Machine Learning as a Service

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For a while ago, it seemed like machine learning (ML) only existed in the labs of data scientists. In recent years, we’ve heard a lot about this new technology as a component of AI. Nevertheless, for the general reader, ML still belongs more to science fiction than real life.

As is often the case, we missed the point when ML technology invaded our daily lives. Facial recognition methods scan us on the streets. We communicate online with smart human chatbots. AI-powered fraud detectors protect our financial transactions. We use natural language processors for voice input. And if we are talking about ML implementations, then there is much more.

Costly and complicated laboratory experiments made ML developments available as user-friendly services. Firms presently see aggressive help as being the primary to implement ML solutions.

MLaaS at closer range

It is a generic term for a variety of interconnected services delivered in the form of online programs. Certain incorporate AI engines, pre-trained ML principles, including various tools designed to create and lead system machine learning standards at each scale.

Deployed in the cloud and delivered as a set, ML platforms are no longer the expensive and difficult-to-maintain solutions they used to be. Because MLaaS is cloud-based, customers can benefit from a variety of other cloud-based solutions such as elastic storage and compute power. 

Like other cloud services, MLaaS removes the in-house ML team from having to delve deeper into data science. Data preprocessing, model building, training and evaluation, and other processes are performed on the side of the service provider. 

To summarize the foregoing, MLaaS is a suite of tools, algorithms, and out-of-the-box custom machine learning modules designed to create working auspicious designs. Users may choose and customize them according to their needs.

ML algorithms track repeating models during data preprocessing. Traced principles underlie mathematical models. After extensive training, those models may give forecasts when new data is received.

Machine learning is all regarding pattern identification and probabilistic thinking.

 Studio Microsoft Azure

Azure Machine Learning Service provider primarily stands out a flexible, multi-faceted machine learning platform. Its innovative drag and drop algorithms and graphical interface are its strong points.

However, despite the availability of WYSIWYG controls, it takes a long time for newbies to learn the intricacies of Azure ML Studio. It supports nearly a hundred methods for predictive analytics.

Bot Service Framework is another set of tools that complements the functionality of Azure ML Studio. As the name suggests, this framework is intended for developing bots. It includes five templates for building bots and an IDE for developing, testing, and deploying bots.

The Bot Service Framework supports the .NET and Node.JS suites used to create your own bots, from simple Q&A to human-like virtual assistants. The framework allows you to deploy your own bots on popular platforms like:

  • Slack; 
  • Telegram; 
  • Skype; 
  • Bing; 
  • Facebook Messenger, etc.