The Best Machine Learning Frameworks To Work With In 2021

Here's an essential guide for machine learning professionals.

Machine learning is seeing increased adoption across many industries. Since 2020, there has been a surge in the amount of machine learning scientists and it is now important to understand the two aspects of the technology, mathematical and implementational. Common machine learning frameworks don't need mathematical understanding; hence, it is easier to master machine learning frameworks.

Machine learning frameworks are libraries that help developers create ML models and applications without using any core algorithms or technicalities. Each framework is designed to meet a different purpose. Here are some of the most-used machine learning frameworks that solve a variety of business problems.

1. Tensorflow

Tensorflow is a popular machine learning framework by Google. It is an open-source software library that has a comprehensive and flexible ecosystem of tools, libraries, and community resources. Tensorflow lets the developers build and ML-powered applications easily. Tensorflow leverages data flow graphs wherein branches (tensors) of data can be processed by a series of algorithms on a graph. The movement of this data on the system is known as flow, hence the name Tensorflow. This helps build and train ML models using intuitive APIs like Keras which can be used in speech recognition systems, image and video recognition and tagging, self-driving cars, text summarization, and sentiment analysis.

2. H2O

H2O is an open-source machine learning framework that provides access to ML algorithms in Python, Java, Scala, R, big data systems, and data sources. H2O is used as a solution to collect data, build models, and serve prediction. It features driverless AI and can function as a Python library with an open-source web-based environment called flow which allows interaction with the dataset during the training process. H2O is widely used in advanced analytics, fraud detection, and digital advertising.

3. Apache SINGA

Deep learning frameworks boast high-performance machine learning capabilities like NLP processing and image recognition. SINGA is an advanced project for developing an open-source machine learning library and facilitating the creation of deep learning models on large volumes of data. It is quite a simple programming model for doing that. It supports convulsive neural networks, restricted Boltzmann machines, and recurrent neural networks. SINGA also simplifies group setup using Apache Zookeeper.

4. Amazon ML

Amazon machine learning is a cloud-based service that is suitable for all developers to deploy machine learning. It boasts visualization tools and a wizard's guide through the entire process of creating ML models. For that, there is no requirement to have knowledge about complex algorithms. Amazon ML makes it easy to extract predictions for applications using simple APIs. This system can be used to analyze and predict customer behavior, recognize message content, predict quantities and intervals of customer service inquiries, personalize web services for customers and classify documents.

5. Microsoft Azure ML Studio

Machine learning requires massive amounts of data and computational power. Microsoft Azure ML Studio provides an apt environment for all ML applications. This is a GUI-based integrated development environment that is used for constructing and managing ML workflow on Azure.

6. Scikit-Learn

Scikit-Learn is an open library for data analysis written in Python for general purposes. It is based on other Python libraries like NumPy, SciPy, and Matplotlib. Scikit-learn contains several implementations for different ML algorithms. It can handle both supervised and unsupervised learning with a wide variety of algorithms and utilities that can make a perfect tool to start programming and structuring data analysis and statistical modeling systems.

7. Apache Mahout

Apache Mahout is an open-source deep learning platform that uses the MapReduce paradigm and is powered by Apache Hadoop. It functions with a distributed linear algebra framework to scribe and implement ML algorithms. This ML framework was actually built to enable scalable machine learning in Hadoop. Presently, Mahout has new additions like Samsara which leverages the mathematical environment and runs with a distributed spark pool.