APIs, Big data, Engineering and Metrics - 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

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

This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform. The platform has shown a 3% boost to customer engagement metrics tracked (liking a show, following a creator, enabling upcoming show notifications) since its launch in May 2022.

Personalization

Personalization APIs Engineering Metrics

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

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. Solution overview.

Analytics

Analytics Engineering APIs 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

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 predicted value indicates the expected value for our target metric based on the training data.

Metrics

Metrics Analytics 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

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. There are also drill-down reports that promise to let your managers slice and dice their data anyway they choose. Rewind it Back Let's take a look back to 2005 when "Web 2.0"

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

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 Calibration

CREATING CREDIT UNION MEMBERS FOR LIFE

Enghouse Interactive

JULY 10, 2018

Contact Babel 2017 DMG says, “It can be stated with some confidence that first-contact resolution is seen as the key to a successful contact center: 76% of the report’s respondents place first-contact resolution as being one of the top 3 metrics that are most influential on customer satisfaction, with 31% stating it as being no.1.”.

Self service

Self service CRM Contact Center 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). Adds an IAM authorizer.

APIs

APIs Government Accountability Scripts

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

Improve governance of your machine learning models with Amazon SageMaker

AWS Machine Learning

DECEMBER 1, 2022

With the SageMaker Python SDK, you can seamlessly update the Model card with evaluation metrics. 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.

Government

Government Metrics APIs Engineering

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

AWS Machine Learning

JULY 21, 2022

As a result, this experimentation phase can produce multiple models, each created from their own inputs (datasets, training scripts, and hyperparameters) and producing their own outputs (model artifacts and evaluation metrics). We also illustrate how you can track your pipeline workflow and generate metrics and comparison charts.

Metrics

Metrics Scripts Enterprise Telecommunications

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. If the model quality metric (for example, RMSE for regression and F1 score for classification) doesn’t meet a pre-specified criterion, the model quality check step is marked as failed.

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

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. With other standard metrics, the improvement ranged from 50–130%.

Scripts

Scripts Engineering Analytics 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. or "Does Service Level include Calls abandoned?"). Now that can happen too.

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. Consequently, maintaining and augmenting older projects required more engineering time and effort. Logging and monitoring. Kubeflow Logging.

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

Customer Contact Central

How Vericast optimized feature engineering using Amazon SageMaker Processing

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

Trending Sources

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

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

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

Detect anomalies in manufacturing data using Amazon SageMaker Canvas