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. In response, SageMaker spins up training jobs with the requested number and type of compute instances. 24xlarge compute instance.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

In this post, we explore the journey that Thomson Reuters took to enable cutting-edge research in training domain-adapted large language models (LLMs) using Amazon SageMaker HyperPod , an Amazon Web Services (AWS) feature focused on providing purpose-built infrastructure for distributed training at scale. So, for example, a 6.6B

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AWS Machine Learning Blog

OCTOBER 16, 2024

Although setting up a processing cluster is an alternative, it introduces its own set of complexities, from data distribution to infrastructure management. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster. format("/".join(tile_prefix),

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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. Clusters are provisioned with the instance type and count of your choice and can be retained across workloads. As a result of this flexibility, you can adapt to various scenarios.

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AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. In this two-part series, we demonstrate how you can deploy a cloud-based FL framework on AWS. For Account ID , enter the AWS account ID of the owner of the accepter VPC.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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AWS Machine Learning Blog

JUNE 7, 2023

Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. in Computer Science from the University of Lille.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU. Suppliers of data center GPUs include NVIDIA, AMD, Intel, and others.

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AWS Machine Learning Blog

AUGUST 9, 2024

This post provides an overview of a custom solution developed by the AWS Generative AI Innovation Center (GenAIIC) for Deltek , a globally recognized standard for project-based businesses in both government contracting and professional services. For technical support or to contact AWS generative AI specialists, visit the GenAIIC webpage.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

With Ray and AIR, the same Python code can scale seamlessly from a laptop to a large cluster. The managed infrastructure of SageMaker and features like processing jobs, training jobs, and hyperparameter tuning jobs can use Ray libraries underneath for distributed computing. You can specify resource requirements in actors too.

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AWS Machine Learning Blog

AUGUST 22, 2023

Usually, if the dataset or model is too large to be trained on a single instance, distributed training allows for multiple instances within a cluster to be used and distribute either data or model partitions across those instances during the training process. Each account or Region has its own training instances.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Data Science Fundamentals Going beyond knowing machine learning as a core skill, knowing programming and computer science basics will show that you have a solid foundation in the field. Computer science, math, statistics, programming, and software development are all skills required in NLP projects.

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Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow. in these fields.

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AWS Machine Learning Blog

JANUARY 20, 2023

In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. Step-by-step solution Step 1 A client makes a request to the AWS API Gateway endpoint.

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Pickl AI

JULY 12, 2024

Natural Language Processing (NLP) This is a field of computer science that deals with the interaction between computers and human language. Computer Vision This is a field of computer science that deals with the extraction of information from images and videos.

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AWS Machine Learning Blog

OCTOBER 30, 2024

These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. About the Authors Kara Yang is a Data Scientist at AWS Professional Services in the San Francisco Bay Area, with extensive experience in AI/ML.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Pickl AI

JULY 25, 2023

These models may include regression, classification, clustering, and more. Cloud Platforms: AWS, Azure, Google Cloud, etc. in fields like Computer Science, Statistics, or related disciplines. ETL Tools: Apache NiFi, Talend, etc. Data Modeling: Entity-Relationship (ER) diagrams, data normalization, etc.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. She received her Ph.D.

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Mlearning.ai

APRIL 24, 2023

Learning about the framework of a service cloud platform is time consuming and frustrating because there is a lot of new information from many different computing fields (computer science/database, software engineering/developers, data science/scientific engineering & computing/research).

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O'Reilly Media

OCTOBER 19, 2021

Cloud-based data warehouses, such as Snowflake , AWS’ portfolio of databases like RDS, Redshift or Aurora , or an S3-based data lake , are a great match to ML use cases since they tend to be much more scalable than traditional databases, both in terms of the data set sizes as well as query patterns. Software Development Layers.

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Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. Below follows a transcript of their conversation, lightly edited for readability. Mayee Chen: Okay. Hi everyone.

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Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. Below follows a transcript of their conversation, lightly edited for readability. Mayee Chen: Okay. Hi everyone.

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PyImageSearch

SEPTEMBER 30, 2024

Deployment Server Tools: Kubernetes, Docker Swarm, AWS CodeDeploy Purpose: Automates the deployment of applications to staging or production environments. Orchestration Tools: Kubernetes, Docker Swarm Purpose: Manages the deployment, scaling, and operation of application containers across clusters of hosts. That’s not the case.

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AWS Machine Learning Blog

MAY 24, 2023

You can efficiently deploy the pre-trained J2-jumbo-instruct, or other Jurassic-2 models available on AWS Marketplace, into your own own virtual private cloud (VPC) using Amazon SageMaker. 24xlarge") # Create a Sgaemkaer endpoint then deploy a pre-trained J2-jumbo-instruct-v1 model from AWS Market Place.

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Pickl AI

SEPTEMBER 18, 2024

Most professionals in this field start with a bachelor’s degree in computer science, Data Science, mathematics, or a related discipline. Algorithm and Model Development Understanding various Machine Learning algorithms—such as regression , classification , clustering , and neural networks —is fundamental.

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Dataconomy

MARCH 13, 2023

Here are some of the essential tools and platforms that you need to consider: Cloud platforms Cloud platforms such as AWS , Google Cloud , and Microsoft Azure provide a range of services and tools that make it easier to develop, deploy, and manage AI applications.

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AWS Machine Learning Blog

APRIL 18, 2023

We serve developers and enterprises of all sizes through AWS, which offers a broad set of global compute, storage, database, and other service offerings. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

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AWS Machine Learning Blog

APRIL 18, 2023

We serve developers and enterprises of all sizes through AWS, which offers a broad set of global compute, storage, database, and other service offerings. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

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AWS Machine Learning Blog

JUNE 27, 2024

In this post, we describe how we built our cutting-edge productivity agent NinjaLLM, the backbone of MyNinja.ai, using AWS Trainium chips. For training, we chose to use a cluster of trn1.32xlarge instances to take advantage of Trainium chips. We used a cluster of 32 instances in order to efficiently parallelize the training.

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The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: Most people are really surprised to hear that my background in college was not computer science. For example, you can use BigQuery , AWS , or Azure. I would need to have the infrastructure to perform computations. It can be a cluster run by Kubernetes or maybe something else. How awful are they?”

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

Organizations that want to build their own models or want granular control are choosing Amazon Web Services (AWS) because we are helping customers use the cloud more efficiently and leverage more powerful, price-performant AWS capabilities such as petabyte-scale networking capability, hyperscale clustering, and the right tools to help you build.

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Flipboard

NOVEMBER 24, 2023

In this post, we show you how SnapLogic , an AWS customer, used Amazon Bedrock to power their SnapGPT product through automated creation of these complex DSL artifacts from human language. SnapLogic background SnapLogic is an AWS customer on a mission to bring enterprise automation to the world.

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AWS Machine Learning Blog

AUGUST 30, 2024

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. Training involved a dataset of over 15 trillion tokens across two GPU clusters, significantly more than Meta Llama 2.

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AWS Machine Learning Blog

OCTOBER 23, 2024

SnapLogic uses Amazon Bedrock to build its platform, capitalizing on the proximity to data already stored in Amazon Web Services (AWS). To address customers’ requirements about data privacy and sovereignty, SnapLogic deploys the data plane within the customer’s VPC on AWS.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Utilizing the latest Hugging Face LLM modules on Amazon SageMaker, AWS customers can now tap into the power of SageMaker deep learning containers (DLCs). About the authors Alfred Shen is a Senior AI/ML Specialist at AWS. Dr. Changsha Ma is an AI/ML Specialist at AWS.

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