2019, APIs and Scripts - Customer Contact Central

Transition your Amazon Forecast usage to Amazon SageMaker Canvas

AWS Machine Learning

JULY 29, 2024

Launched in August 2019, Forecast predates Amazon SageMaker Canvas , a popular low-code no-code AWS tool for building, customizing, and deploying ML models, including time series forecasting models. You can also take advantage of its data flow feature to connect with external data providers’ APIs to import data, such as weather information.

APIs

APIs Scripts Benchmark Metrics

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning

FEBRUARY 19, 2024

For text generation, Amazon Bedrock provides the RetrieveAndGenerate API to create embeddings of user queries, and retrieves relevant chunks from the vector database to generate accurate responses. Boto3 makes it straightforward to integrate a Python application, library, or script with AWS services.

Chatbots

Chatbots APIs Engineering Enterprise

Find answers accurately and quickly using Amazon Q Business with the SharePoint Online connector

AWS Machine Learning

JULY 25, 2024

SharePoint Server 2016, SharePoint Server 2019, and SharePoint Server Subscription Edition are the active SharePoint Server releases. Any additional mappings need to be set in the user store using the user store APIs. You need a Microsoft Windows instance to run PowerShell scripts and commands with PowerShell 7.4.1+.

Scripts

Scripts APIs Engineering Enterprise

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

AWS Machine Learning

OCTOBER 2, 2023

For more information about best practices, refer to the AWS re:Invent 2019 talk, Build accurate training datasets with Amazon SageMaker Ground Truth. For this we use AWS Step Functions , a serverless workflow service that provides us with API integrations to quickly orchestrate and visualize the steps in our workflow.

Scripts

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Develop and train large models cost-efficiently with Metaflow and AWS Trainium

AWS Machine Learning

APRIL 29, 2024

This often means the method of using a third-party LLM API won’t do for security, control, and scale reasons. It provides an approachable, robust Python API for the full infrastructure stack of ML/AI, from data and compute to workflows and observability. The following figure illustrates this workflow.

APIs

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Transaction Risk API – Why we built it (Blog 1 of 3)

Whitepages Pro

APRIL 4, 2019

Our latest product innovation, Transaction Risk API , was officially launched a couple of weeks ago at Merchant Risk Council (MRC) 2019. Introducing our Transaction Risk API. The Transaction Risk API was built by data scientists for data scientists and designed for easy integration into models. The new era or “Fraud 3.0”

APIs

APIs Scripts Marketing Customer Experience

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning

APRIL 19, 2023

Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Ideally, we instead want to load the model PyTorch scripts, extract the features from model input, and run model inference entirely in Java. The DJL was created at Amazon and open-sourced in 2019.

Engineering

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Incremental training with Amazon SageMaker JumpStart

AWS Machine Learning

JUNE 9, 2022

Recently, we also announced the launch of easy-to-use JumpStart APIs as an extension of the SageMaker Python SDK, allowing you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. JumpStart overview. The dataset has been downloaded from TensorFlow. Walkthrough overview.

Scripts

Scripts APIs Enterprise Accountability

Build a serverless meeting summarization backend with large language models on Amazon SageMaker JumpStart

AWS Machine Learning

MAY 17, 2023

Generative AI has grown at an accelerating rate from the largest pre-trained model in 2019 having 330 million parameters to more than 500 billion parameters today. The events trigger Lambda functions to make API calls to Amazon Transcribe and invoke the real-time endpoint hosting the Flan T5 XL model.

APIs

APIs Scripts Engineering Management

Automate classification of IT service requests with an Amazon Comprehend custom classifier

AWS Machine Learning

OCTOBER 11, 2022

It provides APIs powered by ML to extract key phrases, entities, sentiment analysis, and more. To install the active LTS version of Node.js, you can use the following install script for nvm and use nvm to install the Node.js If you’re running this solution in us-east-2, the format of this REST API is [link].execute-api.us-east-2.amazonaws.com/prod/invokecomprehendV1.

APIs

APIs Enterprise Engineering Accountability

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

AWS Machine Learning

NOVEMBER 25, 2022

When our tuning job is complete, we look at some of the methods available to explore the results, both via the AWS Management Console and programmatically via the AWS SDKs and APIs. We use the XGBoost algorithm, one of many algorithms provided as a SageMaker built-in algorithm (no training script required!). dataframe()[:10].

Scripts

Scripts APIs Metrics Construction

Create, train, and deploy a billion-parameter language model on terabytes of data with TensorFlow and Amazon SageMaker

AWS Machine Learning

JUNE 13, 2022

For a detailed guide to enable the TensorFlow training script for the SageMaker distributed model parallel library, refer to Modify a TensorFlow Training Script. For PyTorch, refer to Modify a PyTorch Training Script. Make sure that only device 0 can save checkpoints to prevent other workers from corrupting them. Conclusion.

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

AWS Machine Learning

SEPTEMBER 9, 2022

Example components of the standardized tooling include a data ingestion API, security scanning tools, the CI/CD pipeline built and maintained by another team within athenahealth, and a common serving platform built and maintained by the MLOps team. We’re currently on 1.4.1, evaluating 1.5. AWS is already working on the 1.6

Engineering

Engineering Healthcare APIs Scripts

Super-Agents Are Real (Blog #4)

Enghouse Interactive

OCTOBER 21, 2020

As noted in the 2019 Dimension Data Customer Experience (CX) Benchmarking report: 88% of contact center decision-makers expect self-service volumes to increase over the next 12 months. especially if it will increase service levels, reduce time wasted, and guarantees a positive outcome.

Self service

Self service Contact Center Benchmark Chatbots

Deep demand forecasting with Amazon SageMaker

AWS Machine Learning

JUNE 28, 2022

JumpStart features aren’t available in SageMaker notebook instances, and you can’t access them through SageMaker APIs or the AWS Command Line Interface (AWS CLI). Here, we create a SageMaker MXNet estimator and pass in our model training script, hyperparameters, as well as the number and type of training instances we want.

Metrics

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Run secure processing jobs using PySpark in Amazon SageMaker Pipelines

AWS Machine Learning

APRIL 11, 2023

SageMaker Processing jobs allow you to specify the private subnets and security groups in your VPC as well as enable network isolation and inter-container traffic encryption using the NetworkConfig.VpcConfig request parameter of the CreateProcessingJob API. Maren has been with AWS since November 2019.

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Customer Contact Central

Transition your Amazon Forecast usage to Amazon SageMaker Canvas

Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

Trending Sources

Find answers accurately and quickly using Amazon Q Business with the SharePoint Online connector

Build an end-to-end MLOps pipeline for visual quality inspection at the edge – Part 2

Develop and train large models cost-efficiently with Metaflow and AWS Trainium

Transaction Risk API – Why we built it (Blog 1 of 3)

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

Incremental training with Amazon SageMaker JumpStart

Build a serverless meeting summarization backend with large language models on Amazon SageMaker JumpStart

Automate classification of IT service requests with an Amazon Comprehend custom classifier

Optimize hyperparameters with Amazon SageMaker Automatic Model Tuning

Create, train, and deploy a billion-parameter language model on terabytes of data with TensorFlow and Amazon SageMaker

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

Super-Agents Are Real (Blog #4)

Deep demand forecasting with Amazon SageMaker

Run secure processing jobs using PySpark in Amazon SageMaker Pipelines

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