Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to automatically learn from user interactions without explicit instructions from the users. It relies on patterns and inferences to provide systems with the ability to automatically learn and improve from experience. ML engineers should be well-versed in computer science fundamentals, programming skills, data structures, computability and complexity in order to design Machine Learning Frameworks that perform specific tasks automatically. As per the machine learning framework definition, it is an interface, library or tool, which enables the ML engineers to build models easily without getting into the depth of the underlying algorithms. It provides a collection of pre-built, optimized components that can be used to concisely and speedily build machine learning models. Efficient machine learning tools reduce the complexity of ML quite drastically, making these accessible to more developers. 

Some of the key features of a good machine learning frameworks are:

  • Optimal performance
  • Developer friendly
  • Easy to understand and code
  • Parallelization to distribute the computation process
  • Steady user-base and support community

Commonly Used Machine Learning Frameworks

1. TensorFlow

Developed by Google, TensorFlow is an open-source machine learning framework that offers multiple models and versions that can be used simultaneously, for research and development purposes. It is a very flexible and portable framework; therefore, it can be run on GPU, CPU, servers, desktops and mobile devices. TensorFlow has complicated functions and paradigms and supports regressions, classifications and neural networks. It is mainly used for numerical computations using data flow graphs where batches of data or ‘tensors’ is processed by a series of algorithms described by a graph.

2. Spark MLlib

Spark is a cluster computing framework, written mainly in Java, Scala, R and Python. Apache Spark’s machine learning library, Spark MLlib, makes machine learning applications easy and scalable. MLlib contains learning algorithms and utilities, which include clustering, regression, collaborative filtering, classification, dimensionality reduction, low-level optimization primitives and high-level pipeline APIs. Learn more about MLlib in this guide.

3. H2O

H2O is a business-oriented open source machine learning tool that helps users in decision-making by drawing insights from data. It combines unique features like Best of Breed Open Source Technology, Data Agnostic Support for all common database and file types and easy-to-use Web UI. H2O allows users to continue using their existing machine learning applications. It can also be seamlessly extended onto Hadoop environments. It is mainly used in predictive modelling, risk and fraud analysis, advertising technology, customer intelligence, healthcare and insurance analytics.

4. Microsoft CNTK

CNTK is the ML framework developed by Microsoft for deep learning and training algorithms. CNTK or Cognitive Tool Kit is used to create machine learning models that can ‘learn’ like the human brain. Recurrent neural networks and convolution neural networks are two instances of CNTK applications. With rapid training time and intuitive architecture, it allows neural networks to go through huge amounts of unstructured data. It supports multi-machine and multi-GPU back ends and can be customized as per your requirements.

5. Sci-Kit Learn

Sci-Kit Learn is a ML framework by Python and is very popular among developers. Built on other libraries like SciPy, Numpy and Matplotlib, Sci-Kit are a highly efficient machine learning tool in classification, regression and clustering. It includes supervised and unsupervised learning algorithms as well as cross-validations. It is very useful in data mining operations like bunching, relapse and order.

6. Theano

Theano is a Python machine learning framework that lets you define, optimize and evaluate mathematical expressions. The most arresting feature of this tool is its high level of speed in processing chunks of data, especially when compared to hand-crafted C implementations. Theano can be executed on GPUs and CPUs, given the basic structures.

7. Caffe

Convolutional Architecture for Fast Feature Embedding or CAFFE is a machine learning framework developed in CPP. It is a very popular machine learning application in academic research projects and start-up prototypes. It has MatLab as well as a Python interface and allows switching between CPU and GPU. CAFFE is very useful in processing images and is known to process 60M images per day with a single GPU.

8. Amazon Machine Learning

Amazon Machine Learning is a visualization tool that allows you to create machine learning models without having to gain the knowledge of complex ML algorithms. Developers of all skill levels can use this tool to learn and develop ML models. It can be used in binary classification, multi class categorization as well as regression. This machine learning application is connected to the data stored in Amazon S3, RedShift and RDS. You can learn more about these applications in this blog.

9. Torch

Torch is an ML framework and is very easy to use because of the fast scripting language, LuaJIT and the underlying C/CUDA implementation. The best advantage of this tool is its maximum flexibility and speed while implementing scientific algorithms in a relatively simple process. 

10. Azure ML Studio

This machine learning framework is used by Microsoft Azure users to create and ‘train’ models. These models are then turned into APIs which can be used by other services. Users are allowed up to 10 GB of storage per account for model data. There is also a provision to connect your own Azure storage to the ML Studio for larger data models. A lot of algorithms are available in the library courtesy of Microsoft and other third parties. You also do not need an account to try out the service; an anonymous login can give you up to 8 hours of access to the Studio.

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