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EXIN BCS Machine Learning Award - Including Exam [MACHL-AWARD]
Amsterdam ARISTO Center
vr 29 jan. 2027
en 7 andere data
OVERVIEW
The EXIN BCS Machine Learning Award gives you a clear, structured introduction to machine learning—covering key algorithms, data processing, model training, and real-world applications. You’ll learn how to prepare and transform data, understand supervised and unsupervised learning, and get hands-on insights into programming languages and ML frameworks such as Python, TensorFlow, and Scikit-Learn—even if you’re new to AI.
Updated 14/05/2026
OBJECTIVES
What will you learn?
Gain a structured, easy-to-follow introduction to machine learning fundamentals, including supervised, unsupervised, and semi-supervised learning—even if you’re new to AI.
Understand regression, classification, clustering, and deep learning—the core techniques behind AI-powered decision-making, automation, and predictive analytics.
Learn how machines recognize patterns, train on data, and improve over time without needing a PhD in statistics.
Explore Python, TensorFlow, Scikit-Learn, and R—the leading tools for building ML models, even if you have no prior coding experience.
Learn how to collect, clean, preprocess, and transform data for machine learning—key skills needed to build accurate and reliable AI models.
From Netflix-style recommendations and chatbots to fraud detection and cybersecurity, understand how machine learning is driving innovation across industries.
Get a complete picture of how ML models are trained, tested, fine-tuned, and optimized for real-world deployment.
Understand the biases, legal concerns, and ethical implications of machine learning to ensure responsible AI implementation.
AUDIENCE
- IT Professionals
- Software Developers
- Data Analysts
- Data Scientists
- Business Leaders & AI Strategists
- Project Managers
- Product Managers
- Engineers & Technical Consultants
- Individuals with an interest in AI and a background in science, engineering, knowledge engineering, finance, education, or IT services
CERTIFICATION
Exam Details
Duration: 30 minutes
Number of Questions: 18 - of which 2 scenario-based questions worth 2 points each (Multiple Choice)
Pass mark: 65%
Open book: No
Electronic equipment allowed: No
Level: Foundation
Languages: English, Portuguese
CONTENT
Introduction to Machine Learning
Definition and Overview
Applications of Machine Learning
Role of Learning Agents
Concept of Deep Learning
Purpose and Function of Neural Networks
Integration with Knowledge-Based Systems
Data Interaction in Machine Learning
Programming in Machine Learning
Programming Languages for Machine Learning
Software Tools: Open Source vs. Proprietary
Machine Learning Algorithms
Mathematical Foundations
Common Algorithms in Machine Learning
Types of Learning: Supervised, Unsupervised, and Semi-Supervised
Practical Applications of Machine Learning
Problem Identification for Machine Learning Solutions
Data Preparation and Processing
Training Machine Learning Models
Testing and Validation of Models
Evaluation and Reporting of Results to Stakeholders
€845
Klassikaal
max 16
Develop AI apps and agents on Azure (AI-103) [M-AI103]
VIRTUAL TRAINING CENTER
ma 7 dec. 2026
en 9 andere data
OVERVIEW
Design and build intelligent AI apps and agents on Azure that reason over text, images, and documents.
This course is intended for software developers wanting to build AI infused applications that leverage Microsoft Foundry. Topics in this course include developing generative AI apps, building AI agents, and solutions that implement knowledge connections or tools in your agentic applications. This course also covers multimodal capabilities and understanding of complex content.
Updated 4/2026
OBJECTIVES
By the end of this course, participants should be able to:
Develop generative AI applications using Microsoft Foundry on Azure
Design and implement AI agents capable of task execution and orchestration
Integrate external tools and knowledge sources into agent-based solutions
Apply multimodal AI techniques to process diverse data types
Build natural language processing solutions for conversational and text-based scenarios
Extract, analyse, and reason over visual and complex content
Design scalable, production-ready AI systems using Azure services
AUDIENCE
This course was designed for software engineers concerned with building, managing and deploying AI solutions that leverage Microsoft Foundry.
