Machine learning (ML) is reshaping the way companies make decisions and deal with ever-growing data.
Chatbots are taking over customer service queries. Banks are using intelligent software to detect fraud. SaaS companies are predicting when customers are likely to churn. And, yes, all these tasks (and many others) are powered by machine learning tools.
In the last few years, machine learning has become more accessible than ever, making it easier for businesses to adopt this technology. You can either build or buy a SaaS solution, depending on your budget, the amount of data you handle from day to day, and the technical skills available to you.
Open-source libraries allow you to build your own machine learning software. But you’ll need a team of developers and data scientists to navigate the complexities of open-source code. You’ll also need to spend months fine-tuning machine learning algorithms and integrating custom-built ML tools to your stack.
SaaS (software as a service) products for machine learning, on the other hand, are out-of-the-box solutions that let you get started with machine learning in next to no time. They often provide pre-trained machine learning models, as well as powerful APIs, and integrations with tools you use every day.
Read on to find the best machine learning tools and solutions for your business.
MonkeyLearn is a SaaS machine learning tool that makes text analysis accessible to everyone. You can build custom models for topic classification, keyword extraction, and more, and tailor them to your needs to gain rich insights from your data.
For a quick start, you can use pre-trained models like this sentiment analyzer.
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Knime is an open-source machine learning platform for data mining. The visual workflow editor has ML capabilities and allows you to create and execute data science workflows. You can use it to build services and applications, for predictive analytics, or to analyze customer or financial data.
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Accord.Net is a .NET machine learning framework written in #C language. It contains multiple libraries for machine learning, statistics, pattern recognition, and even audio and image processing. It’s popular for building applications related to computer vision and image categorization.
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Scikit-Learn is a free and versatile library for machine learning in Python. You can find supervised and unsupervised algorithms for classification, regression, and clustering, among others. It’s suitable for academic or commercial use.
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TensorFlow is a powerful open-source machine learning & deep learning library, created by the team at Google Brain. It hosts a wide array of tools and resources to help you build and train advanced ML models. Currently, it’s the most trending library for machine learning, with many big companies using it in their operations and for research.
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Weka is an open-source machine learning tool written in Java. It supports a variety of text and data analysis tasks, such as classification, regression, and data preprocessing. It also includes visualization tools, and is often used for teaching or research.
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PyTorch is an open-source framework for deep learning, developed by Facebook AI. It’s written in Python (but also available in C++) and provides a tool package for building deep learning projects. It’s the leading framework in research, especially for Natural Language Processing (NLP) and computer vision applications.
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Keras is an open-source deep learning library built on top of TensorFlow. It provides an intuitive interface for artificial neural networks, and takes care of the mathematical and computational aspects to facilitate the process of building a model. It’s considered state-of-the-art in research and it’s used by many scientific organizations.
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RapidMiner is a data science platform to help analytics teams create predictive machine learning models. It provides a collection of tools and products that companies can use for machine learning and data analytics. There’s a free version available.
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The Google Cloud ML Engine is a cloud-based AI platform, used to create and run machine learning projects at scale. It’s aimed at developers and data scientists. You can easily build and train custom models using its tool AutoML, or deploy machine learning models which have been created externally.
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Azure Machine Learning Studio is a cloud-based environment that provides low-code and no-code tools for building machine learning tools. This platform allows data scientists to experiment with different algorithms, create workflows, and visualize the results along the way.
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IBM Watson is a suite of AI tools and solutions to help businesses extract value from their data. Watson Studio allows you to prepare data and build AI models. Then, you can deploy and run your ML models with Watson Machine Learning.
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Apache Mahout is an open-source machine learning software created by Apache. It runs on top of Hadoop Map Reduce, works well with huge data sets, and combines three machine learning techniques: classification, recommendation, and clustering. Many big companies, from Yahoo to LinkedIn, use Apache Mahout.
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Apache Spark is an open-source distributed framework for big data processing and machine learning. One of the things that makes it stand out from its competitors is that it processes data in-memory, which makes it perfect for working with huge datasets and delivering real-time analytics.
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Amazon Machine Learning (AML) is a cloud-based set of machine learning services within the AWS environment. This includes Amazon SageMaker ‒ which allows data scientists to build, train, and deploy machine learning models ‒ as well as pre-trained models to use in your applications.
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Google Colab allows you to write and run Python code in an interactive environment called a Colab notebook. You can easily execute and write code, share your Colab notebooks with co-workers or friends, and allow them to comment or even edit them. Colab notebooks are Jupyter notebooks that are hosted by Colab. It’s used extensively by the machine learning community, to import datasets, train classifiers, and evaluate models.
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Machine learning tools will make your business smarter by quickly sorting data and delivering actionable insights. There are many options available, but the quickest way to get started is with SaaS machine learning tools.
Ready-to-use machine learning software, like MonkeyLearn, is perfect for businesses looking for a quick setup, customized solutions, and a powerful suite of machine learning models. Want to know more about how machine learning tools can solve your problems?
Request a free demo, personalized for your use case, or check out MonkeyLearn’s plans and pricing.
November 2nd, 2020