Category: Amazon SageMaker Data Wrangler

Depending on the quality and complexity of data, data scientists spend between 45–80% of their time on data preparation tasks. This implies that data preparation and cleansing take valuable time away from real data science work. After a machine learning (ML) model is trained with prepared data and readied for deployment, data scientists must often […]

Amazon SageMaker Data Wrangler reduces the time that it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio, the first fully integrated development environment (IDE) for ML. With Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of […]

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment based on open-source JupyterLab for anyone to learn and experiment with ML using AWS ML compute resources. It’s based on the same architecture and user interface as Amazon SageMaker Studio, but with a subset of Studio capabilities. When you begin working on ML […]

Data preparation is the process of collecting, cleaning, and transforming raw data to make it suitable for insight extraction through machine learning (ML) and analytics. Data preparation is crucial for ML and analytics pipelines. Your model and insights will only be as reliable as the data you use for training them. Flawed data will produce […]

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including […]

Machine learning (ML) is disrupting a lot of industries at an unprecedented pace. The healthcare and life sciences (HCLS) industry has been going through a rapid evolution in recent years embracing ML across a multitude of use cases for delivering quality care and improving patient outcomes. In a typical ML lifecycle, data engineers and scientists […]

If you use the default lifecycle configuration for your domain or user profile in Amazon SageMaker Studio and use Amazon SageMaker Data Wrangler for data preparation, then this post is for you. In this post, we show how you can create a Data Wrangler flow and use it for data preparation in a Studio environment […]

Organizations moving towards a data-driven culture embrace the use of data and machine learning (ML) in decision-making. To make ML-based decisions from data, you need your data available, accessible, clean, and in the right format to train ML models. Organizations with a multi-account architecture want to avoid situations where they must extract data from one […]

Amazon SageMaker Data Wrangler is a purpose-built data aggregation and preparation tool for machine learning (ML). It allows you to use a visual interface to access data and perform exploratory data analysis (EDA) and feature engineering. The EDA feature comes with built-in data analysis capabilities for charts (such as scatter plot or histogram) and time-saving […]

Data fuels machine learning (ML); the quality of data has a direct impact on the quality of ML models. Therefore, improving data quality and employing the right feature engineering techniques are critical to creating accurate ML models. ML practitioners often tediously iterate on feature engineering, choice of algorithms, and other aspects of ML in search […]