APIs, Big data and Engineering - 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 Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

AUGUST 29, 2022

You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. To further monitor those workflows, data scientists now require cross-account read-only permission to the deployed pipeline in the test account.

Accountability

Accountability APIs Management Engineering

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

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

DECEMBER 21, 2022

As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation.

Scripts

Scripts Engineering APIs Big data

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

Accountability

Accountability Government Engineering Scripts

Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning

NOVEMBER 27, 2023

Let’s invoke the Amazon OpenSearch API to search relevant documents for the generated vector embeddings. The same feature engineering code is often run again and again, wasting time and compute resources on repeating the same operations. To avoid the reprocessing of features, we’ll export our data flow to an Amazon SageMaker pipeline.

Engineering

Engineering APIs Big data Accountability

How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

SEPTEMBER 9, 2022

Our initial ML model uses 21 batch features computed daily using data captured in the past 2 months. This data includes both playback and app engagement history per user, and grows with the number of users and frequency of app usage. Manolya McCormick is a Sr Software Development Engineer for Amp on Amazon. Real-time inference.

Personalization

Personalization APIs Engineering Metrics

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

AWS Machine Learning

JULY 24, 2023

This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. They use big data (such as a history of past search queries) to provide many powerful yet easy-to-use patent tools. implement the model and the inference API. gpt2 and predictor.py

APIs

APIs Engineering Construction Benchmark

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. Data Engineer for Amp on Amazon.

Analytics

Analytics Engineering APIs Metrics

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

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. Aamna Najmi is a Data Scientist with AWS Professional Services.

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

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

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. Anil Kodali is a Solutions Architect with Amazon Web Services.

Engineering

Engineering APIs Education Best practices

Use a generative AI foundation model for summarization and question answering using your own data

AWS Machine Learning

JULY 19, 2023

When that job is done, you can invoke an API that summarizes the text or answers questions about it. Randy has held a variety of positions in the technology space, ranging from software engineering to product management. In entered the Big Data space in 2013 and continues to explore that area.

Construction

Construction APIs Big data Engineering

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

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

AWS Machine Learning

AUGUST 2, 2023

After ingestion, images can be searched via the Amazon Kendra search console, API, or SDK. The image repository is then indexed by Amazon Kendra, which is a search engine that can be used to search for structured and unstructured data. About the Authors Charalampos Grouzakis is a Data Scientist within AWS Professional Services.

Enterprise

Enterprise Healthcare APIs Big data

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

AWS Machine Learning

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. The Lambda function can be called from an application or Amazon API Gateway.

Metrics

Metrics Analytics APIs Big data

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

BI with an IQ - Revisited

Xaqt

FEBRUARY 6, 2020

Back then, Artificial Intelligence, APIs, Robotic Process Automation (RPA), and even "Big Data" weren't things yet. The RPA engine no longer needs to "ask" these types of questions because the context server already knows the state of each connected application and has visibility into all the data-points].

Contact Center

Contact Center Enterprise Call Center APIs

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

AWS Machine Learning

JANUARY 4, 2024

Specifically, we discuss the following: Why do we need Text2SQL Key components for Text to SQL Prompt engineering considerations for natural language or Text to SQL Optimizations and best practices Architecture patterns Why do we need Text2SQL? Effective prompt engineering is key to developing natural language to SQL systems.

Best practices

Best practices Enterprise Engineering APIs

Designing generative AI workloads for resilience

AWS Machine Learning

FEBRUARY 1, 2024

There are unique considerations when engineering generative AI workloads through a resilience lens. Make sure to validate prompt input data and prompt input size for allocated character limits that are defined by your model. If you’re performing prompt engineering, you should persist your prompts to a reliable data store.

Best practices

Best practices Engineering SaaS APIs

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

MARCH 22, 2023

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. Configure Snowflake. Configure SageMaker Studio.

APIs

APIs Engineering Analytics Scripts

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). Adds an IAM authorizer.

APIs

APIs Government Accountability Scripts

CREATING CREDIT UNION MEMBERS FOR LIFE

Enghouse Interactive

JULY 10, 2018

Enghouse Interactive’s technology appealed to Partner Colorado for several reasons: streamlined integration with Avaya IP Office; omni-channel functionality; agent-centric design; as well as the breadth and depth of the company’s engineering and support teams.

