“The Microsoft Fabric implementation program gave our data engineering team a structured path from legacy pipelines to a modern lakehouse architecture.”
Enterprise Program Brief
Databricks Machine Learning
This advanced Databricks program covers end-to-end machine learning implementation from feature engineering to model serving. Participants work with MLflow for experiment tracking and model governance, build and tune models on Spark, and connect ML workflows to production data pipelines.
VNode ITeSIntermediate to AdvancedEnterprise Track
Download Program Brief (PDF)Program Snapshot
Engagement scoped to cohort
Duration3-4 Days
FormatVirtual / Onsite
LevelIntermediate to Advanced
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
Scoping call typically scheduled within one business day
VNode ITeS delivers Databricks Machine Learning as an MCT-led VNode ITeS program for Data Engineers, Data Scientists, ML Engineers. Typical duration is 3-4 Days (Virtual / Onsite). 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
Data Engineers, Data Scientists, ML Engineers
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Learn to build, train, evaluate, and deploy machine learning models using Databricks and MLflow across the full ML lifecycle.
Prerequisites
- Hands-on experience with Python and data analysis workflows
- Working knowledge of Spark basics
- Familiarity with machine learning fundamentals (classification, regression)
Program Outcomes
Capabilities your teams will gain
- Implement MLflow-based experiment tracking and model registry workflows
- Build scalable feature engineering pipelines on Spark
- Train, evaluate, and tune classification and regression models in Databricks
- Deploy and serve ML models integrated with enterprise data workflows
Enterprise Delivery
Private cohort delivery
VNode-designed, vendor-aligned instructor-led program. Scope role mix, delivery format, and rollout goals for your cohort.
Curriculum
Curriculum roadmap
- MLflow experiment tracking and model registry
- Feature engineering with Apache Spark
- Model training, evaluation, and hyperparameter tuning
- Model serving and production deployment patterns
1Module 1
MLflow Tracking and Model Registry
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Module 1
MLflow Tracking and Model Registry
Implement experiment tracking with MLflow, manage model versions, and use the Databricks model registry for production governance.
- Implement experiment tracking with MLflow
- manage model versions
- and use the Databricks model registry for production governance
2Module 2
Feature Engineering with Spark
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Module 2
Feature Engineering with Spark
Build scalable feature engineering pipelines on Apache Spark for ML-ready datasets with proper transformations and data quality checks.
- Build scalable feature engineering pipelines on Apache Spark for ML-ready datasets with proper transformations
- data quality checks
3Module 3
Model Training, Evaluation, and Tuning
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Module 3
Model Training, Evaluation, and Tuning
Train classification and regression models in Databricks, evaluate performance metrics, and apply hyperparameter tuning for optimization.
- Train classification
- regression models in Databricks
- evaluate performance metrics
- and apply hyperparameter tuning for optimization
4Module 4
Model Serving and Deployment
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Module 4
Model Serving and Deployment
Deploy and serve registered models from Databricks Model Serving, integrated with production data pipelines for real-time inference.
- serve registered models from Databricks Model Serving
- integrated with production data pipelines for real-time inference
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Intermediate to Advanced practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Enables teams to build and operationalize scalable ML pipelines in Databricks with enterprise-grade experiment tracking and deployment standards.
- Domain-specific datasets and modeling scenarios
- MLOps lifecycle customization aligned to team standards
- Extended module for model monitoring and lifecycle governance
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.
Enterprise Proof
Trusted delivery outcomes
“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
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
