Google, Microsoft, And Amazon Place Bets On AI In The Enterprise

Google, Microsoft, And Amazon Place Bets On AI In The Enterprise

Google has new enhancements to its machine learning services (MLaaS). Amazon AWS announced the company’s own new MLaaS tools and services at AWS Re: Invent last November. MLaaS is still a young technology, but it may become a dominant AI platform for enterprises.

MLaaS: the promise and the problem

Machine Learning and Deep Learning are used by many different industries. For example, deep neural networks are trained with millions of data samples and use NVIDIA GPUs to do it faster, in order to recognize features and categories. In this new “AI Era,” many enterprises and government agencies are figuring out what they need to do in order to be part of it. So, they must decide which projects to fund, hire talent, buy servers and GPUs, clean their data, and then build and optimize their own Deep Neural Networks (DNNs). Indeed, MLaaS provides an easier option for deep learning because it uses pre-trained neural networks for image, video, voice, and natural language processing (NLP), which is offered by the major cloud service providers. So, you can just write a cloud-based application that accesses a pre-trained network via a simple API.

Google, Microsoft, and AWS: different strengths and approaches

Google: Google Cloud AutoML provides dashboards to enable the developer to easily gauge the precision of the AI model.

Google MLaaS

Google aims to leverage Google leadership expertise in Artificial Intelligence and Deep Learning. Actually, Google has over 7000 AI projects, and over one million AI users.

Google wants that TensorFlow is the most important AI hardware and software.

Apply AI to the development of AI. Google Cloud AutoML can be used to simplify the complex tasks of Deep Neural Network (DNN) development. So, Google Cloud AutoML builds a custom Deep Learning model, starting with the customer’s own data. Furthermore, Google Cloud AutoML comes with really cool dashboards so you can easily see the efficacy of the model as you develop and tune it. Google provides in-house data tagging as a service,

Microsoft MLaaS

Microsoft MLaaS aims to use Microsoft massive enterprise and government installed base and its extensive portfolio of productivity and business process tools to become the default provider of ML technologies in the enterprise.

Microsoft MLaaS provide many Machine Learning APIs to process every data type. So, it enables the user to extend the trained neural network with data samples that encompass the organization’s products, people, vocabulary, etc. Microsoft MSFT offers 29 APIs, and many of them support customization of the Deep Neural Network training data.

It provides the highest performing Machine Learning Framework for those customers who need to build their own Deep Neural Networks such as NLP.

Every Microsoft MSFT product provides smart features to Office 365, Dynamics, Windows, and eventually every product in the Redmond vault.

Amazon AWS MLaaS

Amazon AWS MLaaS is used to scale, and a rich toolset to provide the most cost-effective development and deployment platform for Artificial Intelligent applications.

Amazon AWS MLaaS is providing the tools and platforms as services on AWS. Furthermore, tools developed for Amazon Alexa and for Amazon’s own eCommerce are now available to help you easily build things such as a chat-bot, a voice-activated product, or service.

It provides world-class development tools like MXNet framework, Lex, Rekognition, and SageMaker, which are used to ease the development burden. Amazon AWS will be the deployment platform after the development process is finished. SageMaker is offering a fully-managed platform for the entire ML development lifecycle.

It provides the most cost-effective cloud infrastructure for every developer, and it doesn’t matter which CPU, GPU, or AI Framework the developer selects.

Conclusions

There are some limitations of MLaaS. For example, the pre-trained network as a service could not have all the faces, vocabulary, and objects that you want it to recognize. Additionally, it is not possible that you can have an AI application on your own infrastructure, and keep all that valuable data in-house. So, MLaaS may not be the AI on-ramp enterprises want to have. Microsoft MSFT and Google are attempting to address these functional limitations of MLaaS, but Google AutoML could produce more accurate results because AutoML is actually building a custom AI model. Actually, AutoML runs on NVIDIA GPUs.

 

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Description
Google has new enhancements to its machine learning services (MLaaS). Amazon AWS announced the company’s own new MLaaS tools and services at AWS Re: Invent last November. MLaaS is still a young technology, but it may become a dominant AI platform for enterprises.
By | 2018-11-23T03:50:44+00:00 November 23rd, 2018|Categories: Blog|Tags: , , , , , , |0 Comments

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