Category: Amazon SageMaker Canvas

There has been a paradigm change in the mindshare of education customers who are now willing to explore new technologies and analytics. Universities and other higher learning institutions have collected massive amounts of data over the years, and now they are exploring options to use that data for deeper insights and better educational outcomes. You […]

In today’s highly competitive market, performing data analytics using machine learning (ML) models has become a necessity for organizations. It enables them to unlock the value of their data, identify trends, patterns, and predictions, and differentiate themselves from their competitors. For example, in the healthcare industry, ML-driven analytics can be used for diagnostic assistance and […]

The last few years have seen a tremendous paradigm shift in how institutional asset managers source and integrate multiple data sources into their investment process. With frequent shifts in risk correlations, unexpected sources of volatility, and increasing competition from passive strategies, asset managers are employing a broader set of third-party data sources to gain a […]

Business analysts work with data and like to analyze, explore, and understand data to achieve effective business outcomes. To address business problems, they often rely on machine learning (ML) practitioners such as data scientists to assist with techniques such as utilizing ML to build models using existing data and generate predictions. However, it isn’t always […]

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how […]

Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced […]

November 2022: This post was reviewed and updated with new functionality in Amazon SageMaker Canvas that supports tags to track and allocate costs incurred by users. The proliferation of machine learning (ML) across a wide range of use cases is becoming prevalent in every industry. However, this outpaces the increase in the number of ML […]

Every company, regardless of its size, wants to deliver the best products and services to its customers. To achieve this, companies want to understand industry trends and customer behavior, and optimize internal processes and data analyses on a routine basis. This is a crucial component of a company’s success. A very prominent part of the […]

Logistics and transportation companies track ETA (estimated time of arrival), which is a key metric for their business. Their downstream supply chain activities are planned based on this metric. However, delays often occur, and the ETA might differ from the product’s or shipment’s actual time of arrival (ATA), for instance due to shipping distance or […]

Predicting common machine failure types is critical in manufacturing industries. Given a set of characteristics of a product that is tied to a given type of failure, you can develop a model that can predict the failure type when you feed those attributes to a machine learning (ML) model. ML can help with insights, but […]