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Top 5 Books On AutoML To Streamline Your Data Science Workloads

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AutoML tools are the need of the hour for data scientists to reduce their workloads in the world where the generation of data is only increasing exponentially. Readily available AutoML tools make the data science practitioner's work more comfortable and covers necessary foundations needed to create automated machine learning modules. And with the spur in data and the potential that this data holds, data scientists will benefit more by using AutoML capabilities. As we approach the midpoint of 2020, it is slowly being recognised that this year will see an increase in adaptation of AutoML. With the massive potential of AutoML about to burst, non-data science professionals and data science practitioners will look to get a more comprehensive view on the technology.


Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About

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A few days ago, Amazon announced the release of AutoGloun, a new toolkit that simplifies the creation of deep learning models with just a few lines of code. The release marks Amazon's entrance in the ultra-competitive Automated machine learning(AutoML) space which is becoming one of the hottest trends for enterprise machine learning platforms. With some many news around the AutoML ecosystem, sometimes it becomes hard to differentiate signal from noise. Today, I would like to explore some of the most innovative AutoML stacks in the market that don't receive that much publicity. AutoML is becoming one of the most popular topics in modern data science applications.


Google ups its AI services with new Contact Center solution and developer tools - MarTech Today

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Google is boosting its AI-as-a-service offerings this week, most notably with the alpha release of a new Contact Center AI solution. Contact Center AI is built around its Dialogflow development suite for conversational agents, which was launched last fall and already in wide use. Dialogflow Enterprise Edition now has the ability to build AI-powered virtual agents for contact centers, a Phone Gateway for taking calls without infrastructure, Knowledge Connectors for understanding unstructured data like FAQs and Sentiment Analysis. In Contact Center AI, a Virtual Agent first answers the call and handles it if possible. If not, it passes the call to a human representative, who is helped by an Agent Assist system that continues to monitor the call and provide supporting info as needed.


Google launches Cloud AutoML to automatically build custom AI models

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Google today announced a new cloud service that's designed to make it easier for companies to create custom machine learning algorithms for processing images. Called Cloud AutoML Vision, the system allows developers to upload a bunch of images to Google's cloud and receive a custom model in return.


Google Launches Cloud AutoML for Building Image Recognition Models

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Yesterday, tech giant Google announced its latest solution, the Cloud AutoML, that will enable developers, even those that lack machine learning expertise, to build image recognition models. It is said to be a part of the company's initiative to democratize AI learning and provide a simple approach that anyone can easily understand.