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Deploy Amazon SageMaker Autopilot models to serverless inference endpoints

AWS Machine Learning

In this post, we use the UCI Bank Marketing dataset to predict if a client will subscribe to a term deposit offered by the bank. For more information about regression and classification problem types, refer to Inference container definitions for regression and classification problem types. Solution overview.

Banking 71
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Avaya Shakes Off Debt, Heading to Public Market

Fonolo

We’ll definitely be writing more about that. Banking giant ING recently switched from an Avaya call center to a system built internally using Twilio APIs. Understanding Industry Benchmarks. Oracle is entering the game with “ Customer Engagement Cloud ”. Talkdesk is making strong gains. Wink wink.). More Reading.

Marketing 173
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JustCall Auto Dialer vs. Mojo Dialer: Which Dialer Is Better for Your Business? [Expert Comparison]

JustCall

And when you think about the range of features the latter offers at $49 per user per month — all 3 dialers, bulk SMS campaigns and workflows, live call monitoring , advanced analytics and reporting, API and webhooks, live call monitoring, and so much more, it is simply astounding. How are JustCall’s Sales Dialer and Mojo Dialer different?

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MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

The generated models are stored and benchmarked in the Amazon SageMaker model registry. A SageMaker pipeline is a series of interconnected steps (SageMaker processing jobs, training, HPO) that is defined by a JSON pipeline definition using a Python SDK. This pipeline definition encodes a pipeline using a Directed Acyclic Graph (DAG).

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Contact Center Technologies 2017: find out what 23 experts say

RichCall

Check out what recent reports and experts suggest, and take part in a contact center benchmarking survey to get more accurate data on the current contact center trends. Global Contact Centre Benchmarking Report, Dimension Data 2017. Dimension Data’s 2016 Global Contact Centre Benchmarking Report, © Dimension Data 2013-2016.

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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning

Generative AI definitions and differences to MLOps In classic ML, the preceding combination of people, processes, and technology can help you productize your ML use cases. If an organization has no AI/ML experts in their team, then an API service might be better suited for them. Only prompt engineering is necessary for better results.

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Information extraction with LLMs using Amazon SageMaker JumpStart

AWS Machine Learning

To deploy a model from SageMaker JumpStart, you can use either APIs, as demonstrated in this post, or use the SageMaker Studio UI. The trend continued with Jane from Australia, who on Nov 12th requested a shipment of ten high-definition monitors with total of $9000, emphasizing the need for environmentally friendly packaging.