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Abacus.AI Announces MLOps and DLOps Platform

Artificial Intelligence (AI) , Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), AI Model, AI-powered Solutions, Digital Transformation, ML Model, Natural Language Understanding (NLU), AI News, Artificial Intelligence News
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Abacus.AI, a foundational AI research company that solves the hard problems that enterprises face in the AI/ML space, recently announced a first-of-a-kind enterprise-scale real-time MLOps and DLOps platform. The streaming API of Abacus.AI enables customers to stream data from IoT sensors and social media interaction, online purchases, media views, and clickstream events in real-time. This data will be transformed by Abacus.AI, deep learning models will be trained, and contextual predictions will be generated in real-time.

Deep-learning systems produce powerful real-time AI models that are used in social media platforms like Facebook and search engines like Google, video platforms like YouTube, and mobile apps like Uber. Customer engagement and retention are increased through virtuous feedback loops. The models on YouTube, for instance, recommend live sports videos after a user watches an Olympics video and late-night comedy clips after he/she has changed their intent and is in the mood to laugh.

With Abacus.AI, organizations of all kinds, not just large technology companies, can easily build real-time, large-scale enterprise artificial intelligence (AI) systems. This means companies can train models on neural architecture search provided by Abacus.AI, or specify their models on frameworks such as TensorFlow and Pytorch and let Abacus’ platform handle everything else.

This solution combines all the key elements of an AI solution, including the creation of simple and flexible data pipelines, data cleaning and transformation, training and hosting of models, real-time feature storage, model monitoring, and explanations. Abacus.AI makes complex things possible by keeping simple things simple. ML beginners can build and host simple models through an intuitive UI, while advanced models can be built using APIs and Python code, and experienced data scientists can write SQL queries.

Both supervised and unsupervised deep-learning models can be used for a wide range of data processing tasks, such as e-commerce recommendations, newsfeed personalization, personalized search, and cloud spend monitoring. Abacus’ AI engine can also develop custom deep-learning models based on customers’ use-cases and datasets. Abacus’ AI engine is available to customers as well as the data science teams’ own models.

LSTMs, RNNs, Transformers, and Variational Auto-encoders are among the NN (Neural Network) types that deep-learning models select from a diverse array of options. As a result of these innovations, Abacus.AI is now capable of building custom AI models.

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