APIs, Best practices, Big data and Enterprise

Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

AWS Machine Learning

JUNE 30, 2022

In this post, we will continue to build on top of the previous solution to demonstrate how to build a private API Gateway via Amazon API Gateway as a proxy interface to generate and access Amazon SageMaker presigned URLs. The user invokes createStudioPresignedUrl API on API Gateway along with a token in the header.

APIs

APIs Enterprise Big data Accountability

Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

OCTOBER 30, 2023

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. You can choose from various FMs from Amazon and leading AI startups such as AI21 Labs, Anthropic, Cohere, and Stability AI to find the model that’s best suited for your use case.

APIs

APIs Big data Accountability Enterprise

Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

AUGUST 2, 2023

An example of a customized image search is Enterprise Resource Planning (ERP). In ERP, image data collected from different stages of logistics or supply chain management could include tax receipts, vendor orders, payslips, and more, which need to be automatically categorized for the purview of different teams within the organization.

Enterprise

Enterprise Healthcare APIs Big data

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

Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

JANUARY 4, 2024

In this post, we provide an introduction to text to SQL (Text2SQL) and explore use cases, challenges, design patterns, and best practices. Today, a large amount of data is available in traditional data analytics, data warehousing, and databases, which may be not easy to query or understand for the majority of organization members.

Best practices

Best practices Enterprise Engineering APIs

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

AWS Machine Learning

OCTOBER 27, 2022

To achieve these operational benefits, they implemented a number of best practice processes, including a fast data iteration and testing cycle, and parallel testing to find optimal data combinations. We can then call a Forecast API to create a dataset group and import data from the processed S3 bucket.

APIs

APIs Best practices Metrics Government

Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

AWS Machine Learning

APRIL 24, 2023

In this post, we explore how AWS customer Pro360 used the Amazon Comprehend custom classification API , which enables you to easily build custom text classification models using your business-specific labels without requiring you to learn machine learning (ML), to improve customer experience and reduce operational costs.

APIs

APIs Customer Experience Feedback Chatbots

Identify rooftop solar panels from satellite imagery using Amazon Rekognition Custom Labels

AWS Machine Learning

JULY 19, 2022

To test the model output, we use a Jupyter notebook to run Python code to detect custom labels in a supplied image by calling Amazon Rekognition APIs. The solution workflow is as follows: Store satellite imagery data in Amazon S3 as the input source. Store satellite imagery data in Amazon S3 as an input source.

APIs

APIs Metrics Enterprise Government

The IoT Chronicles Part 2: Three Big Security Threats—and How to Solve Them

Avaya

NOVEMBER 16, 2016

employees at large enterprises bring their own device(s) to work, and 75% of companies currently permit or plan to permit BYOD in the workplace. It may be easy to secure enterprise IoT, but this gets a lot trickier when you factor in the devices employees are using on your network.

APIs

APIs Transportation Enterprise Healthcare

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

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

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

AWS Machine Learning

NOVEMBER 14, 2023

In the era of big data and AI, companies are continually seeking ways to use these technologies to gain a competitive edge. At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data. tar czvf model.tar.gz -C deepspeed.

Management

Management APIs Best practices Calibration

Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

Government

Government Accountability Enterprise Engineering

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. Refer to Operating model for best practices regarding a multi-account strategy for ML.

Scripts

Scripts APIs Enterprise Accountability

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning

APRIL 19, 2023

Solutions need high-throughput updates and low-latency retrieval of the most recent feature values in milliseconds, something most data scientists aren’t prepared to deliver. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management. Nov-01,22:05:05 4 200.00

Engineering

Engineering Analytics APIs Enterprise

Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

Government

Government Accountability Enterprise Engineering

Define customized permissions in minutes with Amazon SageMaker Role Manager

AWS Machine Learning

DECEMBER 1, 2022

The statement labeled RemoveErrorMessagesFromConsole can be removed without affecting the ability to get into Studio, but will result in API errors on the console UI. He is passionate about building governance products in Machine Learning for enterprise customers. Ozan Eken is a Senior Product Manager at Amazon Web Services.

Management

Management Government Enterprise APIs

Call Center Reporting - A New Paradigm

Xaqt

JUNE 22, 2020

Despite significant advancements in big data and open source tools, niche Contact Center Business Intelligence providers are still wed to their own proprietary tools leaving them saddled with technical debt and an inability to innovate from within. Accessing this data throughout the enterprise is often a challenge.

Call Center

Call Center Contact Center Analytics 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. To ensure we meet data compliance standards, we provision our AWS infrastructure in accordance with our athenahealth enterprise guidelines.

Engineering

Engineering Healthcare APIs Scripts

Customer Contact Central

Secure Amazon SageMaker Studio presigned URLs Part 2: Private API with JWT authentication

Use AWS PrivateLink to set up private access to Amazon Bedrock

Trending Sources

Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

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

Generating value from enterprise data: Best practices for Text2SQL and generative AI

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

Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

Identify rooftop solar panels from satellite imagery using Amazon Rekognition Custom Labels

The IoT Chronicles Part 2: Three Big Security Threats—and How to Solve Them

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

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

Model management for LoRA fine-tuned models using Llama2 and Amazon SageMaker

Use Amazon SageMaker Model Card sharing to improve model governance

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

Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

Use Amazon SageMaker Model Cards sharing to improve model governance

Define customized permissions in minutes with Amazon SageMaker Role Manager

Call Center Reporting - A New Paradigm

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

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