on building agents in Python; training or fine-tuning language models is not covered.
Doelgroep :
Python developers, software developers, data and AI engineers, automation engineers and other technical professionals who want a practical introduction to building AI agents.
Voorkennis :
The following prior knowledge is required:
- Leren programmeren in Python
or
- De programmeertaal Python voor ervaren programmeurs
- Basic understanding of APIs and JSON
Onderwerpen :
- Agents and the agent loop
+nbsp;+nbsp;+nbsp;- Introduction to AI agents
+nbsp;+nbsp;+nbsp;- Agents versus chatbots and workflows
+nbsp;+nbsp;+nbsp;- The agent execution loop
+nbsp;+nbsp;+nbsp;- LangChain agent architecture
- Creating an agent with LangChain
+nbsp;+nbsp;+nbsp;- Language models and provider configuration
+nbsp;+nbsp;+nbsp;- Creating an agent with create_agent
+nbsp;+nbsp;+nbsp;- System prompts and agent instructions
- Building and describing tools
+nbsp;+nbsp;+nbsp;- Creating tools from Python functions
+nbsp;+nbsp;+nbsp;- The LangChain @tool decorator
+nbsp;+nbsp;+nbsp;- Tool names, descriptions and input schemas
+nbsp;+nbsp;+nbsp;- Multiple tools and tool selection
- Running an agent and managing state
+nbsp;+nbsp;+nbsp;- Invoking an agent
+nbsp;+nbsp;+nbsp;- Messages and agent state
+nbsp;+nbsp;+nbsp;- Short-term memory
+nbsp;+nbsp;+nbsp;- Structured output
- Reliability and control
+nbsp;+nbsp;+nbsp;- Error handling and tool failures
+nbsp;+nbsp;+nbsp;- Stop conditions and iteration limits
+nbsp;+nbsp;+nbsp;- Tracing and debugging agent execution
- Putting it all together
+nbsp;+nbsp;+nbsp;- Building an end-to-end AI agent
€1.535
Klassikaal
max 12
Slimmer vergaderen met Teams en Copilot (Virtueel)
Virtueel
wo 20 jan. 2027
en 9 andere data
Lesmethode :
Virtueel
Algemeen :
Vergaderingen zijn vaak een bron van versnipperde informatie, losse actiepunten en veel administratief werk. In deze cursus leer je hoe je Microsoft Teams, Microsoft Loop, Facilitator en Microsoft 365 Copilot inzet om de volledige vergadercyclus slimmer te organiseren. Je ontdekt hoe je vergaderingen beter voorbereidt, efficiënter begeleidt en sneller opvolgt, zodat je meer tijd overhoudt voor inhoudelijke samenwerking en besluitvorming.
Tijdens de cursus krijg je inzicht in de samenhang tussen verschillende Microsoft 365-applicaties en leer je wanneer je Teams, Loop, Planner en Copilot het beste inzet. Je werkt met vergadersjablonen en Meeting Notes in Teams, leert gezamenlijke agenda's en actiepunten beheren in Loop en ontdekt hoe Facilitator ondersteuning biedt bij het vastleggen van notities, besluiten en taken. Daarnaast leer je hoe Copilot helpt bij het voorbereiden van vergaderingen, het genereren van samenvattingen, het opstellen van notulen en het organiseren van vervolgacties. Ook besteden we aandacht aan effectief prompten en aan praktische afspraken rondom eigenaarschap, informatiebeheer en controle van AI-gegenereerde output.
De cursus is sterk praktijkgericht. Je oefent met herkenbare vergadersituaties en doorloopt een complete vergadercyclus: van voorbereiding en agendavorming tot besluitvorming en opvolging. Door middel van demonstraties, opdrachten en praktijkcases ervaar je hoe de verschillende tools elkaar versterken. De opgedane kennis kun je direct toepassen binnen je eigen team, project of organisatie.
Doel :
Na afloop kun je Microsoft Teams, Loop, Facilitator en Copilot effectief inzetten voor de voorbereiding, uitvoering en opvolging van vergaderingen. Je bent in staat om samenwerking te structureren, actiepunten beter te beheren en AI verantwoord te gebruiken binnen het vergaderproces.
