AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

AWS 126

AWS Machine Learning Machine Learning ML 126

AWS Machine Learning Blog

MARCH 8, 2023

Manager Data Science at Marubeni Power International. This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. Therefore, the ingestion components need to be able to manage authentication, data sourcing in pull mode, data preprocessing, and data storage.

AWS 101

AWS Machine Learning Machine Learning Analytics 101

AWS Machine Learning Blog

OCTOBER 18, 2023

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. AWS CodeBuild is a fully managed continuous integration service in the cloud.

AWS 131

AWS ML ML Machine Learning 131

AWS Machine Learning Blog

APRIL 2, 2024

It does so by covering the end-to-end ML workflow: whether you’re looking for powerful data preparation and AutoML, managed endpoint deployment, simplified MLOps capabilities, or the ability to configure foundation models for generative AI , SageMaker Canvas can help you achieve your goals. Choose Enable with AWS Organizations.

AWS 128

AWS ML ML Machine Learning 128

AWS Machine Learning Blog

SEPTEMBER 27, 2024

Data preparation SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images. The model was then fine-tuned with training data from the data preparation stage. About the authors Scott Patterson is a Senior Solutions Architect at AWS.

AWS 124

AWS Data Lakes ML ML 124

AWS Machine Learning Blog

JUNE 22, 2023

Working with AWS, Light & Wonder recently developed an industry-first secure solution, Light & Wonder Connect (LnW Connect), to stream telemetry and machine health data from roughly half a million electronic gaming machines distributed across its casino customer base globally when LnW Connect reaches its full potential.

AWS 114

AWS ML ML Machine Learning 114

AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

Becoming Human

MAY 15, 2023

Data Science is a popular as well as vast field; till date, there are a lot of opportunities in this field, and most people, whether they are working professionals or students, everyone want a transition in data science because of its scope. How much to learn? What to do next?

Data Science 75

Data Science Machine Learning Machine Learning Database 75

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.

AWS 102

AWS ML ML ETL 102

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. For example, teams using these platforms missed an easy migration of their AI/ML prototypes to the industrialization of the solution running on AWS.

ML 153

ML ML AWS AI 153

AWS Machine Learning Blog

SEPTEMBER 12, 2023

Scalable Capital’s data science and client service teams identified that one of the largest bottlenecks in servicing our clients was responding to email inquiries. The following diagram shows the workflow for our email classifier project, but can also be generalized to other data science projects.

Data Science 132

Data Science Data Scientist AWS ML 132

AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care.

ML 129

ML ML AWS AI 129

AWS Machine Learning Blog

APRIL 26, 2023

Through ML EBA, experienced AWS ML subject matter experts work side by side with your cross-functional team to provide prescriptive guidance, remove blockers, and build organizational capability for a continued ML adoption. Additionally, AWS can offer financial incentives to help offset the costs for your first ML use case.

ML 120

ML ML AWS Machine Learning 120

AWS Machine Learning Blog

FEBRUARY 22, 2023

Boomi funded this solution using the AWS PE ML FastStart program, a customer enablement program meant to take ML-enabled solutions from idea to production in a matter of weeks. The data science team at Boomi applied the Markov Chain approach to the Step Suggest problem by treating integration steps as states in a state machine.

AWS 97

AWS ML ML Data Science 97

Towards AI

AUGUST 6, 2024

Build a Large Language Model (From Scratch) by Sebastian Raschka provides a comprehensive guide to constructing LLMs, from data preparation to fine-tuning. Generative AI on AWS by Chris Fregly and team demystifies generative AI integration into business, emphasizing model selection and deployment on AWS.

Natural Language Processing 119

Natural Language Processing AI AI AWS 119

AWS Machine Learning Blog

FEBRUARY 6, 2024

In this solution, we fine-tune a variety of models on Hugging Face that were pre-trained on medical data and use the BioBERT model, which was pre-trained on the Pubmed dataset and performs the best out of those tried. We implemented the solution using the AWS Cloud Development Kit (AWS CDK).

AWS 136

AWS ML ML Data Preparation 136

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.

