Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI).

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. with administrative privileges installed on AWS Terraform version 1.5.5 After the key is provisioned, it should be visible on the AWS KMS console.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. release, AWS customers can now do same things as they could with PyTorch 1.x 24xlarge with AWS PyTorch 2.0 on AWS PyTorch2.0

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

Reduced operational overhead – The EMR Serverless integration with AWS streamlines big data processing by managing the underlying infrastructure, freeing up your team’s time and resources. Apache Spark and its Python API, PySpark , empower users to process massive datasets effortlessly by using distributed computing across multiple nodes.

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AWS Machine Learning Blog

JULY 23, 2024

For audio logs, choose an S3 bucket to store the logs and assign an AWS Key Management Service (AWS KMS) key for added security. The following is a sample AWS Lambda function code in Python for referencing the slot value of a phone number provided by the user. Choose Manage conversation logs. Save the changes.

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AWS Machine Learning Blog

OCTOBER 17, 2024

The storage resources for SageMaker Studio spaces are Amazon Elastic Block Store (Amazon EBS) volumes, which offer low-latency access to user data like notebooks, sample data, or Python/Conda virtual environments. Amazon EFS provides a scalable fully managed elastic NFS file system for AWS compute instances.

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AWS Machine Learning Blog

APRIL 16, 2024

Jupyter notebooks can differentiate between SQL and Python code using the %%sm_sql magic command, which must be placed at the top of any cell that contains SQL code. This command signals to JupyterLab that the following instructions are SQL commands rather than Python code. or later image versions. or later image versions.

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AWS Machine Learning Blog

MAY 10, 2023

The SageMaker extension expects the JupyterLab environment to have valid AWS credentials and permissions to schedule notebook jobs. We discuss the steps for setting up credentials and AWS Identity and Access Management (IAM) permissions later in this post. See Installing or updating the latest version of the AWS CLI for instructions.

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AWS Machine Learning Blog

DECEMBER 4, 2024

To access SageMaker Studio on the AWS Management Console , you need to set up an Amazon SageMaker domain. You also need an AWS Identity and Access Management (IAM) role with appropriate permissions. About the Author Vivek Gangasani is a Senior GenAI Specialist Solutions Architect at AWS.

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AWS Machine Learning Blog

MAY 12, 2023

These AI-powered extensions help accelerate ML development by offering code suggestions as you type, and ensure that your code is secure and follows AWS best practices. Additionally, make sure you have appropriate access to both CodeWhisperer and CodeGuru using AWS Identity and Access Management (IAM).

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AWS Machine Learning Blog

APRIL 22, 2024

To solve this pain point, we can use Lightweight Directory Access Protocol (LDAP) and LDAP over TLS/SSL (LDAPS) to integrate with a directory service such as AWS Directory Service for Microsoft Active Directory. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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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

JULY 2, 2024

For production use, it is recommended to use a more robust frontend framework such as AWS Amplify , which provides a comprehensive set of tools and services for building scalable and secure web applications. The process is straightforward, thanks to the user-friendly interface and step-by-step guidance provided by the AWS Management Console.

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AWS Machine Learning Blog

AUGUST 22, 2023

It serializes these configuration dictionaries (or config dict for short) to their ProtoBuf representation, transports them to the client using gRPC, and then deserializes them back to Python dictionaries. For detailed steps to set up a similar scenario, refer to Delegate access across AWS accounts using IAM roles.

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AWS Machine Learning Blog

APRIL 5, 2023

We launch an Amazon SageMaker notebook, which provides a Python environment where you can run the code to pass an image to Amazon Rekognition and then automatically modify the image with the celebrity in focus. We create a SageMaker notebook as well as the AWS Identity and Access Management (IAM) role with the required permissions.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In terms of resulting speedups, the approximate order is programming hardware, then programming against PBA APIs, then programming in an unmanaged language such as C++, then a managed language such as Python. Examples of other PBAs now available include AWS Inferentia and AWS Trainium , Google TPU, and Graphcore IPU.

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AWS Machine Learning Blog

MAY 5, 2023

For example, searching for the terms “How to orchestrate ETL pipeline” returns results of architecture diagrams built with AWS Glue and AWS Step Functions. The solution applies Amazon Rekognition Custom Labels to detect AWS service logos on architecture diagrams to allow the architecture diagrams to be searchable with service names.

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The MLOps Blog

JANUARY 11, 2023

Learn more MLOps: What It Is, Why It Matters, and How to Implement It Designing the MLOps system on AWS It’s important to note that implementing MLOps practices can be challenging and may require significant investment in terms of time, resources, and expertise.

