Course Overview
Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera AI, part of the Cloudera platform, provides the solution, giving data science teams the required resources.
This four-day course covers machine learning workflows and operations using Cloudera AI. Participants explore, visualize, and analyze data. You will also train, evaluate, and deploy machine learning models.
The course walks through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The demonstrations and exercises are conducted in Python (with PySpark) using Cloudera AI.
Who should attend
The course is designed for data scientists who need to understand how to utilize Cloudera AI and the Cloudera platform to achieve faster model development and deliver production machine learning at scale. Data engineers, developers, and solution architects who collaborate with data scientists will also find this course valuable.
Course Objectives
Through lecture and hands-on exercises, you will learn how to:
- Utilize Cloudera SDX and other components of the Cloudera platform
- to locate data for machine learning experiments
- Use an Cloudera Accelerators for Machine Learning Projects (AMPs)
- Manage machine learning experiments
- Connect to various data sources and explore data
- Deploy an ML model as a REST API
- Manage and monitor deployed ML models
Course Content
- Projects and Sessions
- ML Runtimes
- Jobs and Pipelines
- Machine Learning Workflow
- Applications and AMPs
- Experiments and Models
- Model deployment
- Data Visualization
- CI/CD workflows
- Cloudera AI API
- Metrics and Monitoring
- Performance and GPUs
Englisch