AWS Machine Learning Blog
AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy
Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what […]
New technical deep dive course: Generative AI Foundations on AWS
Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Developed by AWS generative AI worldwide foundations lead Emily Webber, this free hands-on course and the supporting GitHub source code […]
AWS Reaffirms its Commitment to Responsible Generative AI
As a pioneer in artificial intelligence and machine learning, AWS is committed to developing and deploying generative AI responsibly As one of the most transformational innovations of our time, generative AI continues to capture the world’s imagination, and we remain as committed as ever to harnessing it responsibly. With a team of dedicated responsible AI […]
Use generative AI foundation models in VPC mode with no internet connectivity using Amazon SageMaker JumpStart
With recent advancements in generative AI, there are lot of discussions happening on how to use generative AI across different industries to solve specific business problems. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It is all backed by very large models […]
How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost
This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. You’re likely familiar with the autocomplete suggestion feature when you search for something on Google or Amazon. Although the search terms in these scenarios are pretty common keywords or expressions that we use in daily life, […]
Optimize AWS Inferentia utilization with FastAPI and PyTorch models on Amazon EC2 Inf1 & Inf2 instances
When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. For production workloads requiring high throughput and low latency, the selection of the Amazon Elastic Compute Cloud (EC2) instance, model serving stack, and deployment architecture is very important. Inefficient architecture can lead to […]
Analyze rodent infestation using Amazon SageMaker geospatial capabilities
Rodents such as rats and mice are associated with a number of health risks and are known to spread more than 35 diseases. Identifying regions of high rodent activity can help local authorities and pest control organizations plan for interventions effectively and exterminate the rodents. In this post, we show how to monitor and visualize […]
Enel automates large-scale power grid asset management and anomaly detection using Amazon SageMaker
This is a guest post by Mario Namtao Shianti Larcher, Head of Computer Vision at Enel. Enel, which started as Italy’s national entity for electricity, is today a multinational company present in 32 countries and the first private network operator in the world with 74 million users. It is also recognized as the first renewables […]
Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker
Artificial intelligence (AI) has become an important and popular topic in the technology community. As AI has evolved, we have seen different types of machine learning (ML) models emerge. One approach, known as ensemble modeling, has been rapidly gaining traction among data scientists and practitioners. In this post, we discuss what ensemble models are and […]
Use a generative AI foundation model for summarization and question answering using your own data
Large language models (LLMs) can be used to analyze complex documents and provide summaries and answers to questions. The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset. Once you have a solid LLM, you’ll want to expose that LLM to […]