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AWS Machine Learning Blog

DECEMBER 14, 2023

You need to grant your users permissions for private spaces and user profiles necessary to access these private spaces. This means that if the file system is provisioned at a root or prefix level within the domain, these settings will automatically apply to the space created by the domain users.

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AWS Machine Learning Blog

JULY 10, 2024

You can now fine-tune Anthropic Claude 3 Haiku in Amazon Bedrock in a preview capacity in the US West (Oregon) AWS Region. Prerequisites To use this feature, make sure you have satisfied the following requirements: An active AWS account. To request access to the preview, contact your AWS account team or submit a support ticket.

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AWS Machine Learning Blog

DECEMBER 29, 2022

Now, teams that collect sensor data signals from machines in the factory can unlock the power of services like Amazon Timestream , Amazon Lookout for Equipment , and AWS IoT Core to easily spin up and test a fully production-ready system at the local edge to help avoid catastrophic downtime events. For Runtime , choose Python 3.9.

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AWS Machine Learning Blog

MARCH 30, 2023

Please use below python code to curate interactions dataset from the MovieLens public dataset. Choose the new aws-trending-now recipe. For Solution version ID , choose the solution version that uses the aws-trending-now recipe. For the interactions data, we use ratings history from the movies review dataset, MovieLens.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

We cover two approaches: using the Amazon SageMaker Studio UI for a no-code solution, and using the SageMaker Python SDK. Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. You can access the Meta Llama 3.2

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IBM Journey to AI blog

JANUARY 10, 2023

This data will be analyzed using Netezza SQL and Python code to determine if the flight delays for the first half of 2022 have increased over flight delays compared to earlier periods of time within the current data (January 2019 – December 2021). Any data from June 2003 up until the most recent month of data available can be selected.

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Mlearning.ai

JUNE 14, 2023

YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1) Deploy LLMs in production Deploy Model Azure —  Use endpoints for inference — Azure Machine Learning | Microsoft Learn AWS + Huggingface —  Exporting ?

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Data Science Blog

SEPTEMBER 3, 2024

Process Mining Tools, die als pure Process Mining Software gestartet sind Hierzu gehört Celonis, das drei-köpfige und sehr geschäftstüchtige Gründer-Team, das ich im Jahr 2012 persönlich kennenlernen durfte. in Databricks oder den KI-Tools von Google, AWS und Mircosoft Azure (Azure Cognitive Services, Azure Machine Learning etc.).

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Mlearning.ai

JUNE 18, 2023

This puts paupers, misers and cheapskates who do not have access to a dedicated deep learning rig or a paid cloud service such as AWS at a disadvantage. While Google Colab’s main focus is running Python code using Jupyter notebooks, with magic commands non-Python code can also be executed. We will use Python to help us do this.

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AWS Machine Learning Blog

APRIL 23, 2024

To use Local Mode, set instance_type='local' when running SageMaker Python SDK jobs such as training and inference. To enable Local Mode and Docker capabilities, you must set the EnableDockerAccess parameter to true for the domain’s DockerSettings using the AWS Command Line Interface (AWS CLI). Python 3.10 CPU Optimized.

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AWS Machine Learning Blog

SEPTEMBER 7, 2023

Amazon SageMaker Pipelines is a fully managed AWS service for building and orchestrating machine learning (ML) workflows. You can use SageMaker Pipelines to orchestrate ML jobs in SageMaker, and its integration with the larger AWS ecosystem also allows you to use resources like AWS Lambda functions, Amazon EMR jobs, and more.

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AWS Machine Learning Blog

JULY 18, 2024

The architecture consists of several AWS services seamlessly integrated with the Amazon Bedrock, enabling efficient and accurate extraction of data from scanned documents. This event invokes an AWS Lambda function, responsible for invoking the Anthropic Claude 3 Sonnet model on Amazon Bedrock.

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AWS Machine Learning Blog

OCTOBER 24, 2024

In this post, we will explore building a reusable RAG data pipeline on LangChain —an open source framework for building applications based on LLMs—and integrating it with AWS Glue and Amazon OpenSearch Serverless. You can choose a wide variety of data sources including databases, data warehouses, and SaaS applications supported in AWS Glue.

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AWS Machine Learning Blog

OCTOBER 11, 2024

You can set up the notebook in any AWS Region where Amazon Bedrock Knowledge Bases is available. You also need an AWS Identity and Access Management (IAM) role assigned to the SageMaker Studio domain. data # Assing local directory path to a python variable local_data_path = "./data/" This was created in Step-2 above.

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