Top Machine Learning Frameworks

Top Machine Learning Frameworks

5 mins read750 Views Comment
clickHere
Rashmi
Rashmi Karan
Manager - Content
Updated on Feb 26, 2024 14:35 IST

Machine learning frameworks streamline the creation and deployment of intelligent models. Mastering these tools equips you to tackle complex data challenges and unlock the potential of artificial intelligence in diverse applications. This blog will discuss some of the most important frameworks shaping the future of AI and machine learning.

ML FRAMEWORKS

What Are Machine Learning Frameworks?

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

Tensorflow

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

TensorFlow implements data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The data movements 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

H20

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

Applications

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

Apache SINGA

Deep learning frameworks empower high-performance machine learning capabilities, such as natural language processing and image recognition. SINGA is 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 forming 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.

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

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 the 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/coaching/training models should not be larger than 100GB.

Features

  • Allows for behavioural classification and predictions
  • Trains and serves models in the cloud without infrastructure setup
  • Defines schema for various input types during data upload
  • Supports common data transformations for preprocessing

Applications

  • Analyze and predict customer behaviour
  • 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

Microsoft Azure ML Studio

Given the 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 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 data.

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 various algorithms and utilities make it the basic tool to start programming and structuring data analysis and 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

Applications

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

Apache Mahout

Apache Mahout is an open-source deep learning platform that uses the MapReduce paradigm and runs on top of Apache Hadoop. It uses 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

Managing and utilizing big data is always a cumbersome task for enterprises, and this is where the role of machine learning comes in. Building advanced analytics solutions with these machine learning frameworks is more approachable and simple as they don’t require any expertise with algorithms. I hope this article helps you identify the most suitable machine-learning framework for your organization.

About the Author
author-image
Rashmi Karan
Manager - Content

Rashmi is a postgraduate in Biotechnology with a flair for research-oriented work and has an experience of over 13 years in content creation and social media handling. She has a diversified writing portfolio and aim... Read Full Bio