Machine learning broke into our lives relatively recent even though the first computer learning software dates back to the 50s. Nowadays, developers progressively tailor this technologys capabilities to their projects making them even smarter and more beneficial. That’s why such giants as Google and Microsoft created their own machine learning platforms to move the whole industry forward and let developers easily integrate artificial intelligence features into their software. In this article, we will cover a list of ready-made ML as a service platforms, as well as examples of apps that are already powered with machine learning.
What is MLaaS? The same as SaaS or BaaS solutions, machine learning as a service platforms are intended to handle nearly all matters connected with infrastructure. So, developers are not obligated to messing with model training and evaluation, as well as worry about further cloud-based predictions. You are probably wondering how to get the result of those predictions into your internal infrastructure. Everything is quite simple -- these two can be united by means of REST APIs. The fact of the matter is that cloud machine learning services help to build first functioning models that are able to bring you prized insights based on predictions. Furthermore, you don’t even need a huge team of developers for that reason. The most powerful and popular representatives of MLaaS on the market are Amazone Machine Learning service along with Google Prediction and
Microsofts Azure Machine Learning that don’t require much expertise in machine learning for getting started with them.
Best machine learning platforms It is high time to consider in more detail the platforms that were already mentioned, as well as tell you about those ones that were not.
1.Google Prediction API Pricing: Free up to 10K units / if the limitation in 10K units is exceeded then $0.50 per 1,000 predictions, $0.05 per 1,000 streaming updates, $0.002 per MB of trained data. Google machine learning as a service offers developers a bunch of use cases among which are the detection of spam, sentiment analysis of customers, a recommendation system etc. The guides written for these cases will coordinate you during the creation of some basic models for them. It is worth pointing out the presence of pre-trained models provided by Google to facilitate the work of developers. We should mention a well-written API documentation that helps developers to jump to working with the platform as quickly as possible. In general, this machine learning environment created in Google is good for ML integration into a project especially if you have short deadlines.
Google Cloud Platform advantages
2.Azure Machine Learning Pricing: Free / $9.99 monthly Azure ML is a visual interface contributing to model building and training, as well as choosing algorithms to apply. The service itself is focused around the ML Studio framework that allows you to create modular solutions. This cloud-based machine learning platform is oriented on both newbies and data science adepts though nearly all its operations can be performed manually. We should single out a good documentation along with tons of machine learning solutions stored in a so-called Cortana Intelligence Gallery that can be reused by other developers in the future.
Model editor in Microsoft Azure ML
3.Amazon Machine Learning Pricing: $0.42/hr for computing, $0.10/1,000 predictions Amazon machine learning service is considered to be one of the market leaders. Respectively, it fits the projects with a tight deadline as well as possible. However, the level of automation of this service generates the limitation-related issues, so it is definitely not the most flexible machine learning solution on the market. The use cases for this machine learning platform vary from classification of documents to detection of frauds. Besides, it has SDKs for different programming languages including Ruby, Java, Node.js, .NET and some
others.
Adding data sources
4.BigML Pricing: From free up to $10,000 monthly Bearing in mind the diversity of its audience, the company has tried to meet the needs of each user in multiple aspects starting from the wide range of prices and well-grounded documentation ending with flexible API. This ML as a service platform can be used for basket analysis, predictive maintenance, customer segmentation etc. BigML offers a set of ready-made scripts even though it is small if compared to previous platforms.
5.PredictionIO
Pricing: Open source If you would like to be in charge of the deployment of your machine learning -thats what you are looking for. It is an open source server intended to let developers create predictive engines. This open source machine learning server has SDKs for multiple programming languages including Java, Ruby, PHP, and Python. Besides, the community has put their efforts into implementing SDKs for other languages like Node.js, Swift, and several more.
PredictionIO interface
6.IBM Watson Price: From free up to $80 per user monthly
It is hard to call this solution from IBM a full-grown platform. The current capabilities of IBM machine learning as a service platform are restricted by data visualization and descriptions of how various data values interact with each other. In general, this service brings automated predictive analytics along with cognitive capabilities that are definitely good news for users without any computer science background. As for data scientists, this platform may seem to be quite limited right now. Nevertheless, thats too early for making any final conclusions concerning the platform because it continues to develop.
IBM Watson: Automated predictive analytics
7.Tensor Flow Price: Open source
This is not exactly ML as a service but an open source library from Google that contains a bunch of machine learning tools. The library has no visual interface and oriented to developers who want to dive deeper into data science. To do that more smoothly, Tensor Flow is integrated into Google Cloud. Moreover, it can also be integrated with other service providers if necessary.