AWS Machine Learning Blog

AUGUST 9, 2024

Question and answering (Q&A) using documents is a commonly used application in various use cases like customer support chatbots, legal research assistants, and healthcare advisors. In this collaboration, the AWS GenAIIC team created a RAG-based solution for Deltek to enable Q&A on single and multiple government solicitation documents.

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Mlearning.ai

JUNE 27, 2023

Hierarchical Clustering. Hierarchical Clustering: Since, we have already learnt “ K- Means” as a popular clustering algorithm. The other popular clustering algorithm is “Hierarchical clustering”. remember we have two types of “Hierarchical Clustering”. Divisive Hierarchical clustering. They are : 1.Agglomerative

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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Mlearning.ai

JULY 17, 2023

Clustering — Beyonds KMeans+PCA… Perhaps the most popular way of clustering is K-Means. It is also very common as well to combine K-Means with PCA for visualizing the clustering results, and many clustering applications follow that path (e.g. this link ).

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ODSC - Open Data Science

FEBRUARY 23, 2023

Tesla’s Automated Driving Documents Have Been Requested by The U.S. Architectures for Running ML at the Edge Tue, Feb 28, 2023, 12:00 PM — 1:00 PM EST In this webinar, we will explore different paradigms for edge deployment of ML models, including federated learning, cloud-edge hybrid architectures, and standalone edge models.

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The MLOps Blog

APRIL 5, 2023

In this comprehensive guide, we’ll explore the key concepts, challenges, and best practices for ML model packaging, including the different types of packaging formats, techniques, and frameworks. Documented : Good model packaging includes clear code documentation that helps others understand how to use and modify the model if required.

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AWS Machine Learning Blog

MAY 1, 2024

This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. Virginia) AWS Region. The following diagram illustrates the solution architecture.

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Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

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AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. kubectl for working with Kubernetes clusters.

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Towards AI

MAY 3, 2023

This code can cover a diverse array of tasks, such as creating a KMeans cluster, in which users input their data and ask ChatGPT to generate the relevant code. This is where ML CoPilot enters the scene. In this paper, the authors suggest the use of LLMs to make use of past ML experiences to suggest solutions for new ML tasks.

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Flipboard

NOVEMBER 17, 2023

The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models.

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AWS Machine Learning Blog

JULY 3, 2023

Companies across various industries create, scan, and store large volumes of PDF documents. There’s a need to find a scalable, reliable, and cost-effective solution to translate documents while retaining the original document formatting. It also uses the open-source Java library Apache PDFBox to create PDF documents.

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. It removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs).

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AWS Machine Learning Blog

JULY 17, 2023

With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface.

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Dataconomy

MAY 22, 2023

TensorFlow provides high-level APIs, such as tf.distribute, to distribute training across multiple devices, machines, or clusters. It is recommended to evaluate each framework’s documentation, performance benchmarks, and community support to determine the best fit for your distributed learning needs.

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AWS Machine Learning Blog

MAY 24, 2023

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. The system is capable of processing images, large PDF, and documents in other format and answering questions derived from the content via interactive text or voice inputs.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

In this post, you’ll see an example of performing drift detection on embedding vectors using a clustering technique with large language models (LLMS) deployed from Amazon SageMaker JumpStart. Then we use K-Means to identify a set of cluster centers. A visual representation of the silhouette score can be seen in the following figure.

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AWS Machine Learning Blog

OCTOBER 9, 2024

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. To learn more, see the documentation.

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AWS Machine Learning Blog

APRIL 19, 2024

The architecture deploys a simple service in a Kubernetes pod within an EKS cluster. Karpenter monitors for any pending pods that can’t run due to lack of sufficient resources in the cluster. If such pods are detected, Karpenter adds more nodes to the cluster to provide the necessary resources. A managed node group with two c5.xlarge

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AWS Machine Learning Blog

JUNE 11, 2024

When a Neuron SDK is released, you’ll now be notified of the support for Neuron DLAMIs and Neuron DLCs in the Neuron SDK release notes, with a link to the AWS documentation containing the DLAMI and DLC release notes. Starting with the AWS Neuron 2.18 In this post, we walk through some of the support highlights with Neuron 2.18.

