Category: Amazon SageMaker

The increasing size of language models has been one of the biggest trends in natural language processing (NLP) in recent years. Since 2018, we’ve seen unprecedented development and deployment of ever-larger language models, including BERT and its variants, GPT-2, T-NLG, and GPT-3 (175 billion parameters). These models have pushed the boundaries of possible architectural innovations. […]

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). SageMaker JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions […]

September 2023: This post was reviewed and updated for accuracy. 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, […]

Data scientists and machine learning (ML) engineers often prepare their data before building ML models. Data preparation typically includes data preprocessing and feature engineering. You preprocess data by transforming data into the right shape and quality for training, and you engineer features by selecting, transforming, and creating variables when building a predictive model. Amazon SageMaker […]

In this post, we talk about how to split a machine learning (ML) dataset into train, test, and validation datasets with Amazon SageMaker Data Wrangler so you can easily split your datasets with minimal to no code. Data used for ML is typically split into the following datasets: Training – Used to train an algorithm […]

This is a guest post co-written by Juan Francisco Fernandez, ML Engineer in Adevinta Spain, and AWS AI/ML Specialist Solutions Architects Antonio Rodriguez and João Moura. InfoJobs, a subsidiary company of the Adevinta group, provides the perfect match between candidates looking for their next job position and employers looking for the best hire for the […]

Amazon SageMaker comes with two options to spin up fully managed notebooks for exploring data and building machine learning (ML) models. The first option is fast start, collaborative notebooks accessible within Amazon SageMaker Studio – a fully integrated development environment (IDE) for machine learning. You can quickly launch notebooks in Studio, easily dial up or […]

Retail businesses are data-driven—they analyze data to get insights about consumer behavior, understand shopping trends, make product recommendations, optimize websites, plan for inventory, and forecast sales. A common approach for sales forecasting is to use historical sales data to predict future demand. Forecasting future demand is critical for planning and impacts inventory, logistics, and even […]

Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables […]

Amazon SageMaker Serverless Inference is an inference option that enables you to easily deploy machine learning (ML) models for inference without having to configure or manage the underlying infrastructure. SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic. In this post, you’ll see how to use SageMaker Serverless Inference to reduce cost when […]