MLaaS - GNY Brings Machine Learning On Chain

Updated: Jan 23

MLaaS; Amazon offers it, as does Google, Microsoft, IBM and many other leading tech providers. It is being used throughout industries such as healthcare, finance, retail etc, but what exactly is MLaaS? Let's take a closer look at this important tool which many believe will have a significant influence on how the world does business going forward.


You have probably heard of SaaS; software as a service, also known as "on-demand software" or "Web-based software". This is a software delivery model in which software is licensed on a subscription basis and access is centrally controlled as well as being centrally hosted.

MLaaS or Machine learning as a service is similar to SaaS in that it is on-demand, web-based, and often offered on a subscription basis. What it provides are machine learning tools as a component of cloud computing services, and without any of the downsides that often hamper organisation that attempt to go it alone and create their own machine learning software.

Those who avail of MLaaS benefit through avoiding the following negatives:

- Machine learning software's steep development costs and the associated time sunk into this pursuit.

- Machine learning developer recruitment and all the the risks and stresses involved when building ML software from scratch in-house.

- That niggling need to keep the software updated to maintain security and continually stay ahead of the pack.

All of these stresses can be eliminated through the use of MLaaS. This is a blessing to small and medium-sized enterprises as it allows them access to ML resources that up until this point have only been available to large corporations.

Machine learning categories that are offered as a service thorough MLaaS are vast, ranging from predictive analytics, to natural language processing, to computer vision, right through to deep learning.

The actual machine learning data processing does not need to be handled by the user of MLaaS, and there are no servers to be managed. The MLaaS providers’ servers or nodes manage it all for you. Once loaded with your data the algorithms and models that have been crafted by the MLaaS providers go about their assigned task of finding patterns in that data. Once all the number crunching and computation is complete the resulting patterns, predictions, and answers are then all fed back to the end user. This can be customised in many ways to suit the end users needs.

GNY's Union of Machine Learning and Blockchain Technologies


As mentioned at the beginning of this piece Amazon, Google, Microsoft, and IBM all have MLaaS offerings available. For example we have the Google Cloud Machine Learning Engine, IBM’s Watson, Amazon Transcribe for speech recognition, and Microsoft's Azure Machine Learning Studio.

Now enter GNY. GNY ‘s mission is to securely unlock the hidden value in data through advanced machine learning. What GNY offers beyond other services is that all this machine learning takes place on platform designed to be decentralized and fully integrated on the blockchain for data security.

Blockchain’s ability to consistently record data sets, which are reliable and secure, allows for a step change in the ability of machine learning’s power. However, until now, harnessing AI analysis for a chain required API calls off-chain, which immediately exposes the data to security risks. GNY answers this challenge by embedding our machine learning software directly on chain. The proprietary design embeds GNY within each node to create a non-linear network approach to problem solving and prediction. This milestone engineering achievement represents the first successful union of machine learning and blockchain technologies.

Now you can optimize data-driven business decisions securely with machine learning, at a fraction of the cost of our competitors. Think of GNY as your plug and play machine-learning assistant. Free to download, and you only pay for what you use with our GNY and LML utility tokens.

Learn more about what our Data Diagnostic can do for your business here.

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