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

AWS Machine Learning

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 102
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Contact Center Trends 2021: The CX Watershed

Fonolo

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.

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Buddying Up – Putting Virtual Employee Assistants at the Heart of Agent Development

TechSee

Companies that measure and hold reps accountable for the overall outcome of an episode, considering the emotional impact on customers, the frequency of the issue and overall company costs, find that they earn the loyalty of both their customers and agents. Gamification.

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The Top 7 Call Center Quality Assurance Software Solutions

Voxjar

Founded in 2012. Klaus has an easy sign up process that does not require that you speak with sales to launch an account. The next two vendors will hopefully introduce you to a new approach to quality assurance in your call center, powered by artificial intelligence and big data. Scorebuddy pricing. playvox.com.

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

AWS Machine Learning

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 71
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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

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. Multi-account considerations Data science workflows have to pass multiple stages as they progress from experimentation to production.

APIs 71
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Publish predictive dashboards in Amazon QuickSight using ML predictions from Amazon SageMaker Canvas

AWS Machine Learning

Exploring, analyzing, interpreting, and finding trends in data is essential for businesses to achieve successful outcomes. Business analysts play a pivotal role in facilitating data-driven business decisions through activities such as the visualization of business metrics and the prediction of future events. Choose Next: Tags.