Category: Amazon SageMaker

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? You’re not alone. Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. This post describes how to implement your first ML use case using Amazon […]

We recently introduced a new capability in the Amazon SageMaker Python SDK that lets data scientists run their machine learning (ML) code authored in their preferred integrated developer environment (IDE) and notebooks along with the associated runtime dependencies as Amazon SageMaker training jobs with minimal code changes to the experimentation done locally. Data scientists typically […]

In the world of machine learning (ML), the quality of the dataset is of significant importance to model predictability. Although more data is usually better, large datasets with a high number of features can sometimes lead to non-optimal model performance due to the curse of dimensionality. Analysts can spend a significant amount of time transforming […]

Batch inference is a common pattern where prediction requests are batched together on input, a job runs to process those requests against a trained model, and the output includes batch prediction responses that can then be consumed by other applications or business functions. Running batch use cases in production environments requires a repeatable process for […]

Each machine learning (ML) system has a unique service level agreement (SLA) requirement with respect to latency, throughput, and cost metrics. With advancements in hardware design, a wide range of CPU- and GPU-based infrastructures are available to help you speed up inference performance. Also, you can build these ML systems with a combination of ML […]

August 30, 2023: Amazon Kinesis Data Analytics has been renamed to Amazon Managed Service for Apache Flink. Read the announcement in the AWS News Blog and learn more. Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products […]

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts of data, LLMs have the potential to revolutionize the way we […]

This post was co-written with Dave Gowel, CEO of RallyPoint. In his own words, “RallyPoint is an online social and professional network for veterans, service members, family members, caregivers, and other civilian supporters of the US armed forces. With two million members on the platform, the company provides a comfortable place for this deserving population […]

Sparked by the release of large AI models like AlexaTM, GPT, OpenChatKit, BLOOM, GPT-J, GPT-NeoX, FLAN-T5, OPT, Stable Diffusion, and ControlNet, the popularity of generative AI has seen a recent boom. Businesses are beginning to evaluate new cutting-edge applications of the technology in text, image, audio, and video generation that have the potential to revolutionize […]

“Instead of focusing on the code, companies should focus on developing systematic engineering practices for improving data in ways that are reliable, efficient, and systematic. In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving […]