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Companies are relying on machine learning for their main and ancillary applications. Tens of hundreds of open jobs requiring Tensorflow and other Machine Learning experience are a useful way to quantify how prevalent machine learning is becoming in business today. Without overstepping the mark, let us drip-feed each machine learning framework and understand the autonomous analytics that AI has to offer in real-time.
Companies that have embraced the AI tech extensively offer a suite of synthetic data and vision API’s to help businesses across different industries train their machine learning algorithms and improve their AI accuracy and repeatability. Key industries to be benefitted by such solutions include agriculture, industrial – including managing construction sites, smart cities, smart homes, e-commerce, FinTech, AdTech, Telco, Gaming, including Microsoft, Lyft, Waze, and King.
How does Machine Learning work?
Machine learning gives the computers the ability to learn without being explicitly programmed, giving you greater control over their business with a self-service AI platform that runs continuously to eliminate blind spots – examples include Image processing, medical diagnosis, prediction, classification, learning association, regression etc.
Some familiar machine learning frameworks include PyTorch, TensorFlow, Scikit-learn, ONNX format, FireBase ML Kit, CAFFE (Convolutional Architecture for Fast Feature Embedding), Apache Spark and likewise. The development tools that best meet your requirements including popular IDE’s, Jupyter notebooks and CLI’s or programming languages such as Python, PERL, RoR must be used.
Machine Learning Frameworks to Consider in 2020
TensorFlow
- Tensor Flow is an open-source learning framework maintained by Google.
- Applications like Gmail, Google Photos, Speech Recognition
- This framework can potentially be used to perform complicated research on ML and Deep Learning.
- Python, JavaScript, C#, C++, Java, Go, Haskell, MATLAB, Ruby, Rust, Julia, Scala programming languages can be used to code TensorFlow.
- Tensor Flow has extensive documentation.
Scikit-Learn
- Scikit-Learn is open-source and free to use and easy to learn.
- It has extensive documentation.
- The library is built upon SciPy (Scientific Python) library and the framework includes NumPy, Matplotlib, IPython, Sympy and Pandas libraries.
- Scikit-Learn is backed by the international community hence the framework update, bug fixes, and new features are released regularly.
- This machine learning framework provides a range of supervised unsupervised learning algorithms.
PyTorch
- PyTorch is easy to learn, open-source and free to use and can be deeply integrated into Python.
- It is mostly used in Natural Language Processing (NLP).
- Python has maximum flexibility and speed and can as well act as a replacement of Tensor Flow.
- It supports dynamic neural networks.
- PyTorch is well documented.
Apache MXNet
- Apache MXNet has support for multiple major programming languages like Python, C++, R, Julia, Perl, Scala, Closure, and Java.
- It is portable, lightweight and can easily scale to make use of multiple CPUs on multiple machines.
- Apache MXNet is designed to be efficient, flexible and enhance productivity. Amazon uses it for Deep Learning Web Services.
- This model can be trained and build on cloud and can be easily deployed in Python and similar supporting languages.
- Packages and toolkits built around MXNet are available to extend the functionalities.
Microsoft Azure ML
- Microsoft Azure Machine Learning is a cloud-based predictive analytics service that comes with a browser-based tool that provides a very easy, drag and drop interface called Azure Machine Learning Studio (ML Studio) for building machine learning models.
- This model can be easily deployed as a Web Service that can be consumed by any programming language of your choice.
- The Azure platform can be exposed as a Web Service and can be used on any device of choice which is independent of programming language.
- The machine learning model can be easily customized using R or Python which have built-in support within the framework.
Keras
- This open-source, nifty tool has the potential to run upon TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML.
- It can process massive volumes of data while accelerating the training time for models.
- It is written in Python and is incredibly easy to handle, easy to use and extensible.
- Keras promotes fast experimentation with deep neural networks.
- It helps to write readable and precise code.
Sonnet
Sonnet Model is explicitly designed to work with TensorFlow and can be integrated with raw TF code and also those written in other high-level libraries.
Gluon
Gluon offers a straightforward and concise API for defining ML/DL models by using an assortment of pre-built and optimized neural network components. Gluon comes with a complete range of plug-and-play neural network building blocks, including predefined layers, initializers and optimizers which help to eliminate many of the underlying complicated implementation details. It allows users to define neural networks using simple, clear, and concise code. It is flexible, and straight to experiment and prototype.
DL4J
DL4J is powered by its unique open-source numerical computing library, ND4J. It provides the flexibility that lets users combine sequence-to-sequence autoencoders, variational autoencoders, recurrent nets and convolutional nets as required in a distributed, production-grade framework that works with Spark and Hadoop.
ONNX
ONNX helps in switching between different ML frameworks such as PyTorch and Caffe2. This framework allows the users to develop in their preferred framework with the chosen inference engine, without worrying about downstream inferencing implications.
Chainer
Chainer is a deep learning framework written in Python on top of NumPy and CuPy libraries. It is the first Deep Learning framework to introduce the define-by-run approach. It is highly intuitive and flexible and offers ease of debugging.
Let’s Proclaim the Machine Learning Frameworks
Machine learning has defined the shape of the modern technology scenario, with AI on the surface. From simple mobile application to B2B e-commerce applications to complex DNA mapping, machine learning makes all of this possible by making use of a set of libraries or a set of tools that allows a user to quickly build models without worrying about the underlying complexities. ML frameworks have been operationalizing Voice-of-Customer insights across organizations in real-time to enable greater customer-centric decisions and direction. The overall goal is to bring greater efficiency, transparency, and reproducibility into AI and ML development. Discuss with us for AI-built-in apps!