What is Data Preparation?
What is the connection between ML and data preparation?
Why is data preparation important for ML?
Data fuels ML. Harnessing this data to reinvent your business, while challenging, is imperative to staying relevant now and in the future. It is survival of the most informed, and those who can put their data to work to make better, more informed decisions respond faster to the unexpected and uncover new opportunities. This important yet tedious process is a prerequisite for building accurate ML models and analytics, and it is the most time-consuming part of an ML project. To minimize this time investment, data scientists can use tools that help automate data preparation in various ways.
Data preparation follows a series of steps that starts with collecting the right data, followed by cleaning, labeling, and then validation and visualization.
Validate and visualize
How can AWS help?
Amazon SageMaker data preparation tools help organizations gain insights from both structured and unstructured data. For instance, you can use Amazon SageMaker Data Wrangler to simplify structured data preparation with built-in data visualizations through a no-code visual interface. SageMaker Data Wrangler includes over 300 built-in data transformations, so you can quickly normalize, transform, and combine features without writing any code. You can also bring your custom transformations in Python or Apache Spark, if you prefer. For unstructured data, you need large high-quality, labeled datasets. Using Amazon SageMaker Ground Truth Plus, you can build high-quality ML training datasets while reducing data labeling costs by up to 40% without having to build labeling applications or manage a labeling workforce on your own.
For analysts or business users who prefer preparing data inside a notebook, you can visually browse, discover, and connect to Spark data processing environments running on Amazon EMR from your Amazon SageMaker Studio notebooks with a few clicks. Once connected, you can interactively query, explore, and visualize data, and run Spark jobs using the language of your choice (SQL, Python, or Scala) to build complete data preparation and ML workflows.