Machine Learning refers to a computer intelligence to learn from huge chunks of data instead of predefined prototypes given by the developer. Though scientific enough, this has found its application in the commercial sector as well. But we cannot use the implementations directly and need tools to harness the power of machine learning. So, if you are someone who is looking forward to incorporate this, then use the tools given below to ease your task. So, without further ado, let’s get started!
It’s an open-source machine learning server that can be used by data scientists and developers to create predictive engines. To boost the performance, it can be installed with Apache Spark, MLlib, HBase, Spray, etc. Responding to dynamic queries is the USP of this tool. PredictionIO reduces the traditional heavy lifting setup and helps in making predictions.
Amazon Machine Learning
This amazing tool is managed service to build machine learning models. To use this you need not be an expert of ML concepts, having basic knowledge of ML will serve you equally well. The reason behind popularity of this tool is the ease with which it combines ML algorithms with visual tools so that you can easily create models, deploy, and predict using the same.
Azure Machine Learning Workbench
This tool is one of the three machine tools that were introduced in Ignite conference(September 2017). It can be defined as a downloadable tool that is compatible with both macOS and Windows. It helps the users to prepare data, experiment, and deploy models on cloud. The simple and user-friendly interface will help you do your work faster.
Basically, this tool was developed for the internal team of Google, but now it is available for masses. Being open-source, it is one of the favorite tools used. Airbnb, Ebay, ZTE, Uber, Dropbox, etc., have put their trust on this tool. Once you start using this, you’ll get to know why it has become favorite of data scientists and other researchers in short span.
Microsoft Distributed Machine Learning Toolkit (DMTK)
DMLT aims to ease crowded machine learning clusters so that it is easy to run multiple machine learning applications at the same time. This tool has gained immense popularity in this era of big data because it has both algorithmic and system innovations. This toolkit is also open-source and you can use the same for your creating your project.
IBM Watson Analytics
Another tool that’s trusted by most of the developers these days. This is designed in a way that organizations with minimum or no experience of predictive analytics can put their data to good use. Two variations of the same are available, namely Community Edition and Deep Analytics Edition. You can opt the one that suits you and your requirements the best.
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This platform was introduced to masses in 2011 and is a scalable ML platform that has been helping the students and professionals to develop their machine learning models. With the models thus prepared it is easier to conclude if the required result is acquired or do we need some alterations. Team BigML is also promoting machine learning in academics through their educational degree program.
It’s a highly dynamic distributed platform which is written in Python and is used for rapid deep learning application development. Using this, you can easily create models from more than 250 optimized units. Also, you can publish your results and run the application on cloud. Quite impressive indeed!
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Developed by the Berkeley Vision and Learning Center and the community members, this tool is defined as the deep learning C++ framework created for machine learning. If we talk about its implementation, then Google and Pinterest have used this in the operations. It’s also used for imaging based automatic system.
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It’s a collaborative effort from two big names Nervana and Intel who joined hands to give this open-source machine learning library. The constant updates show that it is also being used by the students and professionals.
These are the best platforms that could be relied upon for building your machine learning models and improve them. Do let us know if we’ve left anything significant.