Description

Introduction
In the era of data-driven innovation, deploying machine learning (ML) and artificial intelligence (AI) models at scale is essential for organizations to extract actionable insights. Cloudera provides an end-to-end platform for building, training, and deploying ML models seamlessly. This course focuses on leveraging Cloudera Machine Learning (CML) and other tools to operationalize AI projects, manage data pipelines, and scale model deployment across hybrid and multi-cloud environments.

Prerequisites

1. Basic knowledge of machine learning concepts and workflows.
2. Familiarity with Python or R programming for data analysis.
3. Understanding of Hadoop and distributed computing.
4. Experience with Cloudera Data Platform (CDP) is helpful but not required.

Table of Contents

1. Introduction to Cloudera for Machine Learning
   1.1 Overview of Cloudera’s AI and ML Capabilities
   1.2 Benefits of Using Cloudera Machine Learning (CML)
   1.3 Key Components for Machine Learning in CDP
2. Setting Up Your Machine Learning Environment
   2.1 Deploying Cloudera Machine Learning on CDP
   2.2 Configuring ML Workspaces and Resources
   2.3 Integrating Cloudera with Jupyter Notebooks and IDEs
   2.4 Setting Up User Roles and Permissions
3. Data Engineering for Machine Learning
   3.1 Preparing Data Pipelines with Apache Spark
   3.2 Using Cloudera Data Engineering for Feature Engineering
   3.3 Managing Big Data Workflows with Apache NiFi
   3.4 Data Preprocessing and Cleaning Best Practices
4. Model Development and Training
   4.1 Introduction to ML Libraries Supported by Cloudera
   4.2 Building Models with Scikit-learn, TensorFlow, and PyTorch
   4.3 Distributed Training on Cloudera Infrastructure
   4.4 Hyperparameter Tuning and Experiment Tracking
5. Model Deployment and Serving
   5.1 Overview of Model Deployment Options in Cloudera
   5.2 Deploying Models Using Cloudera’s ML Workspaces
   5.3 Setting Up APIs for Real-Time Model Serving
   5.4 Monitoring and Managing Deployed Models
6. Operationalizing Machine Learning
   6.1 Building CI/CD Pipelines for ML Models(Ref: Cloudera Performance Tuning: Optimizing Big Data Workloads )
   6.2 Automating Model Retraining and Updates
   6.3 Integrating ML Models with Business Applications
   6.4 Ensuring Scalability in Hybrid and Multi-Cloud Deployments
7. Performance Optimization and Troubleshooting
   7.1 Optimizing Resource Usage for Training and Inference
   7.2 Identifying and Resolving Bottlenecks in Pipelines
   7.3 Leveraging GPU Acceleration in Cloudera ML
   7.4 Advanced Debugging Techniques
8. AI Governance and Compliance
   8.1 Managing Data Lineage for ML Models with Cloudera Navigator
   8.2 Implementing Bias Detection and Fairness in AI Models
   8.3 Ensuring Compliance with AI and Data Privacy Regulations
   8.4 Monitoring Model Drift and Auditing AI Performance
9. Real-Time Machine Learning with Cloudera
   9.1 Streaming Data Integration with Kafka and Flume
   9.2 Deploying Real-Time Predictive Analytics Workflows
   9.3 Combining Batch and Real-Time Processing in Apache Spark
   9.4 Examples of Real-Time AI Applications
10. Scaling AI Projects Across the Enterprise
    10.1 Collaboration with Data Scientists, Engineers, and Analysts
    10.2 Scaling ML Projects to Serve Thousands of Users
    10.3 Establishing an AI Center of Excellence Using Cloudera
    10.4 Managing Large-Scale AI Projects with Agile Methodologies
11. Hands-On Labs and Case Studies
    11.1 Preparing and Preprocessing Data for ML Models
    11.2 Building and Training a Scalable ML Model in CML
    11.3 Deploying a Real-Time Model Using REST APIs
    11.4 End-to-End Workflow: From Data Pipeline to Deployment
12. Emerging Trends and Future Directions in AI with Cloudera
    12.1 Advancements in Edge AI and IoT with Cloudera
    12.2 AI-Driven Automation in Business Workflows
    12.3 Exploring the Role of Generative AI in Enterprise Solutions
    12.4 Preparing for Next-Generation AI Technologies

Conclusion
This course equips participants with the skills to leverage Cloudera for end-to-end machine learning workflows, from data engineering to model deployment and governance. By mastering these tools and techniques, you’ll be prepared to build scalable AI solutions that drive innovation and deliver business value. Transform your data into actionable intelligence with Cloudera’s powerful machine learning capabilities.

Reference