Doelgroep :
Iedereen die regelmatig online of hybride samenwerkt en vergaderingen organiseert of bijwoont, zoals projectleiders, teamleiders, managers, adviseurs en kenniswerkers. Ook geschikt voor professionals die meer rendement willen halen uit Microsoft Teams en Microsoft 365 Copilot.
Voorkennis :
#Basiservaring met Microsoft Teams
#Vergaderingen kunnen plannen via Teams of Outlook
#Kunnen deelnemen aan Teams-vergaderingen
#Bestanden kunnen openen en delen binnen Teams
#Basiskennis van teams en kanalen
#Ervaring met Copilot, Loop of Facilitator is niet vereist
Onderwerpen :
- Teams, Loop en Copilot in de moderne vergadercyclus
- Vergaderingen voorbereiden en structureren
- Samenwerken met Loop en gedeelde werkruimtes
- AI-ondersteuning met Facilitator en Copilot
- Samenvattingen, notulen en actiepunten genereren
- Taken opvolgen met Planner en To Do
- Best practices voor effectief en verantwoord AI-gebruik
€599
Klassikaal
max 12
AI Workloads on Kubernetes (English)
Nieuwegein
do 15 apr. 2027
en 2 andere data
Lesmethode :
Klassikaal
Algemeen :
In this hands-on, two-day Kubernetes course, you learn how to prepare and operate a Kubernetes platform for GPU-accelerated AI workloads. AI and machine learning workloads need scalable compute, GPU acceleration and flexible resource management. You see how GPUs and other accelerators are made available to Kubernetes, how AI applications request and share these resources, and how Kubernetes schedules workloads based on their requirements.
The course focuses on the infrastructure and platform engineering side of AI. You work with GPU-enabled Kubernetes nodes and make them available through the NVIDIA GPU Operator, device plugins and GPU runtime. You then manage those resources with requests and limits, GPU sharing, Dynamic Resource Allocation, ResourceClaims, workload priorities and quotas. On top of that you scale workloads with the Horizontal Pod Autoscaler and KEDA, serve Large Language Models with vLLM, run AI and ML pipelines with Kubeflow, and monitor everything with Prometheus and Grafana.
Theory and practice alternate throughout the course. Every topic is followed by hands-on exercises in your own Kubernetes environment, where you deploy, schedule, scale, monitor and troubleshoot AI workloads. You do not train machine learning models yourself; instead, you learn how to provide a Kubernetes environment in which data scientists, ML engineers and AI applications can run their workloads reliably.
Doel :
After this AI Workloads on Kubernetes course, you can prepare and operate a Kubernetes platform for GPU-accelerated AI workloads, and deploy, schedule, scale and monitor these workloads reliably. You also know how technologies such as the NVIDIA GPU Operator, Dynamic Resource Allocation, KEDA, vLLM and Kubeflow fit into your Kubernetes environment.
Doelgroep :
Engineers who run or support AI and machine learning workloads on Kubernetes, such as DevOps Engineers, Platform Engineers, Kubernetes Administrators, Cloud Engineers and Infrastructure Engineers. The course focuses on the platform and operational side of AI workloads and is not a machine learning or data science course.
Voorkennis :
The following prior knowledge is required:
- Kubernetes Fundamentals
- Experience with AI or machine learning is a plus; experience training machine learning models is not required.
