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TraceAI : Machine Learning Driven App and API Security

DevOps.com

API security Modern applications are mobile first and are built around cloud-native distributed microservices architectures. These architectures have become the basic building blocks for complex and reliable distributed web and mobile applications.

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ML.NET: A Robust Framework for Implementing Machine Learning in.NET Environments

Exadel

Python is irreplaceable for Machine Learning, but running Python in production can be a problem if other parts of the system are written using C#. ML.NET is a Machine Learning library for C# that helps deliver Machine Learning features in a.NET environment more quickly. That is where ML.NET can help.

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Machine Learning Pipeline: Architecture of ML Platform in Production

Altexsoft

Machine learning (ML) history can be traced back to the 1950s, when the first neural networks and ML algorithms appeared. Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements.

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Unlocking the Power of AI with a Real-Time Data Strategy

CIO

To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making. Cloud-native apps, microservices and mobile apps drive revenue with their real-time customer interactions.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs.

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Accelerating innovation with cloud-native apps on Microsoft Cloud

CIO

A modern bank must have an agile, open, and intelligent systems architecture to deliver the digital services today’s consumers want. These organizations have two choices: They can modernize their architecture and develop the technology they need in-house or collaborate with fintechs to do it for them.

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How AI and Microservices Create a Reliable Enterprise of the Future

DevOps.com

The Rise of Microservices Enterprise adoption of microservices is continuing to rise. Its popularity is so strong that 86% of development professionals internationally expect microservices to become the default application architecture within the next five years. At its core, a […].