CERTIFICATION
Exam AI-103: Developing AI Apps and Agents on Azure
NEXT STEP
Develop AI cloud solutions on Azure (AI-200)
Operationalize machine learning and generative AI solutions (AI-300)
Depending on projects, course codes starting with AI-30xx
CONTENT
Module 1: Introduction to AI applications and agents on Azure
Overview of AI application architectures
Introduction to generative AI concepts
Understanding agent-based systems and their role in modern AI
Overview of Microsoft Foundry capabilities
Azure services for AI development
Module 2: Developing generative AI applications
Working with foundation models
Prompt engineering techniques
Designing application workflows with generative AI
Managing inputs, outputs, and context
Evaluating and refining model responses
Module 3: Building AI agents on Azure
Agent architecture and design patterns
Task planning and execution
Managing agent state and memory
Event-driven and autonomous agent behaviours
Debugging and monitoring agent performance
Module 4: Integrating tools and knowledge into agentic solutions
Tool integration patterns for agents
Connecting agents to APIs and external services
Knowledge grounding and retrieval techniques
Implementing retrieval-augmented generation (RAG)
Managing data sources and context injection
Module 5: Developing natural language AI solutions
Natural language processing fundamentals
Conversational AI design
Text analysis and classification
Language generation and summarisation
Building chat-based interfaces
Module 6: Multimodal AI and complex content understanding
Working with image and text inputs
Multimodal model capabilities
Extracting insights from visual data
Combining modalities in a single workflow
Handling complex and unstructured content
Module 7: Building scalable AI solutions with Microsoft Foundry
Application deployment strategies on Azure
Scaling AI workloads
Performance optimisation
Monitoring and logging
Security and responsible AI considerations
Module 8: Designing production-ready AI systems
End-to-end solution design
Architectural best practices
Managing lifecycle and updates
Testing and validation strategies
Real-world use case scenarios
€2.295
Klassikaal
max 16
Develop AI cloud solutions on Azure (AI-200) [M-AI200]
Eindhoven (Evoluon Noord Brabantlaan 1)
ma 15 feb. 2027
en 9 andere data
OVERVIEW
Create, monitor, and troubleshoot AI solutions on Microsoft Azure.
AI‑200 validates the skills required to design, build, and operate cloud‑native AI solutions on Microsoft Azure.It focuses on integrating Azure AI services into scalable applications using containers, serverless compute, event‑driven architectures, and vector‑enabled data stores.Learners gain hands‑on experience securing, monitoring, and optimizing AI workloads across the full application lifecycle.
OBJECTIVES
In this course, students will learn how to implement Azure compute and containerization patterns to host applications, build serverless APIs with Azure Functions, and integrate services using event driven and message based architectures such as Azure Service Bus and Event Grid. The course also covers working with Azure data services that support AI workloads, including designing and querying solutions with Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for caching, streaming, and vector search. By the end of the course, developers will be able to connect services, orchestrate AI workflows, and build secure, scalable, and observable AI driven applications on Azure.
AUDIENCE
This course is designed for developers who build backend and AI driven applications on Azure and need practical skills in containerized compute, data services for AI, event driven workflows, and application security and monitoring.
CERTIFICATION
None
NEXT STEP
None
CONTENT
Module 1: Implement container application hosting on Azure
Store and manage containers in Azure Container Registry
Deploy containers to Azure App Service
Module 2: Deploy and manage apps on Azure Container Apps
Deploy containers to Azure Container Apps
Manage containers in Azure Container Apps
Scale containers in Azure Container Apps
Module 3: Deploy and monitor applications on Azure Kubernetes Service
Deploy applications to Azure Kubernetes Service
Configure applications on Azure Kubernetes Service
Monitor and troubleshoot applications on Azure Kubernetes Service
Module 4: Develop AI solutions with Azure Cosmos DB for NoSQL
Build queries for Azure Cosmos DB for NoSQL
Implement vector search on Azure Cosmos DB for NoSQL
Optimize query performance for Azure Cosmos DB for NoSQL
Module 5: Develop AI solutions with Azure Database for PostgreSQL
Build and query with Azure Database for PostgreSQL
Implement vector search with Azure Database for PostgreSQL
Optimize vector search in Azure Database for PostgreSQL
Module 6: Enhance AI solutions with Azure Managed Redis
Implement data operations in Azure Managed Redis
Implement event messaging with Azure Managed Redis
Implement vector storage in Azure Managed Redis
Module 7: Integrate backend services for AI solutions
Queue and process AI operations with Azure Service Bus
Develop event-driven AI workflows with Azure Event Grid
Build serverless AI backends with Azure Functions
Module 8: Manage application secrets and configuration for AI solutions
Manage application secrets with Azure Key Vault
Manage application settings with Azure App Configuration
Module 9: Observe and troubleshoot apps on Azure
Instrument an app with OpenTelemetry
Analyze app telemetry with logs and metrics
€2.595
Klassikaal
max 16
Operationalize machine learning and generative AI solutions (AI-300) [M-AI300]
VIRTUAL TRAINING CENTRE
ma 4 jan. 2027
en 9 andere data
OVERVIEW
AI‑300 focuses on operationalizing machine learning and generative AI on Azure—covering MLOps, GenAIOps, automation, deployment, monitoring, and optimization of production AI systems.
