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

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

AWS Machine Learning

DECEMBER 21, 2022

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation.

Scripts

Scripts Engineering APIs Big data

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

FEBRUARY 29, 2024

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs

APIs Healthcare Scripts Enterprise

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

AWS Machine Learning

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

Accountability

Accountability Government Engineering Scripts

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

OCTOBER 4, 2022

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. From a very high level, the ML lifecycle consists of many different parts, but the building of an ML model usually consists of the following general steps: Data cleansing and preparation (feature engineering).

Scripts

Scripts Engineering Management APIs

Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

AWS Machine Learning

AUGUST 3, 2023

Finally, we show how you can integrate this car pose detection solution into your existing web application using services like Amazon API Gateway and AWS Amplify. For each option, we host an AWS Lambda function behind an API Gateway that is exposed to our mock application. iterdir(): if p_file.suffix == ".pth":

APIs

APIs Scripts Engineering Technical Support

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

SEPTEMBER 9, 2022

Amp wanted a scalable data and analytics platform to enable easy access to data and perform machine leaning (ML) experiments for live audio transcription, content moderation, feature engineering, and a personal show recommendation service, and to inspect or measure business KPIs and metrics. Data Engineer for Amp on Amazon.

Analytics

Analytics Engineering APIs Metrics

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

AWS Machine Learning

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

Best practices

Best practices Government Engineering Accountability

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

AWS Machine Learning

APRIL 11, 2023

In the post Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication , we demonstrated how to build a private API to generate Amazon SageMaker Studio presigned URLs that are only accessible by an authenticated end-user within the corporate network from a single account.

APIs

APIs Accountability Scripts Enterprise

Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

AWS Machine Learning

JULY 29, 2022

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy machine learning (ML) models quickly. The SageMaker Python SDK provides open-source APIs and containers to train and deploy models on SageMaker, using several different ML and deep learning frameworks.

Scripts

Scripts APIs Big data Engineering

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

AWS Machine Learning

MARCH 22, 2023

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. Configure Snowflake. Configure SageMaker Studio.

APIs

APIs Engineering Analytics Scripts

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

AWS Machine Learning

MAY 8, 2023

In this post, we address these limitations by implementing the access control outside of the MLflow server and offloading authentication and authorization tasks to Amazon API Gateway , where we implement fine-grained access control mechanisms at the resource level using Identity and Access Management (IAM). Adds an IAM authorizer.

APIs

APIs Government Accountability 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

When a new version of the model is registered in the model registry, it triggers a notification to the responsible data scientist via Amazon SNS. When the review process is complete, the data scientist can proceed and approve the new version of the model in the model registry.

Scripts

Scripts APIs Enterprise Accountability

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

DECEMBER 18, 2023

The SageMaker Canvas UI lets you seamlessly integrate data sources from the cloud or on-premises, merge datasets effortlessly, train precise models, and make predictions with emerging data—all without coding. Solution overview Users persist their transactional time series data in MongoDB Atlas.

Finance

Finance APIs Analytics Scripts

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

AWS Machine Learning

JULY 25, 2022

For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing.

Scripts

Scripts Engineering Analytics APIs

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

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

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 has experience in the telecommunications and software engineering. About the authors.

Metrics

Metrics Scripts Enterprise Telecommunications

Customer Contact Central

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

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Trending Sources

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

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

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

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

Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

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

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

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

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

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

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

Stay Connected

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

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Trending Sources

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

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Build and train computer vision models to detect car positions in images using Amazon SageMaker and Amazon Rekognition

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

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

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

Fine-tune and deploy a summarizer model using the Hugging Face Amazon SageMaker containers bringing your own script

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

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

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

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

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

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

Stay Connected

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