Microsoft AI Foundry track — available for enterprise team delivery

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Enterprise Program Brief

Azure Data Scientist Associate (DP-100)

Warning This certification has been retired. Certification-aligned program for data scientists training, deploying, and operationalizing machine learning solutions in Azure.

Track: Azure Data Scientist AssociateMicrosoftIntermediate
Plan Private DeliveryConsult a Solutions Architect
Download Program Brief (PDF)

Microsoft Credential Retired

This Microsoft certification can no longer be earned.

Program Snapshot

Engagement scoped to cohort

Duration2 to 4 days
FormatVirtual, On-site, or Hybrid
LevelIntermediate
Course CodeDP-100
Certification TrackAzure Data Scientist Associate
SubjectData Science
  • MCT-led delivery
  • Private cohort / virtual ILT
  • Typical response: 1 business day
Scoping call typically scheduled within one business day

VNode ITeS delivers Azure Data Scientist Associate (DP-100) 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.

  • Data Scientist
  • Data Science
  • Tailored Team Delivery
  • Implementation-Focused

Overview

Executive overview

Warning This certification has been retired. Certification-focused enterprise program aligned to Azure Data Scientist Associate (DP-100).

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

Enterprise Delivery

Private cohort delivery

VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.

Request Private CohortBook a ConsultationOpen in learning platformProgram brief (PDF)

Curriculum

Curriculum roadmap

  • Azure Machine Learning workspace setup and environment management
  • Data exploration, preparation, and feature engineering
  • Model training, AutoML, and experiment tracking
  • Model evaluation, responsible AI, and deployment readiness
  • Model deployment, monitoring, and MLOps practices
1

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
2

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
3

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
4

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
5

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 labs
  • Private cohort delivery
  • Intermediate practitioner depth
  • Business 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 Source

Resources

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.

Program brief (PDF)View Official Source

Delivery Capability

Enterprise-grade instruction

View delivery capability profile

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

Plan delivery
Kriya AI