You’ve just completed the DSS Project Walkthrough course. Here are a few of the main takeaways from this course:
- The AI Lifecycle provides a useful guide for building data science projects, and Dataiku DSS fulfills the needs of each stage of the cycle.
- Data Acquisition from a variety of data storage systems is satisfied by DSS connections.
- Data Exploration is supported with tables, charts, and statistics, typically computed on a sample of the full data for rapid interaction.
- Data Preparation is achieved with visual, code, and plugin recipes to transform your data and prepare it for machine learning.
- Experiment in DSS's visual machine learning tool to rapidly prototype models using various methods of feature handling, algorithms, and model assessments.
- Deploy models, either to the Flow to perform batch scoring or to an API node to expose it as a service for real-time scoring.
- Orchestrate your pipelines using metrics, checks, and scenarios to monitor performance and keep you up-to-date when new data arrives in production.
This is the final course in the DSS Overview course series. From here, you should look at the Core Concepts series, which is part of the Core Designer learning path. There you'll gain hands-on experience with some of the concepts you've been introduced to in this course.