Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Data scientists and machine learning engineers
• Big data analysts and data engineers
• AI/ML practitioners looking to scale their models
• IT professionals and solution architects in data-driven roles
• Technical project managers and business analysts supporting AI initiatives

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

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

• Understand the relationship between big data, AI, and machine learning
• Prepare and manage big data for AI/ML applications
• Develop and train machine learning models using large-scale datasets
• Utilize cloud platforms and distributed frameworks for AI integration
• Deploy and monitor AI/ML models in production environments

DAY 1

Big Data Foundations for AI and ML

• Welcome and introduction
• Pre-test
• Introduction to big data architecture and processing frameworks
• Overview of AI and ML concepts in the big data context
• Hadoop, Spark, and distributed computing essentials
• Data lakes, real-time processing, and data pipelines
• Case study

DAY 2

Preparing Big Data for Machine Learning

• Data ingestion from multiple sources (structured, unstructured, streaming)
• Data preprocessing at scale: cleaning, transformation, feature engineering
• Data wrangling with Spark, Pandas, or PySpark
• Data versioning and reproducibility
• Workshop

DAY 3

Machine Learning Algorithms and Model Training

• Supervised learning: regression, classification (decision trees, SVM, XGBoost)
• Unsupervised learning: clustering, dimensionality reduction
• Introduction to deep learning for big data (TensorFlow, PyTorch basics)
• Training ML models on distributed systems using Spark MLlib or cloud services
• Hands-on lab

DAY 4

Scalable AI Deployment and Monitoring

• Model deployment options: batch vs. real-time inference
• Using cloud platforms (AWS SageMaker, Azure ML, Google Vertex AI)
• Building APIs and microservices for AI integration
• Model monitoring, retraining, and drift detection
• Group exercise

DAY 5

AI Strategy, Use Cases, and Capstone Project

• Enterprise integration of AI & big data for strategic value
• Use cases: predictive maintenance, fraud detection, customer insights
• Ethics and governance in AI & big data projects
• Final project
• Post-test
• Certificate ceremony