2020 has been a major tipping point for machine learning, with its increased adoption across different industries. As more and more professionals pick up machine learning skills, it is important to understand the difference between mathematical and implementation skills. Even without getting into the maths of ML, you can learn to use common machine learning frameworks to implement machine learning to your work. In this article, we’ll walk you through top machine learning frameworks that you can master.

 

First, what are Machine Learning Frameworks?

Machine learning frameworks are tools or libraries that help a developer to create ML models or applications without extensively using any core algorithms or any technicalities.

Each framework is designed to serve different purposes. Here are some of the most popular machine learning frameworks fit for solving your business challenges.

 

1. Tensorflow

Tensorflow is a popular machine learning framework by Google. It is an open-source software library and has a comprehensive, flexible ecosystem of tools, libraries and community resources. Tensorflow allows the developers to easily build and deploy ML-powered applications.

TensorFlow implements data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The movements of data through the system are called “flows” – hence the name. Graphics can be assembled with C ++ or Python and can be processed on CPUs or GPUs.

 

Features

  • Build and train ML models easily using intuitive high-level APIs like Keras
  • Easily train and deploy models on the cloud, in the browser, or on-device
  • Powerful experimentation for research

Applications

  • Speech Recognition Systems  
  • Image/Video Recognition and tagging  
  • Self Driving Cars  
  • Text Summarization  
  • Sentiment Analysis  

 

2. H2O

H2O is another open-source machine learning framework. It provides access to machine learning algorithms across common development environments (Python, Java, Scala, R), big data systems (Hadoop, Spark), and data sources (HDFS, S3, SQL, NoSQL). H2O is used as a comprehensive solution to collect data, build models, and serve predictions.  

H2O features ‘driverless AI’ and can function as a native Python library, or via a Jupyter Notebook or the R language in R Studio. This platform also includes an open-source, web-based environment called Flow, exclusive to H2O, allowing interaction with the dataset during the training process.

 

Features

  • Flexibility of Data and Deployment  
  • Automatic Model Documentation  
  • NVIDIA GPU Acceleration  
  • Automatic Data Visualization (Autovis)
  • Time Series Forecasting
  • Automatic Feature Engineering  

Applications

  • Advanced analytics
  • Fraud detection
  • Digital advertising
  • Claims management to save money

 

3. Apache SINGA 

Deep learning frameworks empower high-performance machine learning capabilities, such as natural language processing and image recognition. SINGA, a top-level project for developing an open-source machine learning library and facilitating the formation of deep learning models on large volumes of data.

SINGA provides a simple programming model for the formation of deep learning networks in a group of machines. It supports convulsive neural networks, restricted Boltzmann machines, and recurrent neural networks. SINGA also simplifies group setup with Apache Zookeeper.

Apache singa

Image – SINGA overview

Features

  • Enhanced ONNX 
  • Distributed training with MPI and NCCL Communication optimization through gradient sparsification and compression, and chunk transmission
  • Runs synchronous, asynchronous and hybrid training frameworks
  • Runs training in parallel by partitioning on batch dimension, feature dimension or hybrid partitioning
  • Computational graph construction and optimization for speed and memory using the graph

 

4. Amazon Machine Learning (Amazon ML) 

Amazon ML is a cloud-based service suitable for developers of all skill levels, enabling them to deploy machine learning technology. Its visualization tools and wizards guide through the entire process of creating machine learning models. You do not need to have knowledge of complex algorithms. After your models are ready. This tool makes it easy to obtain predictions for your application using simple APIs.

With Amazon ML, you can connect to data stored in Amazon S3, Redshift, or RDS. You can even run binary classification, multiclass categorization, or regression on that data to create a model. However, note that the resulting models cannot be imported or exported, and the data sets for the training models should not be larger than 100GB.

Features

  • Allows making behavioral classification and predictions
  • Trains and serves your models in the cloud, without having to set up the infrastructure
  • Defines a schema to describe different types of input during data uploading
  • Applies common transformations to data

Applications

  • Analyze and predict customer behavior
  • Recognize message content 
  • Predict quantities and intervals of customer service inquiries
  • Recognize and prevent fraudulent transactions
  • Personalize web services for customers
  • Conduct targeted marketing campaigns 
  • Classify documents

5. Microsoft Azure ML Studio

Given a large amount of data and computational power required to perform machine learning, Microsoft Azure ML Studio provides an ideal environment for ML applications. Machine Learning Studio is a GUI-based integrated development environment. It is used for constructing and operationalizing ML workflow on Azure.

Azure ML Studio allows users to create and train models, and then turn them into usable APIs. Free Tier users get up to 10GB of storage per account for model data. You can connect your own Azure storage to the service for larger models.  

 

Features

  • Predictive modelling  
  • Anomaly detection  
  • Intuitive graphical interface  
  • Support for R scripts  
  • Drag and drop technique for building experiments  
  • Valuable documentation  
  • Text analytic support  
  • High-performance computing  

Applications

Builds, tests, and generates advanced analytics based on the data.

6. Scikit-Learn

Scikit-Learn is a general-purpose open-source library for data analysis written in python. It is based on other Python libraries: NumPy, SciPy and Matplotlib. 

Scikit-learn contains a number of implementations for different popular machine learning algorithms. It handles both supervised and unsupervised learning. Scikit-learn’s wide variety of algorithms and utilities make it the basic tool to start programming and structuring data analysis as well as statistical modelling systems.

Scikit-Learn is licensed under a permissive simplified BSD license. This framework allows high speed working on multiple tasks. It comes with a clean API and is highly efficient for data mining. Scikit-Learn is an amazing option for building models.

Features

  • Classification, including K-Nearest Neighbors
  • Clustering, including K-Means and K-Means++
  • Model selection
  • Preprocessing, including Min-Max Normalization
  • Regression, including Linear and Logistic Regression

Applications

Scikit-Learn is distributed under many Linux distributions, encouraging academic and commercial use.

 

7. Apache Mahout

Mahout

 

 

 

 

Apache Mahout is an open-source deep learning platform that uses the MapReduce paradigm and runs on top of Apache Hadoop. It operates on a distributed linear algebra framework to scribe and implement ML algorithms. 

Mahout was originally built to enable scalable machine learning in Hadoop. After a long period of minimal activity, Mahout has new additions, such as a new environment for math, called Samsara, which allows algorithms to be run through a distributed Spark pool.

It is supported by CPU and GPU operations. The Mahout framework has many algorithms within its umbrella that are useful for standalone applications.

Features

  • Highly scalable
  • Collaborative Filtering
  • Clustering 
  • Classification 
  • Dimensionality Reduction
  • Matrix Factorization with ALS

Applications

  • Creating scalable machine learning algorithms
  • Interest modelling
  • Pattern mining

 

In A Nutshell

Building advanced analytics and ML-based solutions with these machine learning frameworks is more approachable and simple as they don’t require any expertise with algorithms. We hope this article helped you in identifying the most suitable machine learning framework for your organization.

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