Machine Learning Engineering with Databricks Training Course

 Machine Learning Engineering with Databricks Training Course 

Introduction 

Machine Learning Engineering with Databricks Training Course provides comprehensive practical knowledge and technical expertise in designing, developing, deploying, and managing scalable machine learning solutions using the Databricks Lakehouse Platform. The course explores modern machine learning engineering, Apache Spark, MLflow, automated machine learning workflows, feature engineering, model training, model evaluation, and distributed computing. Participants gain practical experience in building production-ready machine learning pipelines, implementing MLOps best practices, leveraging cloud-based data infrastructure, and transforming complex datasets into actionable business intelligence. Emphasis is placed on scalable machine learning architectures, automated model lifecycle management, responsible artificial intelligence, and enterprise-grade machine learning deployment. 

The training also examines advanced machine learning operations, model governance, continuous integration and continuous delivery, real-time inference, model monitoring, and performance optimization within Databricks environments. Through practical exercises and global industry case studies, participants explore how organizations across financial services, healthcare, manufacturing, telecommunications, and retail use machine learning engineering to improve operational efficiency, predictive accuracy, and digital transformation. The course equips professionals with industry-relevant skills to implement end-to-end machine learning solutions, optimize distributed model development, and establish reliable machine learning systems aligned with modern enterprise requirements. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Develop scalable machine learning solutions using Databricks.
2. Apply Apache Spark for distributed machine learning.
3. Implement MLflow for experiment tracking and model management.
4. Design automated machine learning pipelines and workflows.
5. Perform advanced feature engineering and feature selection.
6. Build, train, and evaluate predictive machine learning models.
7. Implement MLOps, model deployment, and lifecycle automation.
8. Integrate machine learning with cloud data engineering architectures.
9. Apply AutoML techniques to accelerate model development.
10. Implement real-time machine learning inference solutions.
11. Optimize model performance and distributed computing resources.
12. Establish model governance, security, and responsible AI practices.
13. Monitor production models and implement continuous improvement strategies.

Organizational Benefits
 

1. Improved predictive analytics and data-driven decision-making.
2. Reduced machine learning development and deployment costs.
3. Increased automation of enterprise machine learning workflows.
4. Enhanced operational efficiency through scalable AI solutions.
5. Improved model accuracy and performance monitoring.
6. Faster delivery of machine learning applications.
7. Stronger data governance and model lifecycle management.
8. Better integration of enterprise data platforms.
9. Increased innovation through advanced AI capabilities.
10. Improved organizational competitiveness and digital transformation.

Target Audiences
 

1. Machine Learning Engineers and AI Developers.
2. Data Scientists and Applied Scientists.
3. Data Engineers and Big Data Specialists.
4. Cloud Architects and Solutions Architects.
5. MLOps Engineers and DevOps Professionals.
6. Business Intelligence and Analytics Professionals.
7. IT Managers and Technology Consultants.
8. Software Engineers and Technical Project Managers.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Databricks Machine Learning Engineering
 

• Databricks Lakehouse architecture and machine learning ecosystem.
• Machine learning engineering principles and enterprise applications.
• Apache Spark fundamentals and distributed data processing.
• Databricks notebooks, workspaces, and collaborative development.
• Machine learning lifecycle and production architecture.
• Case study: Netflix-style recommendation systems and distributed analytics.

Module 2: Data Preparation and Feature Engineering
 

• Data ingestion, transformation, and preparation using Spark.
• Data cleaning, validation, and preprocessing techniques.
• Feature extraction, selection, and transformation.
• Feature Store concepts and reusable feature management.
• Handling structured, semi-structured, and unstructured datasets.
• Case study: Amazon-style customer segmentation and predictive recommendations.

Module 3: Machine Learning Model Development
 

• Supervised and unsupervised machine learning algorithms.
• Classification, regression, and clustering techniques.
• Model training using Databricks ML libraries.
• Hyperparameter tuning and model optimization.
• Model evaluation, validation, and performance metrics.
• Case study: Banking credit-risk prediction and fraud detection.

Module 4: MLflow and Experiment Tracking
 

• MLflow architecture and experiment management.
• Tracking parameters, metrics, artifacts, and model versions.
• Model Registry and centralized model governance.
• Reproducible machine learning experimentation.
• Model packaging and deployment preparation.
• Case study: Enterprise pharmaceutical model experimentation and tracking.

Module 5: Automated Machine Learning and MLOps
 

• Databricks AutoML and automated model development.
• Building reproducible machine learning pipelines.
• Continuous integration and continuous delivery for machine learning.
• Workflow orchestration and automated retraining.
• Model versioning, testing, and lifecycle management.
• Case study: Retail demand forecasting and automated model retraining.

Module 6: Model Deployment and Production Inference
 

• Deploying machine learning models using Databricks.
• Real-time and batch inference architectures.
• Model serving and scalable API integration.
• Integrating models with enterprise applications.
• Production deployment, testing, and performance optimization.
• Case study: Uber-style real-time demand prediction and dynamic pricing.

Module 7: Model Monitoring, Governance, and Responsible AI
 

• Production model monitoring and performance evaluation.
• Data drift, model drift, and anomaly detection.
• Model explainability and responsible AI principles.
• Security, access control, and regulatory compliance.
• Model governance, auditing, and risk management.
• Case study: Financial institution model governance and credit-risk monitoring.

Module 8: Advanced Machine Learning Engineering Capstone
 

• Designing end-to-end machine learning engineering solutions.
• Integrating Databricks with cloud data platforms.
• Optimizing distributed training and computational resources.
• Building scalable enterprise machine learning architectures.
• Implementing production-ready MLOps workflows.
• Case study: Global telecommunications customer churn prediction project.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical demonstrations using Databricks workspaces.
• Hands-on machine learning engineering exercises.
• Real-world enterprise projects and international case studies.
• Group assignments, collaborative problem-solving, and technical assessments.
• Capstone project development and participant evaluation.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.