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Smart Data Collective

NOVEMBER 1, 2020

Unsupervised ML: The Basics. Unlike supervised ML, we do not manage the unsupervised model. Unsupervised ML uses algorithms that draw conclusions on unlabeled datasets. As a result, unsupervised ML algorithms are more elaborate than supervised ones, since we have little to no information or the predicted outcomes.

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Snorkel AI

APRIL 27, 2023

The benefits of using Kubernetes for ML training Despite the challenges we faced building architecture around Kubernetes, there are three big reasons we used it: containerization, scalability, and infrastructure standardization. Spark, Dask, and any other workflow executors used for experimentation can grow along with the size of the cluster.

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Snorkel AI

APRIL 27, 2023

The benefits of using Kubernetes for ML training Despite the challenges we faced building architecture around Kubernetes, there are three big reasons we used it: containerization, scalability, and infrastructure standardization. Spark, Dask, and any other workflow executors used for experimentation can grow along with the size of the cluster.

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Snorkel AI

MAY 3, 2023

Table of contents Why we needed to redesign our interactive ML system In this section, we’ll go over the market forces and technological shifts that compelled us to re-architect our ML system. Customers tackle high cardinality and multi-label ML problems, requiring far more training data to cover rare classes.

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Data Science Dojo

MAY 10, 2024

Their impact on ML tasks has made them a cornerstone of AI advancements. It allows ML models to work with data but in a limited manner. Hence, while it is helpful to develop a basic understanding of a document, it is limited in forming a connection between words to grasp a deeper meaning.

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Pickl AI

JANUARY 23, 2023

It is not a good when dealing with RNN (Recurrent Neural Networks) Also See: 5 Machine Learning Algorithms That Every ML Engineer Should know Microsoft CNTK CNTK is a deep learning framework that was created by Microsoft Research. Theano Theano is one of the fastest and simplest ML libraries, and it was built on top of NumPy.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This significant improvement showcases how the fine-tuning process can equip these powerful multimodal AI systems with specialized skills for excelling at understanding and answering natural language questions about complex, document-based visual information. For a detailed walkthrough on fine-tuning the Meta Llama 3.2

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

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Towards AI

MAY 2, 2023

Use plain English to build ML models to identify profitable customer segments. Here is an example plot we will create by just asking in plain English to create 3 clusters (using kmeans) using income and spending variables, and present the breakdown of spending for each cluster without writing any code.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. How do I develop my body of work?

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

This domain knowledge is traditionally captured in reference manuals, service bulletins, quality ticketing systems, engineering drawings, and more, but the quantity and complexity of documents is growing and takes time to learn. How can I trace the reasoning of my model back to source documents to build user trust?” “How

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Data Science Dojo

MAY 10, 2024

Their impact on ML tasks has made them a cornerstone of AI advancements. It allows ML models to work with data but in a limited manner. Hence, while it is helpful to develop a basic understanding of a document, it is limited in forming a connection between words to grasp a deeper meaning.

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Mlearning.ai

MAY 19, 2023

For instance, a law firm can utilize MPT’s ability to handle long inputs to analyze lengthy legal contracts or complex legal documents without the need for tedious manual segmentation. Here is HuggingFace Link: [link] From the Mosaic ML paper. Here is the HuggingFace Link: [link] From the Mosaic ML paper.

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AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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AWS Machine Learning Blog

OCTOBER 11, 2024

Use cases for vector databases for RAG In the context of RAG architectures, the external knowledge can come from relational databases, search and document stores, or other data stores. Knowledge bases are essential for various use cases, such as customer support, product documentation, internal knowledge sharing, and decision-making systems.

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