Get Started with Databricks for Machine Learning

本课程是对 Databricks Data Intelligence Platform 的全面实践入门课程。您将跟随一段完整的端到端学习旅程，从工作区出发，最终完成对 AI 与机器学习的入门介绍，涵盖在 Databricks 上处理数据所需的日常技能。

课程内容包括：

导航 Databricks 工作区、compute 选项，以及湖仓与 Genie 体验的对比。 通过 Unity Catalog 理解和摄取数据，包括文件上传与 Catalog Explorer 的使用。 在 Databricks Notebooks 中工作，并使用 Genie Code 编写和调试代码。 使用 SQL 查询数据、管理已保存的查询，以及构建仪表盘和可视化图表。 通过 Genie Agents 以自然语言探索数据。 借助 MLflow 和 Data Science Agent 进行 AI 与机器学习入门。

完成本课程后，您将能够熟练导航 Databricks、在 Unity Catalog 中查找和管理数据、在 AI 辅助下编写 SQL 和笔记本代码、构建仪表盘，以及使用 Genie 探索数据。

注意：对于 SCORM 课程文件，请在完成内容后关闭 SCORM 窗口。请勿点击“Next Lesson”按钮，否则 SCORM 模块可能无法标记为已完成。

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Connects apps running outside Databricks to Lakebase. Following the lecture-demo pattern, learners authenticate an external app with a service principal, compare the SDK-native, manual-HTTP, and Data API access paths, and build a production connection manager with pooling, token refresh, and retry on scale-to-zero wake-up.

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Declarative Automation Bundles (DABs), Lakeflow Jobs, and Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.

Note: 

1. This course is the second in the series of Advanced Machine Learning. 

2. For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.