machine learning

Gluon-Interface to deploy machine learning models

Gluon-Interface to deploy machine learning models

Machine learning is an application of Artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

The exponential rise of machine learning is as much a result of technological advancement as it is the active community growing around it.

This includes researchers working on core algorithms, as well as practitioners who are pushing the boundaries of how artificial intelligence can be applied. Training machine learning algorithms can be a tough and time-consuming process. It also requires huge investment for highly specialized tools.

To overcome all these constraints in Machine learning AWS (AMAZON WEB SERVICES) and MICROSOFT both the tech-giants have teamed up to launch an open-source and deep learning interface known as GLUON, which allows developers to more easily and quickly build models, without compromising performance.Gluon-interface for developing machine learning models

The interface would give developers a place where they can prototype, build, train and deploy models. It will help developers who are new to machine learning and will find this interface more familiar to common coding methods since ML models can be defined and changed very common to other data structures. Gluon provides a clear, concise API (application program interface) for defining machine learning models using a collection of pre-built, optimized neural network components. Gluon is available in Apache MXNet, later in Microsoft Cognitive Toolkit release, and in more frameworks over time.

Two tech-giants teamed together to deploy machine learning tools through GLUON.

Gluon introduces to four key innovations:

  1. FRIENDLY API: Gluon networks can be defined using a simple, clear, concise code and this is a lot easier to learn for developers to implement machine learning.
  2. DYNAMIC NETWORKS: The network definition in Gluon is different, it can bend and flex like any other data structure.
  3. THE ALGORITHM CAN DEFINE THE NETWORK: The model and the training algorithm are brought much closer together.
  4. HIGH-PERFORMANCE OPERATIONS FOR TRAINING: It makes possible to have a friendly, concise API and dynamic graphs, without sacrificing training speed.

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It will be a matter of fact that how Google and Android tackle this kind of problem of machine learning and come up with an efficient solution. Developers can get started with Gluon which is available in Apache MXNet and later coming tools which are mentioned earlier.

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