Onderwerpen :
- Introduction to AI workloads on Kubernetes
+nbsp;+nbsp;+nbsp;- AI platform architecture
+nbsp;+nbsp;+nbsp;- CNCF AI platform architecture and ecosystem
+nbsp;+nbsp;+nbsp;- CPU versus GPU workloads
- GPU-enabled Kubernetes nodes
+nbsp;+nbsp;+nbsp;- From hardware to Kubernetes
+nbsp;+nbsp;+nbsp;- NVIDIA GPU Operator
+nbsp;+nbsp;+nbsp;- NVIDIA device plugins and GPU runtime
- Deploying applications with GPU requirements
+nbsp;+nbsp;+nbsp;- Deploying GPU workloads
+nbsp;+nbsp;+nbsp;- Kubernetes resource requests and limits
- GPU resource management
+nbsp;+nbsp;+nbsp;- GPU sharing
+nbsp;+nbsp;+nbsp;- Dynamic Resource Allocation (DRA)
+nbsp;+nbsp;+nbsp;- ResourceClaims
+nbsp;+nbsp;+nbsp;- Workload priorities and quotas
- Scheduling workloads
+nbsp;+nbsp;+nbsp;- Scheduling AI workloads
+nbsp;+nbsp;+nbsp;- Batch workloads
- Autoscaling workloads
+nbsp;+nbsp;+nbsp;- Autoscaling AI workloads
+nbsp;+nbsp;+nbsp;- Horizontal Pod Autoscaler
+nbsp;+nbsp;+nbsp;- Event-driven scaling with KEDA
+nbsp;+nbsp;+nbsp;- AI-aware scheduling and KAI Scheduler concepts
- AI use cases
+nbsp;+nbsp;+nbsp;- Large Language Model inference
+nbsp;+nbsp;+nbsp;- Deploying LLMs on Kubernetes
+nbsp;+nbsp;+nbsp;- Model serving with vLLM
+nbsp;+nbsp;+nbsp;- Concurrent inference workloads
+nbsp;+nbsp;+nbsp;- Distributed inference concepts
+nbsp;+nbsp;+nbsp;- Introduction to Kubeflow
+nbsp;+nbsp;+nbsp;- AI and ML pipelines on Kubernetes
- Monitoring
+nbsp;+nbsp;+nbsp;- Monitoring GPU workloads
+nbsp;+nbsp;+nbsp;- Prometheus metrics
+nbsp;+nbsp;+nbsp;- Grafana dashboards
+nbsp;+nbsp;+nbsp;- Troubleshooting AI workloads
€1.535
Klassikaal
max 12
Slimmer vergaderen met Teams en Copilot
Nieuwegein
wo 20 jan. 2027
en 9 andere data
Lesmethode :
Klassikaal
Algemeen :
Vergaderingen zijn vaak een bron van versnipperde informatie, losse actiepunten en veel administratief werk. In deze cursus leer je hoe je Microsoft Teams, Microsoft Loop, Facilitator en Microsoft 365 Copilot inzet om de volledige vergadercyclus slimmer te organiseren. Je ontdekt hoe je vergaderingen beter voorbereidt, efficiënter begeleidt en sneller opvolgt, zodat je meer tijd overhoudt voor inhoudelijke samenwerking en besluitvorming.
Tijdens de cursus krijg je inzicht in de samenhang tussen verschillende Microsoft 365-applicaties en leer je wanneer je Teams, Loop, Planner en Copilot het beste inzet. Je werkt met vergadersjablonen en Meeting Notes in Teams, leert gezamenlijke agenda's en actiepunten beheren in Loop en ontdekt hoe Facilitator ondersteuning biedt bij het vastleggen van notities, besluiten en taken. Daarnaast leer je hoe Copilot helpt bij het voorbereiden van vergaderingen, het genereren van samenvattingen, het opstellen van notulen en het organiseren van vervolgacties. Ook besteden we aandacht aan effectief prompten en aan praktische afspraken rondom eigenaarschap, informatiebeheer en controle van AI-gegenereerde output.
De cursus is sterk praktijkgericht. Je oefent met herkenbare vergadersituaties en doorloopt een complete vergadercyclus: van voorbereiding en agendavorming tot besluitvorming en opvolging. Door middel van demonstraties, opdrachten en praktijkcases ervaar je hoe de verschillende tools elkaar versterken. De opgedane kennis kun je direct toepassen binnen je eigen team, project of organisatie.
Doel :
Na afloop kun je Microsoft Teams, Loop, Facilitator en Copilot effectief inzetten voor de voorbereiding, uitvoering en opvolging van vergaderingen. Je bent in staat om samenwerking te structureren, actiepunten beter te beheren en AI verantwoord te gebruiken binnen het vergaderproces.
Doelgroep :
Iedereen die regelmatig online of hybride samenwerkt en vergaderingen organiseert of bijwoont, zoals projectleiders, teamleiders, managers, adviseurs en kenniswerkers. Ook geschikt voor professionals die meer rendement willen halen uit Microsoft Teams en Microsoft 365 Copilot.
Voorkennis :
#Basiservaring met Microsoft Teams
#Vergaderingen kunnen plannen via Teams of Outlook
#Kunnen deelnemen aan Teams-vergaderingen
#Bestanden kunnen openen en delen binnen Teams
#Basiskennis van teams en kanalen
#Ervaring met Copilot, Loop of Facilitator is niet vereist
Onderwerpen :
- Teams, Loop en Copilot in de moderne vergadercyclus
- Vergaderingen voorbereiden en structureren
- Samenwerken met Loop en gedeelde werkruimtes
- AI-ondersteuning met Facilitator en Copilot
- Samenvattingen, notulen en actiepunten genereren
- Taken opvolgen met Planner en To Do
- Best practices voor effectief en verantwoord AI-gebruik
€599
Klassikaal
max 12
Routekaart AI adoptie
Nieuwegein
vr 22 jan. 2027
en 3 andere data
Lesmethode :
Klassikaal
Algemeen :
Veel organisaties experimenteren inmiddels met AI. De eerste tools zijn getest, medewerkers zijn nieuwsgierig en de mogelijkheden lijken groot. Toch blijft de belangrijkste vraag vaak liggen: hoe zorg je dat AI niet blijft hangen in losse experimenten, maar echt waarde toevoegt aan je organisatie? In deze workshop leer je hoe je van inspiratie naar structurele toepassing komt en hoe je bepaalt waar je het beste kunt beginnen.
Je benadert AI niet als hype of los initiatief, maar als middel om organisatiedoelen te ondersteunen. De nadruk ligt niet op de techniek achter AI, maar op de voorwaarden voor succesvolle invoering. Je werkt aan thema's zoals richting bepalen, draagvlak creëren, keuzes maken, toepassingen selecteren en AI borgen in de dagelijkse praktijk. Zo krijg je een praktisch kader om AI op een gestructureerde en duurzame manier te positioneren binnen je organisatie.
De workshop is sterk praktijkgericht. Met voorbeelden, opdrachten en herkenbare organisatiesituaties werk je stap voor stap aan een eerste AI-adoptieplan. Je bepaalt waar je met AI wilt beginnen, wat het moet opleveren en hoe dit aansluit op de koers van je organisatie. Ook maak je duidelijke keuzes over welke toepassingen prioriteit hebben, hoe AI-oplossingen passen in bestaande werkprocessen en welke stappen nodig zijn om van idee naar uitvoering te komen. Daarnaast breng je in kaart wie je moet betrekken, wie besluiten neemt en hoe klaar je organisatie is om AI verantwoord toe te passen. Je werkt aan een aanpak voor AI-pilots waarin het menselijke aspect centraal staat: klein starten, leren van ervaringen, opschalen wat werkt en zorgen dat nieuwe werkwijzen worden geborgd. Tot slot richt je een manier in om de impact van AI te blijven meten, zodat je kunt bijsturen op basis van wat in de praktijk wel en niet werkt. Zo ga je naar huis met een routekaart waarmee je AI gestructureerd, meetbaar en duurzaam kunt invoeren.
Doel :
Na afloop weet je welke voorwaarden nodig zijn om AI succesvol te introduceren in je organisatie. Je gaat naar huis met een eerste opzet voor een AI-adoptieroutekaart waarmee je gericht vervolgstappen kunt zetten.
Doelgroep :
Iedereen die binnen een organisatie verantwoordelijk is voor het invoeren, begeleiden of versnellen van AI. Denk aan teamleiders, projectleiders, verandermanagers, innovatiemanagers, HR-professionals, informatiemanagers en afdelingsmanagers.
Voorkennis :
Er is geen voorkennis nodig.
Onderwerpen :
- Introductie in AI en digitale adoptie
- Het organisatiedoel van AI
+nbsp;+nbsp;+nbsp;- Bepaal je doelstelling
+nbsp;+nbsp;+nbsp;- Wat levert het je op? Baten
- Benodigdheden voor AI-adoptie
+nbsp;+nbsp;+nbsp;- Data: AI is zo goed als de data die je gebruikt. In de workshop leer je waar je op moet letten om data bruikbaar, betrouwbaar en passend te maken voor AI-toepassingen.
+nbsp;+nbsp;+nbsp;- Processen: AI werkt alleen goed als processen duidelijk genoeg zijn. Je kijkt daarom naar het belang van gestandaardiseerde en herhaalbare processen.
+nbsp;+nbsp;+nbsp;- Mens en gedrag: AI is meer dan techniek. Je onderzoekt welke vaardigheden al aanwezig zijn, wat nog ontbreekt, waarin je moet investeren en hoe je betrokkenen meekrijgt in de verandering.
