Most of the App you use in day to day life is Machine Learning enables. It is really difficult to deploy and manage Machine Learning applications in your own environment. Specially Machine learning-based Applications are quite resourced consuming in terms of hardware requirements. I found the majority of people work in AI-based company to support the infrastructure behind such applications. To overcome this problem we have good news. We have some cloud-based solutions in the market where you can run and deploy your ML-based application without any headache of managing server and all. Even you will get most of the preprepared code base. The article Top 5 Cloud Platform where you get Machine Learning as a service is full of relevant information over this interesting topic.
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Top 5 Cloud Platform for Machine Learning as a service-
Cloud Platform which provides ML as service offers hardware to run and your code with dependencies. I mean suppose you build some Intelligent App on the top of TensorFlow and some other python dependencies. You may directly run in this cloud-based platform. These Platforms are scalable as well. I mean, you have to pay according to your usages and when you need more bandwidth support. It also helps in that. Before starting I would like to inform you, Please do not take the order as ranking. It is really difficult to give rank them because all 5 are best.
Amazon is really rocking. Back to Back Amazon is providing simpler technical solutions for complex things. Machine Learning is no more an exception. It is really easier to create a Machine Learning model using Amazon Machine Learning. They provide a very simpler API interface to train and predict your data. The pricing model for AWS Machine Learning is also quite interesting. You only need to pay based on usages. Actually AWS provide other infra solution like S3, Dynamo DB which make the development quite easy.
From the very beginning, I have told it is not lesser than AWS ML services or any other else. Just because I put it on second, it does not mean to say Microsoft Azure Machine Learning Studio has fewer features than Amazing services for ML. I found its drag and drop feature most user friendly than other Machine Learning as a service provider. This drag and drop reduce the necessity of coding expertise in data science.
When it comes to cloud and AI, We should not underestimate Google. Google already providing so many AI-based solutions to us. Google’s CLOUD MACHINE LEARNING ENGINE is an amazing Machine Learning as a service provider in the term of cost and performance. Most of the open-source libraries in Data Science are provided and backed by Google itself like TensorFlow etc. You can leverage them in CLOUD MACHINE LEARNING ENGINE.
IBM Watson is one of the most popular cloud solution providers in the corporate world . Especially I like their text analytics and NLP solution most. Although it’s only my preference. I found very accurate and easy chatbot development using IBM Watson.
BigML is not a big name in the cloud or platform as a service provider but it really effective and specific to Machine Learning. I think you should invest some time to read their features and functionality. It is cost-effective and performance-oriented too. It supports multiple cross-functional data sources as well.
We are living in a time where every next five years in technology is a new era. AI is capturing most of the knowledge base jobs. Now, these Machine Learning as a service is again an initiative to boost this process.
The intent of writing this article to give you an overview of Machine Learning as a service and available options. I have tried to demonstrate them in very lesser words. That is why I did not mention pricing and other details in this article. I will recommend you to visit their website. It will help you to mine more effective information. I hope you like this article. In case you have some other suggestion on Machine Learning as a service option for us. Please comment.
Data Science Learner Team
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