This course covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimizing generative AI applications and agents using Microsoft Foundry. Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasizes collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.
Updated August 2026
OBJECTIVES
By the end of this course, learners will be able to:
design,
deploy,
automate,
monitor,
optimize
machine learning and generative AI solutions on Azure using MLOps and GenAIOps practices to deliver secure, scalable, and production‑ready AI systems.
AUDIENCE
This course is intended for data scientists, machine learning engineers, and DevOps professionals who want to design and operate production-grade AI solutions on Azure.
It is suited for learners with experience in Python, a foundational understanding of machine learning concepts, and basic familiarity with DevOps practices such as source control, CI/CD, and command-line tools, who are preparing to implement MLOps and GenAIOps workflows using Azure-native services.
CERTIFICATION
This course prepares you for the “Operationalize Machine Learning and Generative AI Solutions” exam (Exam AI-300).
It enables you to earn the Microsoft certification: Associate Engineer in Machine Learning Operations (MLOps and GenAIOps) on the Azure platform.
NEXT STEP
M-AI103 Develop AI Apps and Agents on Azure to design GenAI applications and agent-based solutions
M-AI500 Multi-Agent AI Solutions Expert to industrialize large-scale ecosystems of autonomous agents
M-DP750 Azure Databricks Data Engineer Associate to master data pipelines focused on Big Data and AI
M-AI200 Develop AI cloud solutions on Azure focused on the implementation and back-end architecture of
AI-powered cloud services
M-SC500 Cloud and AI Security Engineer Associate to govern and secure environments and identities
CONTENT
Module 1: Operationalize machine learning models (MLOps)
Experiment with Azure Machine Learning
Perform hyperparameter tuning with Azure Machine Learning
Run pipelines in Azure Machine Learning
Trigger Azure Machine Learning jobs with GitHub Actions
Trigger GitHub Actions with feature-based development
Work with environments in GitHub Actions
Deploy a model with GitHub Actions
Module 2:Operationalize generative AI applications (GenAIOps)
Plan and prepare a GenAIOps solution
Manage prompts for agents in Microsoft Foundry with GitHub
Evaluate and optimize AI agents through structured experiments
Automate AI evaluations with Microsoft Foundry and GitHub Actions
Monitor your generative AI application
Analyze and debug your generative AI app with tracing
€2.295
Klassikaal
max 16
Extract insights from visual data on Azure (AI-3008) [M-AI3008]
VIRTUAL TRAINING CENTER
ma 14 dec. 2026
en 9 andere data
OVERVIEW
Extract, analyze, and unlock actionable insights from visual data using Azure AI services.
This 1 day course focuses on building intelligent applications that can see, interpret, and reason over images and documents using different multimodal models and agent-based tools. Learners explore how visual and document inputs can be combined with language models to enable structured extraction, analysis, and decision-making workflows. The course emphasizes practical patterns for extracting information, orchestrating tools, and grounding model responses in visual data.
Updated 4/2026
OBJECTIVES
By the end of this course, learners will be able to:
Build and deploy AI solutions on Azure to extract insights from visual data.
