Large Scale ML & Real-Time Application

This course is designed to provide a comprehensive understanding of large-scale machine learning and real-time applications in the context of the Healthcare & Life Sciences industries. Participants will learn how to design, implement, and deploy scalable ML systems that can handle massive volumes of data and provide real-time insights for critical decision-making. The course covers distributed computing frameworks, streaming data processing, and optimization techniques for building high-performance AI solutions tailored to the unique challenges and requirements of the healthcare and life sciences domains.

• Understand the challenges and opportunities of large-scale ML and real-time applications in the Healthcare & Life Sciences industries

• Design and implement distributed ML architectures for processing massive volumes of healthcare and life sciences data

• Apply optimization techniques for training large-scale ML models efficiently and cost-effectively

• Develop real-time data processing pipelines for ingesting, transforming, and analyzing streaming data from medical devices and sensors

• Deploy and manage scalable ML systems in production environments for real-time decision support and personalized healthcare

1. Foundations of Large-Scale Machine Learning

• Overview of large-scale ML and its applications in the Healthcare & Life Sciences industries

• Distributed computing frameworks for big data processing (e.g., Apache Hadoop, Apache Spark)

• Data partitioning and parallel processing techniques for ML workloads

• Scalable feature engineering and data preprocessing techniques

• Hands-on exercises: Setting up a distributed computing environment and processing large-scale healthcare and life sciences datasets

2. Distributed Machine Learning Architectures

• Distributed training architectures for machine learning models (e.g., parameter server, ring-allreduce)

• Federated learning and its applications in privacy-preserving ML for healthcare data

• Model parallelism and data parallelism strategies for training large-scale models

• Distributed hyperparameter optimization and model selection techniques

• Hands-on exercises: Implementing distributed training for a large-scale ML model on healthcare or life sciences data

3. Real-Time Data Processing and Streaming Analytics

• Introduction to real-time data processing and its importance in the Healthcare & Life Sciences industries

• Streaming data ingestion and processing frameworks (e.g., Apache Kafka, Apache Flink)

• Real-time feature extraction and data transformation techniques

• Stateful stream processing and windowing techniques for temporal analysis

• Hands-on exercises: Building a real-time data processing pipeline for streaming healthcare or life sciences data

4. Deploying and Managing Large-Scale ML Systems

• Architectures for deploying large-scale ML systems in production (e.g., microservices, containerization)

• Serverless computing and its applications in real-time ML inference

• Monitoring and logging techniques for ensuring the reliability and performance of ML systems

• Continuous integration and continuous deployment (CI/CD) pipelines for ML models

• Hands-on exercises: Deploying a large-scale ML system for real-time decision support in a healthcare or life sciences use case

• Strong proficiency in programming with Python and familiarity with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

• Understanding of distributed computing concepts and big data technologies (e.g., Apache Hadoop, Apache Spark)

• Knowledge of real-time data processing and streaming frameworks (e.g., Apache Kafka, Apache Flink) is beneficial but not required