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Unlocking insights and enhancing customer service: Intact’s transformative AI journey with AWS

AWS Machine Learning

The goal was to refine customer service scripts, provide coaching opportunities for agents, and improve call handling processes. Using data from sources like Amazon S3 and Snowflake, Intact builds comprehensive business intelligence dashboards showcasing key performance metrics such as periods of silence and call handle time.

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25 Call Center Leaders Share the Most Effective Ways to Boost Contact Center Efficiency

Callminer

Metrics, Measure, and Monitor – Make sure your metrics and associated goals are clear and concise while aligning with efficiency and effectiveness. Make each metric public and ensure everyone knows why that metric is measured. Interactive agent scripts from Zingtree solve this problem. Bill Dettering.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

The DS uses SageMaker Training jobs to generate metrics captured by , selects a candidate model, and registers the model version inside the shared model group in their local model registry. Optionally, this model group can also be shared with their test and production accounts if local account access to model versions is needed.

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How Games24x7 transformed their retraining MLOps pipelines with Amazon SageMaker

AWS Machine Learning

Games24x7 is one of India’s most valuable multi-game platforms and entertains over 100 million gamers across various skill games. All the training and evaluation metrics were inspected manually from Amazon Simple Storage Service (Amazon S3). This helps in validating if our custom scripts will run on SageMaker instances.

Scripts 110
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Use Amazon Titan models for image generation, editing, and searching

AWS Machine Learning

The advanced AI model understands complex instructions with multiple objects and returns studio-quality images suitable for advertising , ecommerce, and entertainment. Before you can write scripts that use the Amazon Bedrock API, you need to install the appropriate version of the AWS SDK in your environment. exclusive) to 10.0

Scripts 134
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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. To run inference on this model, we first need to download the inference container ( deploy_image_uri ), inference script ( deploy_source_uri ), and pre-trained model ( base_model_uri ). Fine-tune the pre-trained model on a new custom dataset.

Scripts 98
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Streamline diarization using AI as an assistive technology: ZOO Digital’s story

AWS Machine Learning

Trusted by the biggest names in entertainment, ZOO Digital delivers high-quality localization and media services at scale, including dubbing, subtitling, scripting, and compliance. git+[link] ffmpeg-python Create an inference script to load the models and run inference Next, we create a custom inference.py