APIs, Big data, Enterprise and Scripts - Customer Contact Central

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

FEBRUARY 29, 2024

Knowledge Bases for Amazon Bedrock supports multiple vector databases, including Amazon OpenSearch Serverless , Amazon Aurora , Pinecone, and Redis Enterprise Cloud. For enterprise implementations, Knowledge Bases supports AWS Key Management Service (AWS KMS) encryption, AWS CloudTrail integration, and more.

APIs

APIs Healthcare Scripts Enterprise

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

DECEMBER 21, 2022

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

Scripts

Scripts Engineering APIs Big data

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

MARCH 23, 2023

Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture. To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.

Scripts

Scripts APIs Government Analytics

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

FEBRUARY 13, 2024

This new capability promotes collaboration and minimizes duplicate work for teams involved in ML model and application development, particularly in enterprise environments with multiple accounts spanning different business units or functions. You need to provide your consumer AWS account ID before running the notebook.

Accountability

Accountability Government Engineering Scripts

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

AWS Machine Learning

APRIL 11, 2023

Enterprise customers have multiple lines of businesses (LOBs) and groups and teams within them. These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner.

APIs

APIs Accountability Scripts Enterprise

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

JANUARY 26, 2024

Reviews can be performed using tools like the AWS Well-Architected Tool , or with the help of your AWS team through AWS Enterprise Support. Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies.

Best practices

Best practices Government Engineering Accountability

Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

MAY 8, 2023

This can be a challenge for enterprises in regulated industries that need to keep strong model governance for audit purposes. How to expose the MLflow server via private integrations to an API Gateway, and implement a secure access control for programmatic access via the SDK and browser access via the MLflow UI. Adds an IAM authorizer.

APIs

APIs Government Accountability Scripts

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

MARCH 22, 2023

Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. For data scientist: An S3 bucket that Data Wrangler can use to output transformed data. An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles.

APIs

APIs Engineering Analytics Scripts

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning

AUGUST 29, 2023

Solution overview The following figure illustrates the proposed target MLOps architecture for enterprise batch inference for organizations who use GitLab CI/CD and Terraform infrastructure as code (IaC) in conjunction with AWS tools and services. The data scientist can review and approve the new version of the model independently.

Scripts

Scripts APIs Enterprise Accountability

Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

AWS Machine Learning

NOVEMBER 7, 2022

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. If you want to jump straight into the JumpStart API code we go through in this post, you can refer to the following sample Jupyter notebook: Introduction to JumpStart – Text to Image.

APIs

APIs Scripts Enterprise Big data

Run image segmentation with Amazon SageMaker JumpStart

AWS Machine Learning

AUGUST 26, 2022

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. The following sections provide a step-by-step demo to perform semantic segmentation with JumpStart, both via the Studio UI and via JumpStart APIs. Semantic segmentation.

APIs

APIs Scripts Enterprise Big data

Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

AWS Machine Learning

NOVEMBER 7, 2022

We explore two ways of obtaining the same result: via JumpStart’s graphical interface on Amazon SageMaker Studio , and programmatically through JumpStart APIs. The following sections provide a step-by-step demo to perform inference, both via the Studio UI and via JumpStart APIs. JumpStart overview. Text generation, GPT-2, and Bloom.

APIs

APIs Scripts Enterprise Big data

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

MARCH 7, 2023

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. The main benefit is that a data scientist can choose which script to run to customize the container with new packages.

Management

Management Scripts Government Engineering

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

AWS Machine Learning

JULY 21, 2022

As a result, this experimentation phase can produce multiple models, each created from their own inputs (datasets, training scripts, and hyperparameters) and producing their own outputs (model artifacts and evaluation metrics). He works with enterprise customers and partners deploying AI solutions in the cloud.

Metrics

Metrics Scripts Enterprise Telecommunications

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

SEPTEMBER 9, 2022

Prior to our adoption of Kubeflow on AWS, our data scientists used a standardized set of tools and a process that allowed flexibility in the technology and workflow used to train a given model. Each project maintained detailed documentation that outlined how each script was used to build the final model.

Engineering

Engineering Healthcare APIs Scripts

Customer Contact Central

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Trending Sources

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

Securing MLflow in AWS: Fine-grained access control with AWS native services

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

Run image segmentation with Amazon SageMaker JumpStart

Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

Stay Connected

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Trending Sources

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Secure Amazon SageMaker Studio presigned URLs Part 3: Multi-account private API access to Studio

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

Securing MLflow in AWS: Fine-grained access control with AWS native services

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart

Run image segmentation with Amazon SageMaker JumpStart

Run text generation with GPT and Bloom models on Amazon SageMaker JumpStart

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

Stay Connected

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction