Accountability, Big data and Scripts - Customer Contact Central

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

AWS Machine Learning

FEBRUARY 13, 2024

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

Accountability

Accountability Government Engineering Scripts

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

AWS Machine Learning

APRIL 11, 2023

One important aspect of this foundation is to organize their AWS environment following a multi-account strategy. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs. In this post, we show how you can extend that architecture to multiple accounts to support multiple LOBs.

APIs

APIs Accountability Scripts Enterprise

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

AWS Machine Learning

OCTOBER 31, 2024

An AWS account and an AWS Identity and Access Management (IAM) principal with sufficient permissions to create and manage the resources needed for this application. If you don’t have an AWS account, refer to How do I create and activate a new Amazon Web Services account? The script deploys the AWS CDK project in your account.

APIs

APIs Scripts Accountability Construction

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

NOVEMBER 9, 2023

Central model registry – Amazon SageMaker Model Registry is set up in a separate AWS account to track model versions generated across the dev and prod environments. Approve the model in SageMaker Model Registry in the central model registry account. Create a pull request to merge the code into the main branch of the GitHub repository.

Scripts

Scripts Accountability Best practices Engineering

How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

AWS Machine Learning

JUNE 17, 2024

Batch transform The batch transform pipeline consists of the following steps: The pipeline implements a data preparation step that retrieves data from a PrestoDB instance (using a data preprocessing script ) and stores the batch data in Amazon Simple Storage Service (Amazon S3).

Scripts

Scripts Engineering Metrics Big data

Use Snowflake as a data source to train ML models with Amazon SageMaker

AWS Machine Learning

MARCH 8, 2023

We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket. Store your Snowflake account credentials in AWS Secrets Manager. Ingest the data in a table in your Snowflake account.

Scripts

Scripts Accountability Enterprise Management

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

Government

Government Management APIs Accountability

Automate Amazon SageMaker Pipelines DAG creation

AWS Machine Learning

FEBRUARY 29, 2024

You can then iterate on preprocessing, training, and evaluation scripts, as well as configuration choices. framework/createmodel/ – This directory contains a Python script that creates a SageMaker model object based on model artifacts from a SageMaker Pipelines training step. script is used by pipeline_service.py The model_unit.py

Scripts

Scripts Engineering Metrics Best practices

Contact Center Trends 2024: Our Predictions

Fonolo

DECEMBER 8, 2023

We live in an era of big data, AI, and automation, and the trends that matter in CX this year begin with the abilities – and pain points – ushered in by this technology. For example, big data makes things like hyper-personalized customer service possible, but it also puts enormous stress on data security.

Contact Center

Contact Center Big data Chatbots Scripts

Deploy a Slack gateway for Amazon Bedrock

AWS Machine Learning

JUNE 19, 2024

Prerequisites You need an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. If you don’t have an AWS account, see How do I create and activate a new Amazon Web Services account?

APIs

APIs Engineering Accountability Big data

Customer Experience Automation: Transforming the Future of Customer Service

TechSee

AUGUST 7, 2024

Today, CXA encompasses various technologies such as AI, machine learning, and big data analytics to provide personalized and efficient customer experiences. Over time, additional interactive solutions like IVR systems added the ability to automate basic queries like account balances or simple troubleshooting.

Customer Experience

Customer Experience Customer Service Interactive Voice Response Chatbots

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

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning

MARCH 15, 2024

Healthcare organizations must navigate strict compliance regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, while implementing FL solutions. FedML Octopus is the industrial-grade platform of cross-silo FL for cross-organization and cross-account training.

Healthcare

Healthcare Big data Engineering Accountability

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

AWS Machine Learning

DECEMBER 21, 2022

The offline store data is stored in an Amazon Simple Storage Service (Amazon S3) bucket in your AWS account. SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Table formats provide a way to abstract data files as a table. You can find the sample script in GitHub.

Scripts

Scripts Engineering APIs Big data

Machine learning with decentralized training data using federated learning on Amazon SageMaker

AWS Machine Learning

AUGUST 22, 2023

However, sometimes due to security and privacy regulations within or across organizations, the data is decentralized across multiple accounts or in different Regions and it can’t be centralized into one account or across Regions. Each account or Region has its own training instances.

Scripts

Scripts Accountability Transportation Benchmark

Build a custom UI for Amazon Q Business

AWS Machine Learning

JUNE 12, 2024

Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account set up. An IAM role in the account with sufficient permissions to create the necessary resources. If you have administrator access to the account, no additional action is required. You can also find the script on the GitHub repo.

