Accountability, Big data, Metrics and Scripts

Accountability

Big data

Metrics

Scripts

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 Engineering Best practices

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

Join 52,000+

Insiders

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Webinars

How to Optimize Call Monitoring: Automate QA and Elevate Customer Experience

MORE WEBINARS

Trending Sources

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

Callminer

SEPTEMBER 14, 2020

They serve as a bridge between IT and other business functions, making data-driven recommendations that meet business requirements and improve processes while optimizing costs. That requires involvement in process design and improvement, workload planning and metric and KPI analysis. Kirk Chewning. kirkchewning.

Call Center

Call Center Analytics First call resolution Metrics

Webinars

How to Optimize Call Monitoring: Automate QA and Elevate Customer Experience

MORE WEBINARS

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

AWS Machine Learning

FEBRUARY 13, 2024

Before moving to full-scale production, BigBasket tried a pilot on SageMaker to evaluate performance, cost, and convenience metrics. Use SageMaker Distributed Data Parallelism (SMDDP) for accelerated distributed training. Log model training metrics. Use a custom PyTorch Docker container including other open source libraries.

Engineering

Engineering Gamification Scripts Big data

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

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

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

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

Contact Center Trends 2021: The CX Watershed

Fonolo

NOVEMBER 17, 2020

As the focus of contact center turns to creating value rather than reducing expenses, KPIs like customer satisfaction and service level will become increasingly favored over metrics like Average Handling Time. FCR is the Most Important Metric. 2016: 50% of Global 1000 companies will have stored customer-sensitive data in the cloud.

Contact Center

Contact Center Gamification Call Center Average Handle Time

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

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

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

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

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

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.

Chatbots

Chatbots Contact Center Banking Technology

Customer Contact Central

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

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

Webinars

Trending Sources

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

Webinars

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

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

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

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

11 Contact Center Technologies to Boost Customer Satisfaction

Contact Center Trends 2021: The CX Watershed

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

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

Automate Amazon SageMaker Pipelines DAG creation

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

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

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

Stay Connected

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

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

Webinars

Trending Sources

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

Webinars

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

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

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

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

11 Contact Center Technologies to Boost Customer Satisfaction

Contact Center Trends 2021: The CX Watershed

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

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

Automate Amazon SageMaker Pipelines DAG creation

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

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

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

Stay Connected

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