APIs, Best practices and Government - Customer Contact Central

Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

AWS Machine Learning

NOVEMBER 14, 2024

This post outlines steps you can take to implement a comprehensive tagging governance strategy across accounts, using AWS tools and services that provide visibility and control. Tagging is an effective scaling mechanism for implementing cloud management and governance strategies.

Government

Government Best practices Accountability APIs

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

NOVEMBER 14, 2024

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. This streamlines the ML workflows, enables better visibility and governance, and accelerates the adoption of ML models across the organization.

Government

Government Management APIs Accountability

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

AWS Machine Learning

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

Government

Government Engineering Metrics APIs

Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

AWS Machine Learning

JULY 31, 2024

The new ApplyGuardrail API enables you to assess any text using your preconfigured guardrails in Amazon Bedrock, without invoking the FMs. In this post, we demonstrate how to use the ApplyGuardrail API with long-context inputs and streaming outputs. For example, you can now use the API with models hosted on Amazon SageMaker.

APIs

APIs Banking Best practices Engineering

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

AWS Machine Learning

OCTOBER 2, 2024

This two-part series explores best practices for building generative AI applications using Amazon Bedrock Agents. This data provides a benchmark for expected agent behavior, including the interaction with existing APIs, knowledge bases, and guardrails connected with the agent.

Best practices

Best practices APIs Metrics Accountability

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

OCTOBER 2, 2024

However, scaling up generative AI and making adoption easier for different lines of businesses (LOBs) comes with challenges around making sure data privacy and security, legal, compliance, and operational complexities are governed on an organizational level. In this post, we discuss how to address these challenges holistically.

Government

Government APIs Enterprise Best practices

Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.

Government

Government Accountability Enterprise Best practices

Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

NOVEMBER 7, 2024

We also dive deeper into access patterns, governance, responsible AI, observability, and common solution designs like Retrieval Augmented Generation. It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic.

Enterprise

Enterprise APIs Government Accountability

Best practices to build generative AI applications on AWS

AWS Machine Learning

MARCH 14, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API. This improves efficiency and allows larger contexts to be used. This supports safer adoption.

Best practices

Best practices Engineering Chatbots Enterprise

Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

NOVEMBER 15, 2024

For now, we consider eight key dimensions of responsible AI: Fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. This tool not only supports responsible AI practices, but also fosters trust and reliability in the use of AI-generated content.

APIs

APIs Government Best practices Metrics

Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

AWS Machine Learning

FEBRUARY 9, 2024

IT teams are responsible for helping the LOB innovate with speed and agility while providing centralized governance and observability. A software as a service (SaaS) layer for foundation models can provide a simple and consistent interface for end-users, while maintaining centralized governance of access and consumption.

SaaS

SaaS APIs Government Accountability

Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

AWS Machine Learning

NOVEMBER 19, 2024

Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon Web Services available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.

APIs

APIs Management Engineering Enterprise

Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

The framework that gives systematic visibility into ML model development, validation, and usage is called ML governance. During AWS re:Invent 2022, AWS introduced new ML governance tools for Amazon SageMaker which simplifies access control and enhances transparency over your ML projects.

Government

Government Accountability Enterprise Best practices

Create a Generative AI Gateway to allow secure and compliant consumption of foundation models

AWS Machine Learning

SEPTEMBER 28, 2023

Regulated and compliance-oriented industries, such as financial services, healthcare and life sciences, and government institutes, face unique challenges in ensuring the secure and responsible consumption of these models. In addition, API Registries enabled centralized governance, control, and discoverability of APIs.

APIs

APIs Government Enterprise Engineering

A progress update on our commitment to safe, responsible generative AI

AWS Machine Learning

JULY 10, 2024

AI Service Cards are a form of responsible AI documentation that provide customers with a single place to find information on the intended use cases and limitations, responsible AI design choices, and deployment and performance optimization best practices for our AI services and models.

Government

Government Education Best practices Benchmark

How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker

AWS Machine Learning

OCTOBER 3, 2024

In this post, we describe how Aviva built a fully serverless MLOps platform based on the AWS Enterprise MLOps Framework and Amazon SageMaker to integrate DevOps best practices into the ML lifecycle. We illustrate the entire setup of the MLOps platform using a real-world use case that Aviva has adopted as its first ML use case.

