Does your company's data scientist or ML engineer spend the most time cleaning and preparing data?
We see the amount of data and models growing rapidly – doubling every year. The data preparation pipeline involves complex data preparation procedures that, as a result, require substantial time to operationalize in a production environment.
The customer needs a solution that helps them to automate data prep, model building, and model deployment into an end-to-end workflow so that the customer spends less time aggregating and preparing data for machine learning (ML) from weeks to minutes.
Data Preparation challenges?
- Data preparation is time consuming and require multiple tools & tasks.
- Simple tasks require a lot of code.
- Deployment can require a code rewrite, and productionizing can take months.
The fastest and easiest way to prepare data for machine learning
At 1CloudHub, technical experts collaborate closely with AWS as an AWS Advanced Consulting Partner to develop new solutions that can assist customers constantly enhance the efficiency of their ML operations.
AWS offers Amazon SageMaker Data Wrangler which can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface.
