Nowadays, machine learning has become an inherent part of our daily lives. From our news feed to cab prices, everything is being controlled by machine learning applications. As the machine learning technology is moving forward, developers have built an array of machine learning tools that help other developers create machine learning applications in an easy manner, This article discusses various cloud-based and open-source machine learning tools. Additionally, we also discuss some of the popular machine learning tools in the Python programming language.
Cloud-Based Machine Learning Tools
Cloud-based machine learning tools help you build machine learning applications even if you don’t have the required infrastructure. You can just log on to the cloud-based machine learning tools, upload your data, and train the available machine learning algorithms to create your own machine learning applications. Following are some of the most popular cloud-based machine learning tools.
Google Cloud ML Engine
Google Cloud ML engine is made available by Google. It provides a managed service for training and hosting machine learning applications. You can easily create a machine learning application using any type of data by employing available algorithms in the Google Cloud ML Engine.
Along with training and building machine learning applications, Google Cloud ML Engine also provides various cloud-based tools for deep learning and predictive modeling. Due to the resource availability, you can easily use Google Cloud ML Engine to train and deploy machine learning models with terabytes of data.
Google also provides various machine learning and deep learning tools like:
- Google Cloud AutoML for training and development of machine learning models.
- Google Cloud AI Platform to create and manage AI applications.
- Google Cloud speech-to-text helps in extracting text from speeches in 120 languages., Google Cloud Vision AI helps create cloud vision models for image processing, text identification, etc.
- Google Cloud Natural Language helps in natural language processing to analyze and classify text data.
Azure Machine Learning Studio
Azure machine learning studio is a cloud based machine learning tool provided by Microsoft to facilitate the development of machine learning applications. It provides a drag-and-drop feature for creating machine learning applications by integrating them with databases.
Along with the machine learning studio, Microsoft azure also provides the following tools such as Microsoft Azure Cognitive Service for smart cognitive services for application development and Microsoft Azure Databricks to facilitate Apache spark-based analytics.
IBM Watson Studio
Just like Google and Microsoft, IBM provides a cloud computing platform using the IBM cloud. IBM Watson Studio is used to facilitate the development of machine learning and artificial intelligence models with cloud-based tools. It also provides features that help in preprocessing, analysis, and visualization of data.
In addition to IBM Watson. IBM Cloud provides various machine learning tools such as IBM Watson speech-to-text, IBM Watson text-to-speech, IBM Watson Natural language understanding, IBM Watson visual recognition, and IBM Watson Assistant. These tools help you create various applications with features such as converting text to speech and vice versa, analyzing and classifying text data, classifying the images and videos, and creating and managing virtual assistants.
Just like other major tech companies, Amazon also provides its cloud-based machine learning platform named Amazon Machine Learning. It provides robust cloud based machine learning tools that help create efficient machine learning applications.
Amazon ML provides machine learning tools that help users identify patterns, build predictive models, and classify raw data. It also provides various visualization tools that help you understand the data in an easy manner.
You can also import a machine learning model into Amazon ML or export a machine learning model from Amazon ML. This helps you create a machine learning model on a cloud-based machine learning tool even if you want to deploy it somewhere else.
Along with the various machine learning algorithms to build and deploy machine learning applications, Amazon ML also provides you with batch APIs for bulk requests and real-time APIs for individual requests to help you retrieve outputs from the machine learning applications.
Google Colab is a cloud based machine learning tool that helps you run python programs on a server originally hosted by Google. It already has various Python libraries installed to facilitate the creation of machine learning applications. However, you can also install different machine learning libraries manually into the runtime.
After training your machine learning model, you can save the binary file to your google drive or download it to your machine. In this way, Google Colab helps you train and run complex machine learning models even if you don’t have enough computational capacity on you personal computer.
Open-Source Machine Learning Tools
Apart from the cloud-based machine learning tools made available by various tech firms, there are also various open-source machine learning tools that you can use to create machine learning applications. Following are some of the popular open-source machine learning tools.
Apple’s Core ML
Apple’s Core ML is an open-source machine learning tool used to integrate machine learning models into your applications. You can use the coremltools python library to convert machine learning models from other libraries like TensorFlow and scikit-learn to Core ML. After converting the machine learning model to Core ML, you can use them in your applications.
After integrating different machine learning models into Core ML, it provides a unified representation for all machine learning models. The Core ML APIs can then be used to make predictions, fine-tune machine learning models, etc on the user’s device.
When you run the machine learning model on the user’s device, it doesn’t require a network connection. This leads to better performance of the application and data security for the user. The application performs better because it doesn’t perform any task (the machine learning tasks are performed on the user’s device). Thus, the application remains responsive. Better data security is ensured because we don’t need to transfer the data from the user’s device to the server for processing.
Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL. It is designed to enable quick implementation of algorithms.
Mahout works with Apache Spark to facilitate building environments for hosting machine learning applications. The applications hosted with Apache Mahout can be scaled quickly and efficiently in order to meet demands. Mahout was originally designed to work with Hadoop. However, you can also use it with other frameworks like Fink and H2O.
Cortex describes itself as a “production infrastructure for machine learning at scale”.It can be used to deploy, manage, and scale machine learning models in production.
Cortex facilitates real-time, asynchronous, as well as batch processing for requests. It means that your application can respond to requests in real-time and autoscale based on in-flight request volumes, process requests asynchronously, as well as run distributed and fault-tolerant batch processing jobs on-demand.
With Cortex’s automated cluster management, your machine learning application can also perform autoscaling, run workloads on spot instances with automated on-demand backups, and create multiple clusters with different configurations.
Accord.NET contains various open-source machine learning tools for image and audio processing. It provides algorithms for numerical optimization, image, audio, and signal processing along with algorithms for statistical analysis, linear algebra, and artificial neural networks.
Accord.NET also supports libraries for graph plotting and visualization. However, a major drawback is that it supports only .NET-supported languages.
Knime is a machine learning tool designed to facilitate tasks such as data analytics, reporting, and data integration. Knime combines different components of machine learning and data mining using pipelining to facilitate better data analysis. This helps you create applications that can be used for business intelligence, financial analysis, and CRM.
Machine learning applications developed using Knime are easy to deploy and install. However, it is difficult to build a complicated machine learning model using Knime.
Weka is an open-source machine learning tool having a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Weka is open source software issued under the GNU General Public License.
Weka is aimed at making machine learning tools available to all. Using Weka, you can apply existing machine learning tools to your data. Alternatively, you can also create your own machine learning algorithms and publish them for others to use.
Weka also has a rich resource library consisting of online courses, and YouTube videos that will help you learn machine learning and data mining concepts in a better way. However, it doesn’t have very good documentation. This might upset you when you are developing your own machine learning application with Weka.
Shogun is one of the open-source machine learning tools having a rich collection of data structures and algorithms to facilitate machine learning tasks. It provides support for different languages such as Python, Octave, R, Ruby, Java, Lua, and Scala.
With Shogun, you can implement various machine learning algorithms such as Hidden Markov models and support vector machines. This helps you process large amounts of data. With its great customer support, presence of great features and functionalities, and ease of use, Shogun is also one of the best open-source machine learning tools.
In this article, we have discussed various open-source and cloud-based machine learning tools. Almost all of the machine learning tools discussed in this article are free to use and they will help you create better machine learning applications if you use them correctly.
If you were looking for a machine learning tool to start your project, you can choose one of these machine learning tools according to your requirements and start working on your project.
I hope you enjoyed reading this article. To know more about machine learning, you can read this article on machine learning techniques. You might also like this article on the best android apps for coding.
Stay tuned for more informative articles. Happy Learning!