AWS 122

AWS Clustering Big Data Big Data 122

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. CodeBuild supports a broad selection of git version control sources like AWS CodeCommit , GitHub, and GitLab.

AWS 127

AWS ML ML Python 127

Pickl AI

DECEMBER 4, 2024

Summary: The future of Data Science is shaped by emerging trends such as advanced AI and Machine Learning, augmented analytics, and automated processes. As industries increasingly rely on data-driven insights, ethical considerations regarding data privacy and bias mitigation will become paramount.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 22, 2023

Building a production-ready solution in AWS involves a series of trade-offs between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS.

AWS 119

AWS ML ML Machine Learning 119

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. Across 180 countries, millions of developers and hundreds of thousands of businesses use Twilio to create magical experiences for their customers.

ML 126

ML ML AWS Machine Learning 126

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.

SQL 130

SQL AWS ML ML 130

Flipboard

MARCH 22, 2023

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics.

AWS 123

AWS Data Preparation Azure Data Scientist 123

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.

AWS 127

AWS ML ML Data Preparation 127

AWS Machine Learning Blog

AUGUST 22, 2023

We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. Solution Overview Imagine you, as an administrator, have to manage data for multiple data science teams running their own data preparation workflows in SageMaker Data Wrangler. Create an S3 access point.

AWS 96

AWS Data Science Data Preparation Artificial Intelligence 96

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.

ML 135

ML ML AWS Python 135

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. Before you get started with the solution, create an AWS account. This identity is called the AWS account root user. For more detailed steps to prepare the data, refer to the GitHub repo.

AWS 138

AWS Machine Learning Machine Learning AI 138

AWS Machine Learning Blog

SEPTEMBER 9, 2024

However, companies can face challenges when using generative AI for data insights, including maintaining data quality, addressing privacy concerns, managing model biases, and integrating AI systems with existing workflows. Domo is a cloud-centered data experiences innovator that empowers users to make data-driven decisions.

AI 126

AI AI AWS ML 126

AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. Lauren Mullennex is a Senior AI/ML Specialist Solutions Architect at AWS. In his spare time, he loves traveling and writing.

ML 127

ML ML Machine Learning Machine Learning 127

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.

ML 134

ML ML Azure AWS 134

ODSC - Open Data Science

AUGUST 9, 2023

Be sure to check out his talk, “ Build Classification and Regression Models with Spark on AWS ,” there! In the unceasingly dynamic arena of data science, discerning and applying the right instruments can significantly shape the outcomes of your machine learning initiatives. A cordial greeting to all data science enthusiasts!

Machine Learning 52

Machine Learning Machine Learning AWS Big Data 52

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.

ML 123

ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

APRIL 16, 2024

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. An execution role update may be required to bring in data browsing and the SQL run feature. You need to create AWS Glue connections with specific connection types.

SQL 130

SQL AWS Database Data Scientist 130

AWS Machine Learning Blog

MAY 23, 2023

We finish with a case study highlighting the benefits realize by a large AWS and PwC customer who implemented this solution. Solution overview AWS offers a comprehensive portfolio of cloud-native services for developing and running MLOps pipelines in a scalable and sustainable manner. The following diagram illustrates the workflow.

Machine Learning 102

Machine Learning Machine Learning AWS ML 102

Towards AI

AUGUST 16, 2023

Therefore, a common mistake when interviewing applicants is to focus on the minutia of a particular platform (AWS, GCP, Databricks, MLflow, etc.). Every data science team develops its own approach for each ML library that is used, so the link between the model and the code and parameters is often lost. References [1] J.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

AWS Machine Learning Blog

MAY 30, 2023

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support offering. In Part 1 , we showed how to get started using AWS Cost Explorer to identify cost optimization opportunities in SageMaker. You can build custom queries to look up AWS CUR data using standard SQL.

AWS 98

AWS ML ML EDA 98

Flipboard

MARCH 7, 2023

Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. You can manage app images via the SageMaker console, the AWS SDK for Python (Boto3), and the AWS Command Line Interface (AWS CLI). Environments without internet access.

Python 123

Python AWS ML ML 123