Remove Accountability Remove Construction Remove Metrics Remove Scripts
article thumbnail

Build an air quality anomaly detector using Amazon Lookout for Metrics

AWS Machine Learning

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

Metrics 69
article thumbnail

Amazon SageMaker with TensorBoard: An overview of a hosted TensorBoard experience

AWS Machine Learning

It provides a suite of tools for visualizing training metrics, examining model architectures, exploring embeddings, and more. Solution overview A typical training job for deep learning in SageMaker consists of two main steps: preparing a training script and configuring a SageMaker training job launcher. x_test / 255.0 x_test / 255.0

Scripts 72
Insiders

Sign Up for our Newsletter

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

article thumbnail

International Contact Centre Operations Tips & Best Practices

Callminer

Encourage agents to cheer up callers with more flexible scripting. “A 2014 survey suggested that 69% of customers feel that their call center experience improves when the customer service agent doesn’t sound as though they are reading from a script. They are an easy way to track metrics and discover trends within your agents.

article thumbnail

Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library

AWS Machine Learning

For example, in some e-commerce platforms, account registration is wide open. Fraudsters can behave maliciously just once with an account and never use the same account again. Additionally, it’s challenging to construct a streaming data pipeline that can feed incoming events to a GNN real-time serving API.

article thumbnail

Putting Humanity in Contact Centers

Customer Relationship Metrics

Ownership over Accountability. When your focus is on how to hold people accountable, it takes your focus off an important question: “Why do we need to hold people accountable in the first place?”. She states that if you believe people need to be held accountable, what is YOUR underlying belief? Why is that? Ownership.

article thumbnail

Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

For a quantitative analysis of the generated impression, we use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), the most commonly used metric for evaluating summarization. This metric compares an automatically produced summary against a reference or a set of references (human-produced) summary or translation.

article thumbnail

Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Evaluate model performance on the hold-out test data with various evaluation metrics. 2xlarge instances, so you should raise a service limit increase request if your account requires increased limits for this type. Because it’s a binary classification task, we use the accuracy score and F1 score as the evaluation metrics.

Scripts 71