+nbsp;+nbsp;+nbsp;- Besturing: Je kijkt hoe besluitvorming over AI-initiatieven verloopt, welk beleid nodig is en welke rollen daarbij horen. Ook komen use cases voorbij over besturing van AI binnen organisaties.
- De AI-readiness scan
- Jouw AI-adoptieroutekaart
€795
Klassikaal
max 12
Python: Building AI Agents with LangChain (English) (Virtual)
Virtueel
wo 24 feb. 2027
en 3 andere data
Lesmethode :
Virtueel
Algemeen :
AI agents combine a language model with tools, allowing an application to reason about a task, choose suitable actions and work step by step towards a result. Unlike a chatbot, an agent can call Python functions, retrieve information, process data and interact with external systems. In this two-day, hands-on course, you learn how to build AI agents in Python with LangChain. You learn the difference between a language-model application and an AI agent and work with the agent loop of model, tool and result.
You learn how LangChain's create_agent assembles this agent loop on top of the LangGraph runtime. You connect a language model, turn Python functions into tools with the @tool decorator, and define clear tool names, descriptions and input schemas so an agent can choose between multiple tools. You also work with system prompts, messages and agent state for short-term memory and structured output. In addition, you learn how to handle failing tools and invalid results, apply stop conditions and iteration limits, and use tracing to inspect agent decisions and tool calls.
During the course, you spend most of your time writing code, with explanations kept short. You invoke agents, process their results and finish by building a working end-to-end agent that uses multiple tools to carry out a practical task. This gives you direct experience building agentic AI applications in Python.
Doel :
After this course, you can build tool-using AI agents in Python with LangChain, including tools, prompts, conversation state and structured output. You can also handle failures, apply execution limits, trace agent decisions and build an end-to-end agent application. The course focuses on building agents in Python; training or fine-tuning language models is not covered.
Doelgroep :
Python developers, software developers, data and AI engineers, automation engineers and other technical professionals who want a practical introduction to building AI agents.
Voorkennis :
The following prior knowledge is required:
- Leren programmeren in Python
or
- De programmeertaal Python voor ervaren programmeurs
- Basic understanding of APIs and JSON
Onderwerpen :
- Agents and the agent loop
+nbsp;+nbsp;+nbsp;- Introduction to AI agents
+nbsp;+nbsp;+nbsp;- Agents versus chatbots and workflows
+nbsp;+nbsp;+nbsp;- The agent execution loop
+nbsp;+nbsp;+nbsp;- LangChain agent architecture
- Creating an agent with LangChain
+nbsp;+nbsp;+nbsp;- Language models and provider configuration
+nbsp;+nbsp;+nbsp;- Creating an agent with create_agent
+nbsp;+nbsp;+nbsp;- System prompts and agent instructions
- Building and describing tools
+nbsp;+nbsp;+nbsp;- Creating tools from Python functions
+nbsp;+nbsp;+nbsp;- The LangChain @tool decorator
+nbsp;+nbsp;+nbsp;- Tool names, descriptions and input schemas
+nbsp;+nbsp;+nbsp;- Multiple tools and tool selection
- Running an agent and managing state
+nbsp;+nbsp;+nbsp;- Invoking an agent
+nbsp;+nbsp;+nbsp;- Messages and agent state
+nbsp;+nbsp;+nbsp;- Short-term memory
+nbsp;+nbsp;+nbsp;- Structured output
- Reliability and control
+nbsp;+nbsp;+nbsp;- Error handling and tool failures
+nbsp;+nbsp;+nbsp;- Stop conditions and iteration limits
+nbsp;+nbsp;+nbsp;- Tracing and debugging agent execution
- Putting it all together
+nbsp;+nbsp;+nbsp;- Building an end-to-end AI agent
€1.535
Klassikaal
max 12
AI Workloads on Kubernetes (English) (Virtual)
Virtueel
do 15 apr. 2027
en 2 andere data
Lesmethode :
Virtueel
Algemeen :
In this hands-on, two-day Kubernetes course, you learn how to prepare and operate a Kubernetes platform for GPU-accelerated AI workloads. AI and machine learning workloads need scalable compute, GPU acceleration and flexible resource management. You see how GPUs and other accelerators are made available to Kubernetes, how AI applications request and share these resources, and how Kubernetes schedules workloads based on their requirements.
