“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Track: Azure Data Scientist Associate
MicrosoftIntermediate
Azure Data Scientist Associate
Certification-aligned program for data scientists training, deploying, and operationalizing machine learning solutions in Azure.
Duration
2 to 4 days
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Microsoft
Data ScienceIT
Azure Data Scientist Associate
Azure Machine Learning
AI
On this page
Ideal for
Data ScientistData ScienceTailored Team DeliveryImplementation-Focused
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Azure Data Scientist Associate as an MCT-led Microsoft program for Data Scientist. Typical duration is 2 to 4 days (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for data scientists and ML practitioners using Azure to manage model development and deployment.
Overview
Executive overview
Certification-focused enterprise program aligned to Azure Data Scientist Associate (DP-100).
Readiness
Prerequisites
- Hands-on experience in the relevant Microsoft workload area.
- Familiarity with core product concepts and enterprise delivery expectations.
Program Outcomes
Capabilities your teams will gain
Strengthen readiness for DP-100 certification objectives
Build deeper delivery capability in data science scenarios
Prepare teams for more confident project execution in the target Microsoft workload
Support structured certification-led upskilling across the organization
Curriculum
Curriculum roadmap
1
Azure Machine Learning workspace setup and environment management
2
Data exploration, preparation, and feature engineering
3
Model training, AutoML, and experiment tracking
4
Model evaluation, responsible AI, and deployment readiness
5
Model deployment, monitoring, and MLOps practices
1Module 1
Azure ML Workspace and Environment Setup
+
Module 1
Azure ML Workspace and Environment Setup
Create and configure Azure Machine Learning workspaces, compute clusters and instances, environments, datastores, and assets for managed machine learning operations.
- configure Azure Machine Learning workspaces
- compute clusters
- environments
- and assets for managed machine learning operations
2Module 2
Data Exploration and Preparation
+
Module 2
Data Exploration and Preparation
Load, explore, and preprocess datasets using Azure ML data assets, apply feature engineering techniques, and build reusable data preparation pipelines.
- and preprocess datasets using Azure ML data assets
- apply feature engineering techniques
- and build reusable data preparation pipelines
3Module 3
Model Training and Experimentation
+
Module 3
Model Training and Experimentation
Train machine learning models using Azure ML pipelines and components, leverage Automated ML (AutoML), track experiments with MLflow, and optimize hyperparameters.
- Train machine learning models using Azure ML pipelines
- leverage Automated ML (AutoML)
- track experiments with MLflow
- and optimize hyperparameters
4Module 4
Model Evaluation and Responsible AI
+
Module 4
Model Evaluation and Responsible AI
Evaluate model performance metrics, apply Responsible AI dashboard insights, interpret model behavior using explainability tools, and ensure fairness and compliance.
- Evaluate model performance metrics
- apply Responsible AI dashboard insights
- interpret model behavior using explainability tools
- and ensure fairness
5Module 5
Model Deployment and MLOps
+
Module 5
Model Deployment and MLOps
Deploy models to real-time and batch endpoints, implement model monitoring and data drift detection, and apply CI/CD pipeline patterns for continuous model delivery.
- Deploy models to real-time
- batch endpoints
- implement model monitoring
- data drift detection
- and apply CI/CD pipeline patterns for continuous model delivery
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryIntermediate practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Strengthens cloud-based data science capability and supports a smoother path from experimentation to deployment.
- •Align labs to your production environment and platform priorities
- •Add focused exam-readiness reviews and instructor-led practice sessions
- •Extend into project-specific architecture or delivery coaching
Credentials
Certification & official source
- •Azure Data Scientist Associate
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios