• Data scientists and engineers working with big data frameworks
• AI/ML professionals interested in scalable deep learning
• Developers seeking to integrate AI models in distributed environments
• Analysts and architects exploring deep learning pipelines on Spark

• Basic understanding of Python and machine learning concepts
• Familiarity with Apache Spark fundamentals
• Prior experience with data pipelines is helpful but not required

Course Outline:

Module 1: Foundations of AI and Deep Learning

• Introduction to Artificial Intelligence and Deep Learning
• Key components of neural networks
• Overview of big data and Apache Spark architecture
• Spark MLlib for machine learning and deep learning
• Comparing TensorFlow, PyTorch, and Keras with Spark integrations

Module 2: Distributed Deep Learning with Apache Spark

• Deep learning workflows with Spark
• Using MLlib for classification, regression, and clustering
• Introduction to TensorFlowOnSpark
• Hands-on: Building a neural network using Spark and Keras
• Hands-on: Distributed training and tuning hyperparameters

Module 3: Scalable Deployment and Use Cases

• Model evaluation and performance metrics
• Exporting and saving models in Spark pipelines
• Hands-on: Inference at scale with distributed models
• Use case walkthroughs: Fraud detection, sentiment analysis, and image classification
• Best practices for deploying AI models in production Spark clusters