Self service

Self service CRM Contact Center Analytics

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 a Data Engineer he was involved in applying AI/ML to fraud detection and office automation.

APIs

APIs Enterprise Big data Engineering

Senior back-end software engineer

Stratifyd

JANUARY 3, 2019

We are seeking an analytical, results-driven individual with a passion for developing applications and improving existing ones who knows how to get things done and is not afraid to take on big challenges. As a Senior Back-End Software Engineer with Stratifyd, Inc. Maintain and develop Stratifyd’s API layer and/or analytics pipeline.

Engineering

Engineering APIs Analytics Healthcare

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.

Management

Management APIs Best practices Big data

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

DECEMBER 18, 2023

The SageMaker Canvas UI lets you seamlessly integrate data sources from the cloud or on-premises, merge datasets effortlessly, train precise models, and make predictions with emerging data—all without coding. Solution overview Users persist their transactional time series data in MongoDB Atlas.

Finance

Finance APIs Analytics Scripts

What’s All This Fuss About Composability?

ConvergeOne

DECEMBER 7, 2021

The underlying technologies of composability include some combination of artificial intelligence (AI), machine learning, automation, container-based architecture, big data, analytics, low-code and no-code development, Agile/DevOps deployment, cloud delivery, and applications with open APIs (microservices).

APIs

APIs Big data Analytics 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 post walks through a complete example of how you can couple streaming feature engineering with Feature Store to make ML-backed decisions in near-real time. Apache Flink is a popular framework and engine for processing data streams. cc_num trans_time amount fraud_label …1248 Nov-01 14:50:01 10.15

Analytics

Analytics Engineering APIs Enterprise

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

AWS Machine Learning

AUGUST 29, 2023

When a new version of the model is registered in the model registry, it triggers a notification to the responsible data scientist via Amazon SNS. When the review process is complete, the data scientist can proceed and approve the new version of the model in the model registry.

Scripts

Scripts APIs Enterprise Accountability

MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

JUNE 24, 2022

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally.

Enterprise

Enterprise Engineering Accountability APIs

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 APIs Metrics Engineering

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

AWS Machine Learning

JULY 25, 2022

For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing. Packaging the solution as a scalable workflow.

Scripts

Scripts Engineering Analytics APIs

Intelligently search Adobe Experience Manager content using Amazon Kendra

AWS Machine Learning

SEPTEMBER 6, 2023

Perform intelligent search with Amazon Kendra Before you try searching on the Amazon Kendra console or using the API, make sure that the data source sync is complete. To check, view the data sources and verify if the last sync was successful. She is passionate about designing big data workloads cloud-natively.

Management

Management Enterprise Analytics Finance

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.

Engineering

Engineering Healthcare APIs Scripts

Demystifying machine learning at the edge through real use cases

AWS Machine Learning

JUNE 15, 2022

Edge is a term that refers to a location, far from the cloud or a big data center, where you have a computer device (edge device) capable of running (edge) applications. How do I eliminate the need of installing a big framework like TensorFlow or PyTorch on my restricted device? Edge computing. or Where is tool B?

Transportation

Transportation APIs Big data Wireless

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. Now that can happen too.

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. Ram Vittal is a Principal ML Solutions Architect at AWS. He has over 20 years of experience architecting and building distributed, hybrid, and cloud applications.

Government

Government Accountability Enterprise Engineering

How Vericast optimized feature engineering using Amazon SageMaker Processing

Use AWS PrivateLink to set up private access to Amazon Bedrock

Trending Sources

Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

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

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

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

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

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

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

Localize content into multiple languages using AWS machine learning services

Use a generative AI foundation model for summarization and question answering using your own data

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

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

Detect anomalies in manufacturing data using Amazon SageMaker Canvas

Reduce cost and development time with Amazon SageMaker Pipelines local mode

BI with an IQ - Revisited

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

Designing generative AI workloads for resilience

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

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

CREATING CREDIT UNION MEMBERS FOR LIFE

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

Senior back-end software engineer

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

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

What’s All This Fuss About Composability?

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

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

MLOps foundation roadmap for enterprises with Amazon SageMaker

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

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

Intelligently search Adobe Experience Manager content using Amazon Kendra

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

Demystifying machine learning at the edge through real use cases

Call Center Reporting - A New Paradigm

Use Amazon SageMaker Model Card sharing to improve model governance

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Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction