AWS Machine Learning Blog

DECEMBER 1, 2023

This is where the AWS suite of low-code and no-code ML services becomes an essential tool. As a strategic systems integrator with deep ML experience, Deloitte utilizes the no-code and low-code ML tools from AWS to efficiently build and deploy ML models for Deloitte’s clients and for internal assets.

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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. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

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AWS Machine Learning Blog

AUGUST 26, 2024

AWS makes it possible for organizations of all sizes and developers of all skill levels to build and scale generative AI applications with security, privacy, and responsible AI. In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos.

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AWS Machine Learning Blog

MARCH 1, 2023

In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

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AWS Machine Learning Blog

MARCH 8, 2023

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account.

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Flipboard

DECEMBER 11, 2024

SageMaker Unied Studio is an integrated development environment (IDE) for data, analytics, and AI. Discover your data and put it to work using familiar AWS tools to complete end-to-end development workflows, including data analysis, data processing, model training, generative AI app building, and more, in a single governed environment.

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Flipboard

NOVEMBER 21, 2023

In recent years, MathWorks has brought many product offerings into the cloud, especially on Amazon Web Services (AWS). Here is a quick guide on how to run MATLAB on AWS. Installation of AWS Command-Line Interface (AWS CLI) , AWS Configure , and Python3. Set up AWS Configure to interact with AWS resources.

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AWS Machine Learning Blog

JULY 11, 2024

It supports all stages of ML development—from data preparation to deployment, and allows you to launch a preconfigured JupyterLab IDE for efficient coding within seconds. Specifically, we demonstrate how you can customize SageMaker Distribution for geospatial workflows by extending it with open-source geospatial Python libraries.

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AWS Machine Learning Blog

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps you build, deploy, and manage edge device software.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Additionally, using Amazon Comprehend with AWS PrivateLink means that customer data never leaves the AWS network and is continuously secured with the same data access and privacy controls as the rest of your applications. For more details, refer to Integrating SageMaker Data Wrangler with SageMaker Pipelines.

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AWS Machine Learning Blog

AUGUST 21, 2024

In the following sections, we provide a detailed, step-by-step guide on implementing these new capabilities, covering everything from data preparation to job submission and output analysis. This use case serves to illustrate the broader potential of the feature for handling diverse data processing tasks.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

SEPTEMBER 3, 2024

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. This same interface is also used for provisioning EMR clusters.

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AWS Machine Learning Blog

APRIL 26, 2024

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for fully managed Retrieval Augmented Generation (RAG).

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AWS Machine Learning Blog

APRIL 17, 2023

In other words, companies need to move from a model-centric approach to a data-centric approach.” – Andrew Ng A data-centric AI approach involves building AI systems with quality data involving data preparation and feature engineering. Custom transforms can be written as separate steps within Data Wrangler.

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Flipboard

NOVEMBER 24, 2023

In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges. It is powered by Amazon SageMaker Studio and provides JupyterLab for Python and Posit Workbench for R.

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AWS Machine Learning Blog

DECEMBER 13, 2023

The built-in project templates provided by Amazon SageMaker include integration with some of third-party tools, such as Jenkins for orchestration and GitHub for source control, and several utilize AWS native CI/CD tools such as AWS CodeCommit , AWS CodePipeline , and AWS CodeBuild. all implemented via CloudFormation.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Data preparation is important at multiple stages in Retrieval Augmented Generation ( RAG ) models. Create a dataflow Complete the following steps to create a data flow in SageMaker Canvas: On the SageMaker Canvas home page, choose Data preparation. This will land on a data flow page. Choose your domain.

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AWS Machine Learning Blog

MARCH 28, 2024

For more information on Mixtral-8x7B Instruct on AWS, refer to Mixtral-8x7B is now available in Amazon SageMaker JumpStart. LangChain is an open source Python library designed to build applications with LLMs. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user.

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AWS Machine Learning Blog

AUGUST 1, 2024

To address this challenge, AWS recently announced the preview of Amazon Bedrock Custom Model Import , a feature that you can use to import customized models created in other environments—such as Amazon SageMaker , Amazon Elastic Compute Cloud (Amazon EC2) instances, and on premises—into Amazon Bedrock.

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AWS Machine Learning Blog

JUNE 17, 2024

Being one of the largest AWS customers, Twilio engages with data and artificial intelligence and machine learning (AI/ML) services to run their daily workloads. The training data used for this pipeline is made available through PrestoDB and read into Pandas through the PrestoDB Python client.

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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. Amazon SageMaker Pipelines allows orchestrating the end-to-end ML lifecycle from data preparation and training to model deployment as automated workflows. The full code can be found on the aws-samples-for-ray GitHub repository.

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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

JULY 11, 2024

Fine tuning embedding models using SageMaker SageMaker is a fully managed machine learning service that simplifies the entire machine learning workflow, from data preparation and model training to deployment and monitoring. Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account set up.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

In first part of this multi-series blog post, you will learn how to create a scalable training pipeline and prepare training data for Comprehend Custom Classification models. We will introduce a custom classifier training pipeline that can be deployed in your AWS account with few clicks.

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AWS Machine Learning Blog

MARCH 6, 2023

In the past few years, numerous customers have been using the AWS Cloud for LLM training. We recommend working with your AWS account team or contacting AWS Sales to determine the appropriate Region for your LLM workload. Data preparation LLM developers train their models on large datasets of naturally occurring text.

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Dataconomy

NOVEMBER 11, 2024

Data preparation for LLM fine-tuning Proper data preparation is key to achieving high-quality results when fine-tuning LLMs for specific purposes. Importance of quality data in fine-tuning Data quality is paramount in the fine-tuning process.

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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. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

This required custom integration efforts, along with complex AWS Identity and Access Management (IAM) policy management, further complicating the model governance process. Several activities are performed in this phase, such as creating the model, data preparation, model training, evaluation, and model registration.

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AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. Python’s simplicity, versatility, and extensive library support make it the go-to language for AI development.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Given this mission, Talent.com and AWS joined forces to create a job recommendation engine using state-of-the-art natural language processing (NLP) and deep learning model training techniques with Amazon SageMaker to provide an unrivaled experience for job seekers. The recommendation system has driven an 8.6%

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AWS Machine Learning Blog

JULY 24, 2024

This is where MLflow can help streamline the ML lifecycle, from data preparation to model deployment. You can create workflows with SageMaker Pipelines that enable you to prepare data, fine-tune models, and evaluate model performance with simple Python code for each step.

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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

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

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AWS Machine Learning Blog

MAY 25, 2023

This allows you to create unique views and filters, and grants management teams access to a streamlined, one-click dashboard without needing to log in to the AWS Management Console and search for the appropriate dashboard. On the AWS CloudFormation console, create a new stack. amazonaws.com docker build -t. docker tag :latest.dkr.ecr.us-east-1.amazonaws.com/

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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. The raw training data never leaves the account that owns the data and only the derived weights are sent across the peered connection.

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