APIs

APIs Enterprise Accountability Feedback

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

AWS Machine Learning

MARCH 23, 2023

default_bucket() upload _path = f"training data/fhe train.csv" boto3.Session().resource("s3").Bucket To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker. resource("s3").Bucket Bucket (bucket).Object Object (upload path).upload

Scripts

Scripts APIs Government Analytics

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

AWS Machine Learning

MAY 8, 2023

How to use MLflow as a centralized repository in a multi-account setup. Prerequisites Before deploying the solution, make sure you have access to an AWS account with admin permissions. You can use this script add_users_and_groups.py How to extend Studio to enhance the user experience by rendering MLflow within Studio.

APIs

APIs Government Accountability Scripts

Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

OCTOBER 4, 2022

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. Register model step (model package). Fail step (run failed).

Scripts

Scripts Engineering Management APIs

Run image segmentation with Amazon SageMaker JumpStart

AWS Machine Learning

AUGUST 26, 2022

We fetch any additional packages, as well as scripts to handle training and inference for the selected task. You can use any number of models pre-trained for the same task with a single training or inference script. Fine-tune the pre-trained model. The second is incremental training.

APIs

APIs Scripts Enterprise Big data

21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

Callminer

SEPTEMBER 14, 2020

For instance, a call center business analyst might recommend implementing an interaction analytics solution for a collections and accounts receivables management (ARM) firm to ensure that call center agents meet compliance requirements for debt collection. They can assess how current scripts are performing and change them as needed.

Call Center

Call Center Analytics First call resolution Metrics

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

AWS Machine Learning

FEBRUARY 13, 2024

During each training iteration, the global data batch is divided into pieces (batch shards) and a piece is distributed to each worker. Each worker then proceeds with the forward and backward pass defined in your training script on each GPU.

Engineering

Engineering Gamification Big data Scripts

3 Things Make or Break your Conversational AI Experience

SmartAction

NOVEMBER 4, 2020

So you have to have AI that is purpose-built for this kind of challenge, which means training models with domain-specific telephony audio, and, more importantly, the ability to customize language acoustic models on a question by question basis to account for the narrow scope of expected answers. 2nd Factor – Human-centric Design.

Big data

Big data Interactive Voice Response Virtual Agent Scripts

Guest Blog: Contact Center Talent in These Changing Times, Part 1 – Setting the Stage

Calabrio

NOVEMBER 25, 2020

Technology is continuously enabling convenient consumer options, such as account balance notifications for banking and same-day delivery and price-matching features for online shopping. The one-size-fit-all script no longer cuts it. Consumer behavior is at the top of that list, rapidly evolving as customers become more empowered.

Contact Center

Contact Center Self service Analytics Banking

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

AWS Machine Learning

MARCH 22, 2023

An AWS account with permissions to create AWS Identity and Access Management (IAM) policies and roles. Access and permissions to configure IDP to register Data Wrangler application and set up the authorization server or API. You must configure the IdP to use ANY Role to use the default role associated with the Snowflake account.

APIs

APIs Engineering Analytics Scripts

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. Each project maintained detailed documentation that outlined how each script was used to build the final model. AWS Secrets Manager to protect secrets needed to access your applications.

Engineering

Engineering Healthcare APIs Scripts

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). Amazon SageMaker now supports cross-account lineage tracking and multi-hop lineage querying.

Metrics

Metrics Scripts Enterprise Telecommunications

If you want to be customer obsessed, do this.

Infinity

JANUARY 22, 2019

As explained previously, big data sets mean big information. For example; If your data shows you that 56% of customers abandoned carts at the point of sale. We need to experience their journey by putting down the big data for a moment, and embarking on their journey. We’re not in their heads.

Big data

Big data Customer emotions Scripts Sales

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

AWS Machine Learning

AUGUST 3, 2023

Prerequisites For this walkthrough, you should have the following prerequisites: An AWS account. Two components need to be configured in our inference script : model loading and model serving. On top, he likes thinking big with customers to innovate and invent new ideas for them. iterdir(): if p_file.suffix == ".pth":

APIs

APIs Scripts Engineering Technical Support

Guest Blog: Contact Center Talent in These Changing Times, Part 1 – Setting the Stage

Calabrio

NOVEMBER 25, 2020

Technology is continuously enabling convenient consumer options, such as account balance notifications for banking and same-day delivery and price-matching features for online shopping. The one-size-fit-all script no longer cuts it. Consumer behavior is at the top of that list, rapidly evolving as customers become more empowered.

Contact Center

Contact Center Self service Analytics Banking

Conversational AI: Everything You Need to Know for Your Contact Center

Balto

JUNE 20, 2023

And if you’re still relying on a traditional contact center model with long wait times, scripted interactions, and frustrated customers, your business is destined to lose a lot of customers, and concurrently, money. Customer expectations have reached new heights, and businesses must adapt to meet their demands.