Analyze images and visual content to derive meaningful insights that support business and application scenarios on Azure
Implement secure and scalable AI workloads on Azure for visual data processing and analysis.
Evaluate and optimize visual data AI solutions using Azure-native tools and services.
AUDIENCE
This course is designed for developers, AI engineers, and technical professionals who want to build applications that work with images and documents using multimodal, agent-driven approaches. It’s best suited for learners with basic programming experience and a general understanding of cloud or AI concepts
CERTIFICATION
None
CONTENT
Module 1: Develop a vision-enabled generative AI application
Use a vision-capable model in the Microsoft Foundry portal
Develop a vision-based chat app
Module 2: Generate images with AI
What are image-generation models?
Explore image-generation models in Microsoft Foundry portal
Create a client application that uses an image generation model
Module 3: Generate videos with Microsoft Foundry
Deploy a video generating model
Generate video from a prompt
Generate video in Python
Module 4: Analyze images with Content Understanding
What is Content Understanding?
Analyze images with Content Understanding
Module 5: Create a multimodal analysis solution with Azure Content Understanding
What is Azure Content Understanding?
Create a Content Understanding analyzer
Use the Content Understanding API
Module 6: Create an Azure Content Understanding client application
Prepare to use the AI Content Understanding API
Create a Content Understanding analyzer
Analyze content
Module 7: Extract data with Azure Document Intelligence
What is Azure Document Intelligence?
Use the Document Intelligence Studio
Use prebuilt models
Train and use custom models
Module 8: Create a knowledge mining solution with Azure AI Search
What is Azure AI Search?
Extract data with an indexer
Enrich extracted data with AI skills
Search an index
Persist extracted information in a knowledge store
€750
Klassikaal
max 16
Introduction to AI in Azure (AI-901) [M-AI901]
Nieuwegein (Iepenhoeve 5)
vr 13 nov. 2026
en 9 andere data
OVERVIEW
Explore the fundamental concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions.
Introduction to AI in Azure is an beginner-level course designed to explain the fundamental concepts of artificial intelligence and their practical implementation through Microsoft Azure services. The course familiarizes learners with important AI workloads, including generative AI, natural language processing, speech recognition, computer vision, and information extraction. Participants also gain practical experience working with Azure AI Foundry tools and other Azure AI features. It is particularly suitable for developers, AI engineers, and technology professionals who want to start developing intelligent applications and AI-driven solutions using the Azure platform.
OBJECTIVES
After this course participants should be able to:
Gain knowledge of the key concepts and terminology related to artificial intelligence.
Recognize major AI workloads, including generative AI, natural language processing, speech technologies, and computer vision.
Understand the foundational ideas behind large language models (LLMs), prompting techniques, and AI agents.
Learn about the Microsoft Azure services that support the development of AI-based applications.
Develop simple AI solutions using tools available in Azure AI Foundry.
Use Azure AI services to process and interpret text, speech, and visual data.
Derive meaningful insights from unstructured information through AI-driven technologies.
Incorporate responsible AI practices while designing and deploying AI solutions.
AUDIENCE
This course is for aspiring technology professionals at the beginning of their career in AI solution development. Some knowledge of Python coding syntax and programming techniques is useful. Additionally, knowledge of core cloud concepts, including cloud storage, cloud compute, and cloud-based authentication and authorization is recommended.
CONTENT
Module 1: AI concepts for developers and technology professionals
Introduction to AI concepts
Introduction to generative AI and agents
Introduction to natural language processing concepts
Introduction to AI speech concepts
Introduction to computer vision concepts
Introduction to AI-powered information extraction concepts
Module 2: Get started with AI applications and agents on Azure
Get started with AI in Azure
Get started with generative AI and agents in Azure
Get started with text analysis in Azure
Get started with speech in Azure
Get started with computer vision in Azure
Get started with AI-powered information extraction in Azure
€750
Klassikaal
max 16
Van Sabotage naar Succes
Twee teams.
Allebei slim, gemotiveerd en met meer dan genoeg talent in huis.
Het ene marcheert op de plaats.
Het andere boekt het ene resultaat na het andere.
Wat maakt het verschil?
Het verschil zit niet in competenties, tools of werkprocessen. Het zit in iets wat vrijwel niemand ziet: de onbewuste programmering waarmee we werken. In Van Sabotage naar Succes leer je door een nieuwe lens naar menselijk gedrag op de werkvloer (en aan de keukentafel).
Twee motivatiestijlen: PLICHT (‘eerst inspannen, dan ontspannen’) en WENS (‘eerst ontspannen, dan inspannen’) sturen ongemerkt hoe we samenwerken, beslissen en presteren. Ze brengen ons in beweging, maar laten ons rondcirkelen. Tot we ze leren herkennen.
Op basis van twintig jaar praktijkervaring, duizenden gesprekken en coachsessies introduceert dit boek een nieuwe lens die cruciaal is in leiderschap en verandering. Je ontdekt hoe PLICHT en WENS botsen in teams, welke drie onbewuste strategieën (WERKEN, PAKKEN, ONTVANGEN) we inzetten om iets voor elkaar te krijgen, en vooral… hoe een bewust geformuleerd DOEL het beste uit twee breinsystemen samenbrengt.
Of je nu leidinggeeft aan een team, verantwoordelijk bent voor een verandertraject of simpelweg de beste versie van jezelf wilt worden: dit boek geeft je het raamwerk én de praktische handvatten om onbenut potentieel te ontsluiten. In jezelf, in je team en in je organisatie.
€32
Boek
Implement data engineering solutions using Azure Databricks (DP-750) [M-DP750]
Eindhoven (Evoluon Noord Brabantlaan 1)
ma 21 dec. 2026
en 9 andere data
OVERVIEW
Design and deliver scalable data pipelines using Azure Databricks and the lakehouse architecture.
This course moves from foundational setup to production deployment, covering environment configuration and enterprise-grade governance. Learn to build robust ingestion pipelines, implement security with Unity Catalog, and deploy optimized workloads. By the end, you will have the practical skills to implement, secure, and maintain scalable lakehouse solutions that meet rigorous enterprise requirements.
OBJECTIVES
By the end of this course, learners will be able to:
Configure and manage Azure Databricks environments, including workspaces, clusters, and compute resources.
Ingest, transform, and model data using SQL and Python to support analytics and downstream workloads.
Build, deploy, and maintain scalable data pipelines using Azure Databricks and lakehouse patterns.
Apply data governance and security best practices using Unity Catalog for access control, governance, and data quality.
Integrate Azure Databricks with core Azure services, including Azure Storage, Azure Data Factory, Azure Monitor, Microsoft Entra ID, and Azure Key Vault.
Troubleshoot, monitor, and optimize Databricks workloads to ensure reliable, production‑ready data engineering solutions.
AUDIENCE
The target audience is data engineers who have fundamental knowledge of data analytics concepts, a basic understanding of cloud storage, and familiarity with data organization principles. They should be comfortable working with SQL and have experience using Python, including notebooks, for data engineering tasks. Learners are expected to have a good understanding of Azure Databricks workspaces and Unity Catalog, along with familiarity with data access patterns and core data engineering and data warehouse concepts. In addition, they should have foundational knowledge of Azure security, including Microsoft Entra ID, and be familiar with Git version control fundamentals.
CERTIFICATION
None
NEXT STEP
None
CONTENT
Module 1: Set up and configure an Azure Databricks environment
Explore Azure Databricks
Understand Azure Databricks architecture
Understand Azure Databricks Integrations
Select and Configure Compute in Azure Databricks
Create and organize objects in Unity Catalog
Module 2: Secure and govern Unity Catalog objects in Azure Databricks
Secure Unity Catalog objects
Govern Unity Catalog objects
Module 3: Prepare and process data with Azure Databricks
Design and implement data modeling with Azure Databricks
Ingest data into Unity Catalog
Cleanse, transform, and load data into Unity Catalog
Implement and manage data quality constraints with Azure Databricks
Module 4: Deploy and maintain data pipelines and workloads with Azure Databricks
Design and implement data pipelines with Azure Databricks
Implement Lakeflow Jobs with Azure Databricks
Implement development lifecycle processes in Azure Databricks
Monitor, troubleshoot and optimize workloads in Azure Databricks
€2.295
Klassikaal
max 16
Develop AI-enabled database solutions [M-DP800]
VIRTUAL TRAINING CENTER
di 12 jan. 2027
en 9 andere data
OVERVIEW
Design and develop AI‑enabled database solutions across Microsoft SQL platforms using Azure SQL, SQL Server, and Microsoft Fabric.