The course focuses on the infrastructure and platform engineering side of AI. You work with GPU-enabled Kubernetes nodes and make them available through the NVIDIA GPU Operator, device plugins and GPU runtime. You then manage those resources with requests and limits, GPU sharing, Dynamic Resource Allocation, ResourceClaims, workload priorities and quotas. On top of that you scale workloads with the Horizontal Pod Autoscaler and KEDA, serve Large Language Models with vLLM, run AI and ML pipelines with Kubeflow, and monitor everything with Prometheus and Grafana.
Theory and practice alternate throughout the course. Every topic is followed by hands-on exercises in your own Kubernetes environment, where you deploy, schedule, scale, monitor and troubleshoot AI workloads. You do not train machine learning models yourself; instead, you learn how to provide a Kubernetes environment in which data scientists, ML engineers and AI applications can run their workloads reliably.
Doel :
After this AI Workloads on Kubernetes course, you can prepare and operate a Kubernetes platform for GPU-accelerated AI workloads, and deploy, schedule, scale and monitor these workloads reliably. You also know how technologies such as the NVIDIA GPU Operator, Dynamic Resource Allocation, KEDA, vLLM and Kubeflow fit into your Kubernetes environment.
Doelgroep :
Engineers who run or support AI and machine learning workloads on Kubernetes, such as DevOps Engineers, Platform Engineers, Kubernetes Administrators, Cloud Engineers and Infrastructure Engineers. The course focuses on the platform and operational side of AI workloads and is not a machine learning or data science course.
Voorkennis :
The following prior knowledge is required:
- Kubernetes Fundamentals
- Experience with AI or machine learning is a plus; experience training machine learning models is not required.
Onderwerpen :
- Introduction to AI workloads on Kubernetes
+nbsp;+nbsp;+nbsp;- AI platform architecture
+nbsp;+nbsp;+nbsp;- CNCF AI platform architecture and ecosystem
+nbsp;+nbsp;+nbsp;- CPU versus GPU workloads
- GPU-enabled Kubernetes nodes
+nbsp;+nbsp;+nbsp;- From hardware to Kubernetes
+nbsp;+nbsp;+nbsp;- NVIDIA GPU Operator
+nbsp;+nbsp;+nbsp;- NVIDIA device plugins and GPU runtime
- Deploying applications with GPU requirements
+nbsp;+nbsp;+nbsp;- Deploying GPU workloads
+nbsp;+nbsp;+nbsp;- Kubernetes resource requests and limits
- GPU resource management
+nbsp;+nbsp;+nbsp;- GPU sharing
+nbsp;+nbsp;+nbsp;- Dynamic Resource Allocation (DRA)
+nbsp;+nbsp;+nbsp;- ResourceClaims
+nbsp;+nbsp;+nbsp;- Workload priorities and quotas
- Scheduling workloads
+nbsp;+nbsp;+nbsp;- Scheduling AI workloads
+nbsp;+nbsp;+nbsp;- Batch workloads
- Autoscaling workloads
+nbsp;+nbsp;+nbsp;- Autoscaling AI workloads
+nbsp;+nbsp;+nbsp;- Horizontal Pod Autoscaler
+nbsp;+nbsp;+nbsp;- Event-driven scaling with KEDA
+nbsp;+nbsp;+nbsp;- AI-aware scheduling and KAI Scheduler concepts
- AI use cases
+nbsp;+nbsp;+nbsp;- Large Language Model inference
+nbsp;+nbsp;+nbsp;- Deploying LLMs on Kubernetes
+nbsp;+nbsp;+nbsp;- Model serving with vLLM
+nbsp;+nbsp;+nbsp;- Concurrent inference workloads
+nbsp;+nbsp;+nbsp;- Distributed inference concepts
+nbsp;+nbsp;+nbsp;- Introduction to Kubeflow
+nbsp;+nbsp;+nbsp;- AI and ML pipelines on Kubernetes
- Monitoring
+nbsp;+nbsp;+nbsp;- Monitoring GPU workloads
+nbsp;+nbsp;+nbsp;- Prometheus metrics
+nbsp;+nbsp;+nbsp;- Grafana dashboards
+nbsp;+nbsp;+nbsp;- Troubleshooting AI workloads
€1.535
Klassikaal
max 12