DSCI-273: Enterprise AI with Cloudera Machine Learning

Course Modules

• Introduction to Enterprise AI and Cloudera Machine Learning

  • Overview of Cloudera Machine Learning (CML) and its role in the AI lifecycle
  • Key features of Cloudera Data Platform (CDP) for enterprise AI
  • Building scalable AI solutions in an enterprise big data environment

• Data Preparation for Enterprise AI

  • Preparing large datasets for AI applications using Cloudera’s data engineering tools
  • Efficient data wrangling, cleaning, and transformation with Apache Spark and CML
  • Managing and processing data from various sources (structured, semi-structured, unstructured)

• Building Scalable AI Models with CML

  • Using machine learning algorithms to build enterprise-grade models
  • Model selection, training, and evaluation techniques for large datasets
  • Implementing deep learning, ensemble learning, and reinforcement learning with CML

• Implementing Natural Language Processing (NLP) for Enterprise AI

  • Applying NLP techniques to process and analyze textual data at scale
  • Building text classification, sentiment analysis, and topic modeling applications
  • Using pre-built NLP models and fine-tuning them for enterprise use cases

• Model Deployment and Monitoring at Scale

  • Deploying machine learning models in production environments using Cloudera Machine Learning
  • Managing the lifecycle of AI models with model versioning and retraining pipelines
  • Monitoring and optimizing AI models for performance and accuracy in production

• AI for Business Intelligence and Decision-Making

  • Integrating AI models into business intelligence (BI) workflows
  • Using AI-driven analytics to support decision-making processes in enterprises
  • Case studies on how AI is transforming business operations in various sectors like finance, healthcare, and marketing

• Automating AI Pipelines with CML

  • Building and automating end-to-end AI pipelines for enterprise-scale workflows
  • Integrating with Apache NiFi, Apache Airflow, and other orchestration tools
  • Streamlining model training, deployment, and monitoring through automated pipelines

• Ethics and Responsible AI in the Enterprise

  • Addressing ethical concerns and challenges in deploying AI at scale
  • Implementing fairness, transparency, and accountability in AI models
  • Ensuring compliance with AI regulations and guidelines for responsible AI deployment

• Future Trends in Enterprise AI

  • Exploring emerging trends in AI such as explainable AI (XAI), federated learning, and AI optimization
  • Understanding the future potential of AI in large-scale enterprise environments
  • Preparing for the next generation of AI applications using Cloudera's advanced machine learning capabilities