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

How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning

MAY 3, 2023

This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process. One aspect of this data preparation is feature engineering. However, generalizing feature engineering is challenging.

Engineering

Engineering Metrics APIs 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. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs

APIs Big data Accountability Enterprise

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

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

How AI can help you deliver an 11-star customer experience

Hello Customer

JUNE 21, 2023

But what organizations can do is reverse engineer that 11-star experience. Representation of the data flow within Hello Customer For data to be valuable it’s crucial that you can manage, store and use data in an efficient way. That’s where the concept of data mesh comes into play.

Customer Experience

Customer Experience Engineering Feedback Finance

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. Conclusion. About the authors.

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

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. As always, AWS welcomes feedback. So, get started today!

APIs

APIs Scripts Engineering Technical Support

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

AWS Machine Learning

NOVEMBER 29, 2023

Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio. With this launch, you can programmatically run notebooks as jobs using APIs provided by Amazon SageMaker Pipelines , the ML workflow orchestration feature of Amazon SageMaker.

APIs

APIs Management Engineering Big data

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

OCTOBER 4, 2022

In several domains, ML has proven capable of solving problems previously unsolvable with classical big data and analytical techniques, and the demand for data science and ML practitioners is increasing steadily. We create the following steps: Processing step (feature engineering). Model training and tuning.

Scripts

Scripts Engineering Management APIs

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

AWS Machine Learning

NOVEMBER 17, 2023

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. As always, AWS welcomes feedback.

APIs

APIs Enterprise Big data Chatbots

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

Improve governance of your machine learning models with Amazon SageMaker

AWS Machine Learning

DECEMBER 1, 2022

Model cards provide model risk managers, data scientists, and ML engineers the ability to perform the following tasks: Document model requirements such as risk rating, intended usage, limitations, and expected performance. The SageMaker Python SDK allows data scientists and ML engineers to easily interact with SageMaker components.

Government

Government Metrics APIs Engineering

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. By putting it into the public domain, we invite your feedback and contributions.

Call Center

Call Center Contact Center Analytics Engineering

Use Amazon SageMaker Model Card sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. We encourage you try out the new model card sharing feature and let us know your feedback. About the authors Vishal Naik is a Sr.

Government

Government Accountability Enterprise 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. Consequently, maintaining and augmenting older projects required more engineering time and effort. About the authors.

Engineering

Engineering Healthcare APIs Scripts

Team and user management with Amazon SageMaker and AWS SSO

AWS Machine Learning

JULY 29, 2022

The solution also uses SAML attribute mapping to populate the SAML assertion with specific access-relevant data, such as user ID and user team. Because the solution creates a SAML API, you can use any IdP supporting SAML assertions to create this architecture. The API Gateway calls an SAML backend API. Custom SAML 2.0

Management

Management APIs Accountability Construction

Use Amazon SageMaker Model Cards sharing to improve model governance

AWS Machine Learning

AUGUST 31, 2023

In addition to data engineers and data scientists, there have been inclusions of operational processes to automate & streamline the ML lifecycle. We encourage you try out the new model card sharing feature and let us know your feedback. About the authors Vishal Naik is a Sr.

Government

Government Accountability Enterprise Engineering

Customer Contact Central

How Vericast optimized feature engineering using Amazon SageMaker Processing

Use AWS PrivateLink to set up private access to Amazon Bedrock

Trending Sources

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

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

How AI can help you deliver an 11-star customer experience

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

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

Schedule Amazon SageMaker notebook jobs and manage multi-step notebook workflows using APIs

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

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

Improve governance of your machine learning models with Amazon SageMaker

Call Center Reporting - A New Paradigm

Use Amazon SageMaker Model Card sharing to improve model governance

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

Team and user management with Amazon SageMaker and AWS SSO

Use Amazon SageMaker Model Cards sharing to improve model governance

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