APIs

APIs Construction Engineering Accountability

Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

NOVEMBER 19, 2024

Plus, learn how to evolve from data aggregation to data semantics to support data-driven applications while maintaining flexibility and governance. In this session, learn best practices for effectively adopting generative AI in your organization. This session covers best practices for a responsible evaluation.

APIs

APIs Enterprise Best practices Government

10 Best Practices to Develop Your Digital Transformation Framework

aircall

MAY 24, 2021

By following best practices for your digital transformation framework, you also get the benefit of flexibility so you can add and subtract digital tools as your company’s needs change. Organization: structure, governance, roles, etc. 10 Best Practices to Develop a Framework for Digital Transformation.

Best practices

Best practices Call Center Analytics Technology

Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

AWS Machine Learning

MAY 31, 2024

Amazon Bedrock is a fully managed service that makes foundational models (FMs) from leading artificial intelligence (AI) companies and Amazon available through an API, so you can choose from a wide range of FMs to find the model that’s best suited for your use case. Who does GDPR apply to?

Best practices

Best practices APIs Accountability Personalization

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

AWS Machine Learning

APRIL 18, 2024

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

Scripts

Scripts Best practices Engineering Accountability

Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

AWS Machine Learning

JANUARY 10, 2024

The solution uses AWS Lambda , Amazon API Gateway , Amazon EventBridge , and SageMaker to automate the workflow with human approval intervention in the middle. The approver approves the model by following the link in the email to an API Gateway endpoint. API Gateway invokes a Lambda function to initiate model updates.

APIs

APIs Construction Engineering Analytics

Improve public speaking skills using a generative AI-based virtual assistant with Amazon Bedrock

AWS Machine Learning

OCTOBER 15, 2024

Organizations across various sectors, including corporations, educational institutions, government entities, and social media personalities, can use this solution to provide automated coaching for their employees, students, and public speaking engagements. The system prompt instructs the Anthropic Claude 3.5 Connect with Nehal on LinkedIn.

APIs

APIs Construction Engineering Accountability

How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning

MAY 7, 2024

With a decade of enterprise AI experience, Veritone supports the public sector, working with US federal government agencies, state and local government, law enforcement agencies, and legal organizations to automate and simplify evidence management, redaction, person-of-interest tracking, and eDiscovery.

APIs

APIs Advertising Entertainment Metrics

Use Amazon SageMaker Studio with a custom file system in Amazon EFS

AWS Machine Learning

OCTOBER 17, 2024

Improved data governance and security – The shared EFS directory, being centrally managed by the AWS infrastructure admin, can provide improved data governance and security. You should have an AWS CloudTrail log file that logs the SageMaker API CreateUserProfile.

Government

Government Accountability APIs Engineering

Design secure generative AI application workflows with Amazon Verified Permissions and Amazon Bedrock Agents

AWS Machine Learning

OCTOBER 14, 2024

Agents automatically call the necessary APIs to interact with the company systems and processes to fulfill the request. The App calls the Claims API Gateway API to run the claims proxy passing user requests and tokens. Claims API Gateway runs the Custom Authorizer to validate the access token.

APIs

APIs Construction Government Big data

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

AWS Machine Learning

MAY 8, 2023

It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. This can be a challenge for enterprises in regulated industries that need to keep strong model governance for audit purposes. Now let’s dive deeper into the details. Adds an IAM authorizer.

APIs

APIs Government Accountability Scripts

The executive’s guide to generative AI for sustainability

AWS Machine Learning

APRIL 22, 2024

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. Throughout this lifecycle, implementing AWS Well-Architected Framework best practices is recommended. A Gartner, Inc. Figure 7: The generative AI lifecycle 11.

Best practices

Best practices Benchmark Transportation Engineering

Simplify access to internal information using Retrieval Augmented Generation and LangChain Agents

AWS Machine Learning

SEPTEMBER 14, 2023

Integrating security in our workflow Following the best practices of the Security Pillar of the Well-Architected Framework , Amazon Cognito is used for authentication. Amazon API Gateway hosts a REST API with various endpoints to handle user requests that are authenticated using Amazon Cognito. 2xlarge 676.8

APIs

APIs Healthcare Scripts Engineering

Introducing AWS AI Service Cards: A new resource to enhance transparency and advance responsible AI

AWS Machine Learning

NOVEMBER 30, 2022

Governance – Processes to define, implement and enforce responsible AI practices within an organization. They are part of a comprehensive development process we undertake to build our services in a responsible way that addresses fairness and bias, explainability, robustness, governance, transparency, privacy, and security.

Government

Government Best practices APIs Technology

Enable data sharing through federated learning: A policy approach for chief digital officers

AWS Machine Learning

MARCH 15, 2024

Furthermore, model hosting on Amazon SageMaker JumpStart can help by exposing the endpoint API without sharing model weights. FL can have a potential impact on the entire treatment cycle, and now even more so with the focus on data interoperability from large federal organizations and government leaders.

Healthcare

Healthcare Government Best practices Engineering

Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning

OCTOBER 11, 2024

Amazon Bedrock makes it straightforward to adopt any of these choices by providing a common set of APIs, industry-leading embedding models, security, governance, and observability. This information can be valuable for data governance, auditing, and compliance purposes. client('sts').get_caller_identity().get('Account')

APIs

APIs Enterprise Engineering Government

Introducing self-service quota management and higher default service quotas for Amazon Textract

AWS Machine Learning

SEPTEMBER 27, 2022

Amazon Textract now has higher service quotas for several asynchronous and synchronous APIs in multiple major AWS Regions. The following table summarizes the before and after default quota numbers for each of these Regions for the respective synchronous and asynchronous APIs. Increased default service quotas for Amazon Textract.

Self service

Self service APIs Management Accountability

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

AWS Machine Learning

NOVEMBER 16, 2023

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production. Regulations in the healthcare industry call for especially rigorous data governance.

Healthcare

Healthcare Government Engineering APIs

Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

AWS Machine Learning

SEPTEMBER 29, 2022

Without proper cost management and governance, your ML spend may lead to surprises in your monthly AWS bill. In this post, we share tips and best practices regarding cost allocation for your SageMaker environment and workloads. To tag existing domains and users, use the add-tags API.

Enterprise

Enterprise APIs Government Accountability

Build and train ML models using a data mesh architecture on AWS: Part 1

AWS Machine Learning

JULY 29, 2022

This is mainly targeting the data steward persona, who is responsible for streamlining and standardizing the process of sharing data between data producers and consumers and ensuring compliance with data governance rules. Allow the consumer banking LoB to share data products into the central governance layer.

Government

Government Banking Analytics Accountability

Reduce food waste to improve sustainability and financial results in retail with Amazon Forecast

AWS Machine Learning

OCTOBER 27, 2022

With environmental, social, and governance (ESG) initiatives becoming more important for companies, our customer, one of Greater China region’s top convenience store chains, has been seeking a solution to reduce food waste (currently over $3.5 We use the AutoPredictor API, which is also accessible through the Forecast console.

APIs

APIs Best practices Metrics Government

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

AWS Machine Learning

NOVEMBER 22, 2023

By using the Framework, you will learn operational and architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable workloads in the cloud. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS.

Best practices

Best practices Metrics Feedback Enterprise

What Is CPQ Cloud?

Cincom

MAY 8, 2024

Strict access controls govern who can access data in the CPQ system. By using Cincom Open APIs, customers can extend and integrate other systems as needed. This consolidates relevant systems and information while guiding sellers to standardize best practices. What are some key benefits of CPQ cloud solutions?

APIs

APIs Enterprise SaaS CRM

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

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning

JUNE 27, 2023

Machine Learning Operations (MLOps) provides the technical solution to this issue, assisting organizations in managing, monitoring, deploying, and governing their models on a centralized platform. At-scale, real-time image recognition is a complex technical problem that also requires the implementation of MLOps.

Engineering

Engineering APIs Scripts Entertainment

Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning

NOVEMBER 22, 2023

Hear best practices for using unstructured (video, image, PDF), semi-structured (Parquet), and table-formatted (Iceberg) data for training, fine-tuning, checkpointing, and prompt engineering. In this talk, explore strategies for putting your proprietary datasets to work when building unique, differentiated generative AI solutions.

Engineering

Engineering Best practices APIs Government

Innovate or Stagnate: Top 4 Customer Service Trends for 2024

Comm100

DECEMBER 12, 2023

On top of this, Comm100 Omnichannel offers out-of-the-box integrations to your core systems, combined with a highly flexible API. Read this blog to discover more proactive chat ideas and best practices. This reduces escalation and increases support capacity. Omnichannel communication isn’t the future – it’s the present.

Customer Service

Customer Service Chatbots APIs Government

Integrate QnABot on AWS with ServiceNow

AWS Machine Learning

FEBRUARY 6, 2024

The workflow includes the following steps: A QnABot administrator can configure the questions using the Content Designer UI delivered by Amazon API Gateway and Amazon Simple Storage Service (Amazon S3). Amazon Lex V2 getting started- Streaming APIs]([link] Expand the Advanced section and enter the same answer under Markdown Answer.

Chatbots

Chatbots APIs Self service Banking

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning

SEPTEMBER 18, 2023

Trained models can be stored, versioned, and tracked in Amazon SageMaker Model Registry for governance and management. It has APIs for common ML data preprocessing operations like parallel transformations, shuffling, grouping, and aggregations. He works with government sponsored entities, helping them build AI/ML solutions using AWS.

Scripts

Scripts APIs Government Construction

Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Trending Sources

Improve governance of models with Amazon SageMaker unified Model Cards and Model Registry

Use the ApplyGuardrail API with long-context inputs and streaming outputs in Amazon Bedrock

Best practices for building robust generative AI applications with Amazon Bedrock Agents – Part 1

Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

Use Amazon SageMaker Model Card sharing to improve model governance

Build a multi-tenant generative AI environment for your enterprise on AWS

Best practices to build generative AI applications on AWS

Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

Build an internal SaaS service with cost and usage tracking for foundation models on Amazon Bedrock

Automate emails for task management using Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails

Use Amazon SageMaker Model Cards sharing to improve model governance

Create a Generative AI Gateway to allow secure and compliant consumption of foundation models

A progress update on our commitment to safe, responsible generative AI

How Aviva built a scalable, secure, and reliable MLOps platform using Amazon SageMaker

Your guide to generative AI and ML at AWS re:Invent 2024

10 Best Practices to Develop Your Digital Transformation Framework

Implementing Knowledge Bases for Amazon Bedrock in support of GDPR (right to be forgotten) requests

Generate customized, compliant application IaC scripts for AWS Landing Zone using Amazon Bedrock

Build an Amazon SageMaker Model Registry approval and promotion workflow with human intervention

Improve public speaking skills using a generative AI-based virtual assistant with Amazon Bedrock

How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

Use Amazon SageMaker Studio with a custom file system in Amazon EFS

Design secure generative AI application workflows with Amazon Verified Permissions and Amazon Bedrock Agents

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

The executive’s guide to generative AI for sustainability

Simplify access to internal information using Retrieval Augmented Generation and LangChain Agents

Introducing AWS AI Service Cards: A new resource to enhance transparency and advance responsible AI

Enable data sharing through federated learning: A policy approach for chief digital officers

Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

Introducing self-service quota management and higher default service quotas for Amazon Textract

Philips accelerates development of AI-enabled healthcare solutions with an MLOps platform built on Amazon SageMaker

Set up enterprise-level cost allocation for ML environments and workloads using resource tagging in Amazon SageMaker

Build and train ML models using a data mesh architecture on AWS: Part 1

Reduce food waste to improve sustainability and financial results in retail with Amazon Forecast

Build well-architected IDP solutions with a custom lens – Part 1: Operational excellence

What Is CPQ Cloud?

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

How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

Your guide to generative AI and ML at AWS re:Invent 2023

Innovate or Stagnate: Top 4 Customer Service Trends for 2024

Integrate QnABot on AWS with ServiceNow

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

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