This course provides students with the knowledge and skills to design and develop AI enabled database solutions across Microsoft SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. It is intended for professionals who build modern data solutions that integrate structured and semi structured data and incorporate AI features into scalable enterprise applications. It will also be valuable for individuals who develop applications that rely on SQL based data services enhanced with vector search, embeddings, and other AI driven capabilities.
OBJECTIVES
After this course participants should be able to:
Design AI‑enabled database solutions across Azure SQL, SQL Server, and Microsoft Fabric
Apply advanced T‑SQL to build scalable, AI‑ready database architectures
Implement vectors, embeddings, and semantic search directly within SQL platforms
Build retrieval‑augmented generation (RAG) workflows using SQL‑based data
Integrate AI capabilities into enterprise applications without moving data
Secure, optimize, and deploy AI‑enabled SQL solutions using best practices
AUDIENCE
The audience for this course is data professionals who want to learn about designing and developing AI-enabled database solutions across Microsoft’s SQL platforms, including SQL Server, Azure SQL, and SQL databases in Microsoft Fabric. This role develops database solutions that include both structured and semi-structured data and integrates AI features into modern and highly scalable enterprise applications.
CERTIFICATION
None
CONTENT
Module 1: Design and develop database solutions
Design and implement database objects with SQL
Implement programmability objects with SQL
Write advanced T-SQL code
Implement SQL solutions by using AI-assisted tools
Module 2: Secure, optimize, and deploy database solutions
Implement data security and compliance with SQL
Optimize database performance
Implement CI/CD by using SQL Database Projects
Integrate SQL solutions with Azure services
Module 3: Implement AI capabilities in database solutions
Design and implement models and embeddings with SQL
Design and implement intelligent search with SQL
Design and implement RAG with SQL
€1.995
Klassikaal
max 16
Secure AI solutions in the cloud using Microsoft Defender for Cloud and Microsoft Entra (SC-5009) [M-SC5009]
Nieuwegein (Iepenhoeve 5)
do 31 dec. 2026
en 9 andere data
OVERVIEW
Secure AI workloads in Azure using cloud native protection and identity controls.
Secure AI solutions in the cloud by configuring AI workloads, applying cloud-native protections, and reinforcing security outcomes with identity controls. Learn how AI workloads authenticate, how trust boundaries are established, and how security posture and workload protection reduce risk using Microsoft Defender for Cloud and Microsoft Foundry. Extend these protections by using Microsoft Entra to design and apply identity and access controls that explain and harden earlier security decisions.
OBJECTIVES
After this course participants should be able to:
Apply security posture management and workload protection for AI services using Microsoft Defender for Cloud
Configure and secure Microsoft Foundry environments using cloud-native security controls
Design and apply identity and access controls for AI workloads using Microsoft Entra
AUDIENCE
This course is intended for professionals responsible for securing and operating AI workloads in the cloud. The audience includes cloud security engineers, platform engineers, and application teams working with AI services who need to understand how workload protection, security posture, and identity controls apply to AI environments. Familiarity with Azure, cloud-native security concepts, and basic identity and access principles is recommended.
CERTIFICATION
None
NEXT STEP
None
CONTENT
Module 1: Protect Microsoft Foundry solutions by using Microsoft Defender for Cloud
Understand how Microsoft Defender for Cloud supports AI security and governance in Azure
Protect AI workloads with Microsoft Defender for Cloud
Configure and manage guardrails in Microsoft Foundry
Secure Microsoft Foundry environments
Module 2:Secure AI identity infrastructure with Microsoft Entra
Understand identity architecture for AI workloads
Implement access management for Azure resources
Plan, implement, and administer Conditional Access
Manage Microsoft Entra Identity Protection
€750
Klassikaal
max 16