DSCI-273: Enterprise AI with Cloudera Machine Learning Course Overview

Prerequisites for DSCI-273: Enterprise AI with Cloudera Machine Learning

To successfully undertake the DSCI-273: Enterprise AI with Cloudera Machine Learning course, participants should have the following minimum knowledge and skills:

• Basic understanding of machine learning concepts and terminology.
• Familiarity with Python programming.
• Experience with data science tools and methodologies.
• Basic knowledge of cloud computing environments.
• Understanding of Git for version control.

These prerequisites ensure that you have the foundational knowledge needed to effectively engage with the course material and gain the most from this advanced training.

Target Audience for DSCI-273: Enterprise AI with Cloudera Machine Learning

1. Introduction:

The DSCI-273 course equips professionals with essential skills in utilizing Cloudera Machine Learning, Generative AI, and Large Language Models (LLMs) to develop powerful enterprise AI solutions.

2. Target Audience:

• Data Scientists

• Machine Learning Engineers

• AI Researchers

• Data Analysts

• Enterprise AI Solution Developers

• Big Data Engineers

• Business Intelligence Professionals

• IT Consultants

• AI/ML Team Leads

• Data Science Managers

• Enterprise Architects

• Technical Project Managers in AI/ML

• Software Developers focusing on AI

• Cloud Data Engineers

• Chief Data Officers (CDOs)

• Tech-savvy Business Analysts

Learning Objectives - What you will Learn in this DSCI-273: Enterprise AI with Cloudera Machine Learning?

1. Introduction: The DSCI-273: Enterprise AI with Cloudera Machine Learning course empowers data scientists and machine learning engineers to harness enterprise data using Generative AI and Large Language Models (LLMs) in Cloudera Machine Learning to create scalable, robust AI solutions.

2. Learning Objectives and Outcomes:

• Utilize Cloudera SDX and other components to locate and integrate data for ML experiments.
• Use Applied ML Prototypes (AMPs) to streamline AI solution development.
• Manage and conduct machine learning experiments effectively within Cloudera ML.
• Explore and visualize enterprise data for actionable insights.
• Select appropriate LLM models tailored to specific business use cases.
• Configure prompts and apply Prompt Engineering techniques to fine-tune LLMs.
• Implement Retrieval Augmented Generation (RAG) for enhanced data-driven responses.
• Deploy machine learning models as REST APIs for scalable solutions.
• Monitor and manage deployed models to ensure performance and accuracy.
• Leverage autoscaling, GPU settings, and other performance optimization techniques for efficient model training and deployment.