Large Scale ML & Real-Time Application

This course equips you with the knowledge and skills to tackle big data challenges in Supply Chain Management (SCM) using Large Scale Machine Learning (LSML) and real-time applications. You'll explore techniques for handling massive datasets and building models that deliver insights and optimize your supply chains in real-time, enabling faster and more informed decision-making.

• Define Large Scale Machine Learning and its relevance to handling big data in SCM.

• Explore distributed computing frameworks (e.g., Apache Spark) for training and deploying LSML models.

• Understand real-time streaming analytics and its applications in monitoring and optimizing supply chain processes.

• Identify challenges and considerations for building and deploying real-time AI applications in SCM environments.

• Analyze the benefits and potential impact of LSML and real-time applications on supply chain performance.

1. Taming Big Data with Large Scale ML

• Introduction to Large Scale Machine Learning (LSML): Understanding the need for LSML when dealing with massive SCM datasets.

• Exploring Distributed Computing Frameworks: Leveraging frameworks like Apache Spark for parallelizing training and handling large datasets.

• Hands-on Exercises (Optional): Utilizing online tools or platforms to explore basic concepts of distributed computing for LSML tasks (may involve basic scripting).

• Case Studies: Examining applications of LSML in SCM for tasks like demand forecasting with high-volume sales data or real-time anomaly detection in sensor data.

2. The Power of Real-Time Insights

• Introduction to Real-Time Streaming Analytics: Processing and analyzing data streams for immediate insights and decision-making.

• Understanding Real-Time Machine Learning Applications: Building and deploying models that learn and adapt from continuous data streams in SCM.

• Exploring Real-Time Data Stream Processing Tools (e.g., Apache Kafka): Platforms for ingesting, processing, and analyzing real-time data in supply chains.

• Hands-on Exercises (Optional): Working with online tools or simulated streaming data to explore basic real-time analytics techniques (may involve basic coding).

• Case Studies: Analyzing real-world examples of real-time AI for optimizing transportation routes based on live traffic data or monitoring warehouse operations with real-time sensor data.

3. Considerations for Real-World Implementation

• Challenges and Considerations for LSML and Real-time Applications: Addressing scalability, infrastructure needs, and model explainability in SCM deployments.

• Mitigating Risks and Ensuring Responsible AI: Security, bias, and fairness considerations for implementing LSML in supply chain processes.

• The Future of Large Scale ML and Real-Time Applications in SCM: Exploring cutting-edge trends and their potential impact on the future of supply chain management.

• 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