Contact Center

Contact Center Chatbots Complaint resolution Wait times

11 Contact Center Technologies to Boost Customer Satisfaction

TechSee

DECEMBER 5, 2017

Fully customizable, Enchant includes features such as unlimited Help Desk Inboxes, smart folders that update in real time, multiple knowledge base sites with their own set of articles, multiple messengers in a single account with each pointing to a different team or configured for a different website.

Contact Center

Contact Center Technology Self service Call Center

Personalized Customer Experience: How Can Your Contact Center be Effective?

NobelBiz

SEPTEMBER 21, 2021

To maintain your customers’ and prospects’ confidence, personalize your scripts by piquing their interests. The bulk gathering and fine-tuning of consumer data (big data) can open up new possibilities in the field of predictive analysis, allowing smart data to intelligently anticipate the client’s next requirements.

Contact Center

Contact Center Personalization Customer Experience CRM

Control data access to Amazon S3 from Amazon SageMaker Studio with Amazon S3 Access Grants

AWS Machine Learning

SEPTEMBER 26, 2024

Prerequisites To set up the SageMaker Studio environment and configure S3 Access Grants as described in this post, you need administrative privileges for the AWS account you’ll be working with. If you don’t have these permissions, consult with your AWS administrator or account owner for guidance. preprocess.py file locally.

Scripts

Scripts Accountability Engineering Big data

Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

SmartKarrot

APRIL 17, 2023

Measuring the ROI of AI chatbots requires a holistic approach that takes into account both tangible and intangible benefits. The integration of AI chatbots with other technologies such as Machine Learning (ML), Natural Language Processing (NLP), and Big Data analytics will enable them to provide more personalized and intelligent responses.

Chatbots

Chatbots Scripts Enterprise Personalization

From CTO to CX Industry Analyst: An In-depth Conversation with Mark Hillary

Vistio

OCTOBER 5, 2021

And you can look at various specific areas such as data analytics, big data, being able to study patterns within data, using artificial intelligence or using machine learning to actually gather up every customer interaction, and remember the original problem and the solution.

Contact Center

Contact Center Chatbots Banking Technology

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

AWS Machine Learning

JANUARY 26, 2024

In addition to awareness, your teams should take action to account for generative AI in governance, assurance, and compliance validation practices. You should begin by extending your existing security, assurance, compliance, and development programs to account for generative AI.

Best practices

Best practices Government Engineering Accountability

Marketing & Social Content Writer

Stratifyd

DECEMBER 20, 2018

Key accountabilities & responsibilities. Create slide decks, presentations, digital material, weekly blogs, and scripts for animations and videos. Experience and/or knowledge of AI, Big Data, Tech. Write/produce internal newsletter, wrap-ups on events for social media and blogs. Preferred Skills & Experience.

Marketing

Marketing Brand ambassadors Healthcare Big data

Contact Center Trends 2021: The CX Watershed

Fonolo

NOVEMBER 17, 2020

More and more, customers simply want to solve inquires on their own – especially for simple questions like “what’s the balance on my account.” A properly scripted menu leads customers to the answers they need, provides them with the opportunity to navigate to a live agent, and decreases the overall call volume that reaches the call center.

Contact Center

Contact Center Gamification Call Center Average Handle Time

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

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

Trending Sources

Create a generative AI–powered custom Google Chat application using Amazon Bedrock

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

How Twilio used Amazon SageMaker MLOps pipelines with PrestoDB to enable frequent model retraining and optimized batch transform

Use Snowflake as a data source to train ML models with Amazon SageMaker

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Automate Amazon SageMaker Pipelines DAG creation

Contact Center Trends 2024: Our Predictions

Deploy a Slack gateway for Amazon Bedrock

Customer Experience Automation: Transforming the Future of Customer Service

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

Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

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

Machine learning with decentralized training data using federated learning on Amazon SageMaker

Build a custom UI for Amazon Q Business

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

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

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Run image segmentation with Amazon SageMaker JumpStart

21 Business Analysts & Call Center Leaders Reveal the Optimal Role of the Business Analyst in Call Center Operations

How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

3 Things Make or Break your Conversational AI Experience

Guest Blog: Contact Center Talent in These Changing Times, Part 1 – Setting the Stage

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

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

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

If you want to be customer obsessed, do this.

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

Guest Blog: Contact Center Talent in These Changing Times, Part 1 – Setting the Stage

Conversational AI: Everything You Need to Know for Your Contact Center

11 Contact Center Technologies to Boost Customer Satisfaction

Personalized Customer Experience: How Can Your Contact Center be Effective?

Control data access to Amazon S3 from Amazon SageMaker Studio with Amazon S3 Access Grants

Revolutionizing Communication: Unleashing the Power of the Best Artificial Intelligence Chatbots

From CTO to CX Industry Analyst: An In-depth Conversation with Mark Hillary

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

Marketing & Social Content Writer

Contact Center Trends 2021: The CX Watershed

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