Accountability, APIs, Best practices and Big data

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

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

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

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

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 Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

AWS Machine Learning

JULY 24, 2023

They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. In this section, we show how to build your own container, deploy your own GPT-2 model, and test with the SageMaker endpoint API. implement the model and the inference API. gpt2 and predictor.py

APIs

APIs Engineering Construction Benchmark

Localize content into multiple languages using AWS machine learning services

AWS Machine Learning

JULY 19, 2022

We set the OutputFormat to mp3 , which tells Amazon Polly to generate an audio stream for this API call. We again use the start_speech_synthesis_task method but specify OutputFormat to json , which tells Amazon Polly to generate speech marks for this API call. Prerequisites. AWS Cloud Development Kit (AWS CDK). Deploy the solution.

Engineering

Engineering APIs Education Best practices

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

AWS Machine Learning

AUGUST 2, 2023

We demonstrate CDE using simple examples and provide a step-by-step guide for you to experience CDE in an Amazon Kendra index in your own AWS account. After ingestion, images can be searched via the Amazon Kendra search console, API, or SDK. About the Authors Charalampos Grouzakis is a Data Scientist within AWS Professional Services.

Enterprise

Enterprise Healthcare APIs Big data

Designing generative AI workloads for resilience

AWS Machine Learning

FEBRUARY 1, 2024

Ingesting from these sources is different from the typical data sources like log data in an Amazon Simple Storage Service (Amazon S3) bucket or structured data from a relational database. In the low-latency case, you need to account for the time it takes to generate the embedding vectors.

Best practices

Best practices Engineering SaaS APIs

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. Software integrations assist on both accounts. Accountability framework. What Is a Digital Transformation Framework? Digital platform.

Best practices

Best practices Call Center Technology Analytics

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).

APIs

APIs Government Accountability Scripts

Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

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

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account).

Scripts

Scripts APIs Enterprise Accountability

Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

As you scale your models, projects, and teams, as a best practice we recommend that you adopt a multi-account strategy that provides project and team isolation for ML model development and deployment. Depending on your governance requirements, Data Science & Dev accounts can be merged into a single AWS account.

Government

Government Accountability Enterprise Engineering

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

This example notebook demonstrates the pattern of using Feature Store as a central repository from which data scientists can extract training datasets. In addition to creating a training dataset, we use the PutRecord API to put the 1-week feature aggregations into the online feature store nightly. Nov-01,22:01:00 1 74.99 …9843 99.50

Engineering

Engineering Analytics APIs Enterprise

How SaaS Software Can Help Sync Your Sales & Support Teams

aircall

APRIL 16, 2021

SaaS works well for a variety of general use cases, including: Data backup. Big data analytics. Flexibility – SaaS uses an open API (application programming interface) technology. Best Practices to Align Your Sales and Support Teams. Use SaaS software to ensure accountability for each support agent.

SaaS

SaaS Sales Coaching APIs

Define customized permissions in minutes with Amazon SageMaker Role Manager

AWS Machine Learning

DECEMBER 1, 2022

They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. An example IAM policy for an ML administrator may look like the following code. Delete the ML administrator role that you created.

Management

Management Government Enterprise APIs

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

AWS Machine Learning

SEPTEMBER 9, 2022

Access to AWS services from Katib and from pipeline pods using the AWS IAM Roles for Service Accounts (IRSA) integration with Kubeflow Profiles. 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.

Engineering

Engineering Healthcare APIs Scripts

Senior back-end software engineer

Stratifyd

JANUARY 3, 2019

The Senior Back-End Software Engineer will have demonstrated practical experience in building high-performance and reliable systems and possess a love for Git and all that version control provides (we can also accept a love/hate relationship with Git). Key accountabilities & responsibilities. and Python/C API.

Engineering

Engineering APIs Analytics Healthcare

Customer Contact Central

Use AWS PrivateLink to set up private access to Amazon Bedrock

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

Trending Sources

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

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

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

How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost

Localize content into multiple languages using AWS machine learning services

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

Designing generative AI workloads for resilience

10 Best Practices to Develop Your Digital Transformation Framework

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

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 Amazon SageMaker Model Cards sharing to improve model governance

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

How SaaS Software Can Help Sync Your Sales & Support Teams

Define customized permissions in minutes with Amazon SageMaker Role Manager

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

Senior back-end software engineer

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