BritePathGlobal

Course Overview

About this course

This comprehensive instructor-led program combines Artificial Intelligence, Machine Learning, and the Databricks Lakehouse ecosystem into one practical learning experience. Participants will progress from AI/ML foundations and model development to large-scale data processing, Databricks Machine Learning, MLflow, model management, deployment, MLOps, and Generative AI workflows. The program emphasizes practical implementation through integrated labs, real-world use cases, and a complete end-to-end capstone project.

What's Included

Everything you get

✓ Live Instructor-Led Online Training

✓ Artificial Intelligence & Machine Learning Foundations

✓ Python for AI/ML

✓ Data Analysis and Machine Learning

✓ Supervised and Unsupervised Learning

✓ Deep Learning Fundamentals

✓ Generative AI and LLM Fundamentals

✓ Databricks Lakehouse Fundamentals

✓ Apache Spark and Spark DataFrames

✓ Delta Lake and Data Management

✓ Machine Learning with Databricks

✓ Feature Engineering

✓ MLflow Experiment Tracking

✓ Model Registry and Model Management

✓ Model Serving and Deployment

✓ MLOps and Production ML Workflows

✓ Integrated AI/ML and Databricks Labs

✓ Real-World Industry Use Cases

✓ End-to-End AI/ML & Databricks Capstone Project

✓ Course Materials

✓ Instructor Guidance and Q&A Support

✓ Training Completion Certificate

What You'll Achieve

Learning outcomes

OUTCOME 1

Build a strong foundation in Artificial Intelligence and Machine Learning

OUTCOME 2

Develop and evaluate practical Machine Learning models

OUTCOME 3

Understand Deep Learning and Generative AI concepts

OUTCOME 4

Work with the Databricks Lakehouse environment

OUTCOME 5

Process and manage large-scale data using Apache Spark and Delta Lake

OUTCOME 6

Build and manage Machine Learning workflows in Databricks

OUTCOME 7

Use MLflow for experiment tracking and model lifecycle management

OUTCOME 8

Understand model registration, serving, deployment, and monitoring

OUTCOME 9

Apply MLOps practices to production Machine Learning workflows

OUTCOME 10

Integrate AI/ML models with Databricks data and engineering workflows

OUTCOME 11

Develop scalable, production-oriented AI/ML solutions

OUTCOME 12

Complete an end-to-end AI/ML and Databricks capstone project

Skills You'll Gain

Practical, job-ready skills

Artificial Intelligence Machine Learning Python for AI/ML Data Analysis Machine Learning Algorithms Deep Learning Generative AI Large Language Model Fundamentals Databricks Lakehouse Apache Spark Spark DataFrames Delta Lake Data Engineering Workflows Databricks Machine Learning Feature Engineering MLflow Experiment Tracking Model Management Model Serving MLOps Production ML Workflows End-to-End AI/ML Solution Development AI/ML & Databricks Project Development

Career Outcomes

Where this can take you

Step 1

AI/ML Engineer

Develop and implement end-to-end AI and Machine Learning solutions.

Step 2

Machine Learning Engineer

Build, optimize, deploy, and manage Machine Learning models.

Step 3

Data Scientist

Develop data-driven AI/ML solutions using modern data platforms.

Step 4

Databricks ML Engineer

Build Machine Learning workflows using the Databricks ecosystem.

Step 5

MLOps Engineer

Implement model deployment, lifecycle management, monitoring, and production workflows.

Step 6

AI Developer

Develop practical AI and Generative AI applications.

Step 7

Data Engineer

Build scalable data processing and preparation workflows using Spark and Delta Lake.

Step 8

AI/ML Solutions Developer

Integrate AI/ML models with scalable data and production workflows.

Curriculum

Course modules

1

Module 1 – AI, Machine Learning & Data Science Foundations

2

Module 2 – Python, Data Analysis & Machine Learning Workflows

3

Module 3 – Supervised & Unsupervised Learning

4

Module 4 – Deep Learning & Generative AI Fundamentals

5

Module 5 – Databricks Lakehouse & Workspace Architecture

6

Module 6 – Apache Spark, DataFrames & Distributed Processing

7

Module 7 – Delta Lake, Data Engineering & Feature Preparation

8

Module 8 – Machine Learning Development with Databricks

9

Module 9 – MLflow, Experiment Tracking & Model Lifecycle

10

Module 10 – Model Registry, Serving & Deployment

11

Module 11 – MLOps, Production Workflows & Monitoring

12

Module 12 – Integrated AI/ML & Databricks Solution Development

13

Module 13 – Real-World Industry Use Cases

14

Module 14 – End-to-End AI/ML & Databricks Capstone Project

How You'll Learn

Format & delivery

✓ Live Instructor-Led Online Sessions

✓ AI/ML Concept Demonstrations

✓ Python-Based Practical Exercises

✓ Machine Learning Labs

✓ Deep Learning and Generative AI Demonstrations

✓ Databricks Platform Demonstrations

✓ Apache Spark Hands-on Labs

✓ Delta Lake Practical Exercises

✓ MLflow Experiment Tracking Labs

✓ Model Deployment and Serving Exercises

✓ MLOps Workflow Demonstrations

✓ Real-World Industry Case Studies

✓ Integrated AI/ML and Databricks Projects

✓ Instructor Q&A Sessions

✓ Project-Based Learning

✓ Final End-to-End Capstone Project

Assessment & Certification

How you're assessed

Module-Based Knowledge Checks
AI/ML Practical Exercises
Machine Learning Model Assessments
Databricks Hands-on Labs
Spark and Delta Lake Practical Assessments
MLflow Practical Assessment
Model Deployment and MLOps Exercises
Integrated Project-Based Assessment
End-to-End AI/ML & Databricks Capstone Assessment
Final Course Assessment
Certificate of Completion upon Successful Completion

Is This Course For You?

Who should attend

AI and Machine Learning Professionals
Aspiring AI/ML Engineers
Data Scientists
Machine Learning Engineers
Data Engineers
MLOps Professionals
Databricks Professionals
Software Developers
Data Analysts
IT Professionals
Professionals Transitioning into AI, Machine Learning, or Databricks
Students Pursuing AI, Data, and Machine Learning Careers
Professionals Seeking an Integrated AI/ML and Databricks Skill Set

AI + Machine Learning + Databricks – Complete Instructor-Led Training