Keeping an up-to-date asset inventory with real devices deployed in the field can be a challenging and time-consuming task. Many electricity providers use manufacturer’s labels as key information to link their physical assets within asset inventory systems. Computer vision can be a viable solution to speed up operator inspections andContinue Reading
Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 million H100 GPU hours. On 256Continue Reading
In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. Generative AI solutions can play an invaluable role during the model development phase by simplifying training and test dataContinue Reading
GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. Although GraphStorm can run efficiently on single instances for small graphs, it truly shines when scaling to enterprise-level graphs in distributedContinue Reading
RAW HTML pdfplumber, pypdf, and pdfminer to help with the extraction of text and tabular data from the PDF. The following is an example of using pdfplumber to parse the first page of the 2023 Amazon annual report in PDF format. import pdfplumber pdf_file = “Amazon-com-Inc-2023-Annual-Report.pdf” with pdfplumber.open(pdf_file) as pdf:Continue Reading
Today, organizations are constantly seeking ways to use advanced large language models (LLMs) for their specific needs. These organizations are engaging in both pre-training and fine-tuning massive LLMs, with parameter counts in the billions. This process aims to enhance model efficacy for a wide array of applications across diverse sectors,Continue Reading
Large language models (LLMs) are AI systems trained on vast amounts of text data, enabling them to understand, generate, and reason with natural language in highly capable and flexible ways. LLM training has seen remarkable advances in recent years, with organizations pushing the boundaries of what’s possible in terms ofContinue Reading
To stay competitive, businesses across industries use foundation models (FMs) to transform their applications. Although FMs offer impressive out-of-the-box capabilities, achieving a true competitive edge often requires deep model customization through pre-training or fine-tuning. However, these approaches demand advanced AI expertise, high performance compute, fast storage access and can beContinue Reading
Video generation has become the latest frontier in AI research, following the success of text-to-image models. Luma AI’s recently launched Dream Machine represents a significant advancement in this field. This text-to-video API generates high-quality, realistic videos quickly from text and images. Trained on the Amazon SageMaker HyperPod, Dream Machine excelsContinue Reading
In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, andContinue Reading
