9 Best Machine Learning Frameworks to Build a Successful App

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated the top 9 machine learning frameworks that are cutting edge technology in your hands.

Through the machine learning platforms, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

Introduction

Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.

  • The framework should be performance-oriented
  • The grasping and coding should be quick
  • It allows to distribute of the computational process, the framework must have parallelization
  • It should consist of a facility to create models and provide a developer-friendly tool

Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project requirements. Before we dive deeper into these mentioned frameworks, know the best framework for machine learning that are available on the web. Here are some ML frameworks:

  • Mathematical oriented
  • Neural networks-based
  • Linear algebra tools
  • Statistical tools

Now, let’s have an insight into top machine learning frameworks that will help you in selecting the right framework for your ML application.

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Best Machine Learning Frameworks

  1. TensorFlow

    TensorFlow is an open-source ML framework and software library for data-based programming across multiple tasks. It is one of the most popular machine learning frameworks for transmitting complex parallel computations and for developing advanced neural network models. The framework is based on computational graphs which is essentially a network of codes.

    Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.

    TensorFlow work frame

    This is one of the best machine learning libraries that demands additional efforts while learning the TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.

    The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.

  2. FireBase ML Kit

    Firebase machine learning framework is the best ML framework library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud. 

    Firebase ML Kit

    This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.

    • The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
    • The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
    • The last one is a full glass recognition image.

    Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.

  3. CAFFE (Convolutional Architecture for Fast Feature Embedding)

    CAFFE (Convolutional Architecture for Fast Feature Embedding) framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model building that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.

    Caffe work frame

    This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of the Mat lab and python interface.

    Now, if you have to make a machine learning app development, then Caffe 2 is mainly used in academic research projects and to design startup prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.

  4. Apache Spark

    The Apache Spark machine learning is a cluster-computing framework written in different programming languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.  

    Apache Spark

    The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that Apache Hadoop allows data scientists to solve multiple data problems in addition to their machine learning problems.

    It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving data science problems instead of learning and maintaining a different tool for each scenario.

  5. Scikit-Learn

    Scikit-learn is said to be one of the greatest feats of the Python research community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.

    Scikit-Learn

    This is one of the best machine learning tools that can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.

    With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data structures, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.

  6. PyTorch

    Developed by FAIR (Facebook AI Research) in early 2018, PyTorch is another popular machine learning framework. The core team of FAIR has assembled CAFFE2 (another ML team) into Pytorch, they are another competitor of Tensorflow. When deciding to use deep learning frameworks, generally engineers stumble upon the one question, that “Do we choose PyTorch or Tensorflow?”

    Along with serving their purposes, Tensorflow and PyTorch are interchangeable. Similar to TensorFlow, PyTorch can also perform the following tasks.

    • It can perform classification, regression, and neural networks, etc.
    • It can run on GPU and CPU.

    PyTorch is considered pythonic (efficient and adjusts easily to its syntax and possibilities). Whereas, Tensorflow can get up and running with a few customizations.

    Due to the following traditional approach of an object-oriented programming approach for creating classes, PyTorch is highly customizable. The model training time of PyTorch is significantly faster. It may sound marginal for small projects, however, it can make a huge difference in larger projects.

    Similar to Torch (scientific computing framework), PyTorch is based on Python and doesn’t need Lua or Lua Package Manager. Should we look at the PyTorch model?

    PyTorch Model - 1

    PyTorch Model - 2

    PyTorch Model - 3

  7. Keras

    Keras is one of the deep learning frameworks which is built on the top level of TensorFlow, Microsoft cognitive toolkit, and PlaidML. The speed is the USP of the Keras framework. Keras comes with built-in support of Data parallelism. Therefore, Keras has the ability to process large amounts of data while speeding up the training time.

    Keras

    Undoubtedly, Keras is excellent while performing high-level computations, however, when it comes to performing low-level computations, it does not perform that great.

    However, Keras is easy to learn for beginners who are starting their journey in a deep learning framework. Keras helps to write exact code and permits coders to experiment with deep neural networks.

  8. MXNet

    MXNet is one of the most highly scalable and open-source deep learning frameworks. This framework supports many languages, like Javascript, R, C++, Python, and Julia. It is built to train and deploy deep neural networks. Due to faster computing, it improves fast model training.

    Let us discuss some of the main advantages of MXNet.

    It is flexible, fast, and efficient when it comes to deep learning algorithms.

    • MXNet supports multiple GPU modes and GPU support.
    • It supports all the device types.
    • It offers rich support for multiple languages like C++, JavaScript, Scala, Python, and R.

    MXnet is a great framework to use in large projects. Since it is new, you might not receive the support when you require it.

  9. Sonnet

    Sonnet is developed by DeepMind. It is a high-level library, which is designed to build complex neural network structures in TensorFlow. This deep learning framework is set up on top of TensorFlow. With Sonnet, it is possible to develop Python objects that are connected with a particular part of a neural network.

    Sonnet

    The computational TensorFlow graph connects all of these objects independently. To make the high-level architectures easier, the process creates independent Python objects and links them to graphs.

    Additionally, Sonnet is built around – “snt.Module ” is a powerful model developed around a single concept. It is self-contained modules but decouples from each other whenever necessary.

    Sonnet lets you write modules that declare other submodules internally or transfer modules being the construction process.

    Sonnet is specifically developed to work with TensorFlow, it will be easier for you to access its underlying details, which include variable_scopes and Tensors.

    Sonnet models can be used with both raw TF code and other high-level libraries.

Machine Learning Models & Algorithms

Machine learning comprises the use of machine learning algorithms and models. Before we discuss the models, let us understand the difference between machine learning algorithms and models.

The basic difference between machine learning algorithms is the learning procedure that functions on data to create a machine learning model. The model is the final result of a data-driven machine learning process.

We have mentioned the top 3 machine learning algorithms you can choose for your next project.

  1. Supervised Learning
  2. The machine will be under supervision while it is in the learning phase. This type of learning is known as supervised learning.

  3. Unsupervised Learning
  4. In unsupervised learning, the user will not help a computer to learn. Without labeled training sets, the machine will find the data which humans can not find. Hence, unsupervised learning is useful to identify patterns in the data to help us make the right decision.

  5. Reinforcement Learning
  6. This learning type closely matches how we humans learn. As we humans learn by interacting with the information or tools, in reinforcement learning the agents and algorithms retain information by interacting with its environment.

Frequently Asked Questions

What is a Machine Learning framework?

An ML framework is a tool, library, or interface, allowing ml developers and data scientists to create Machine Learning models quickly and with ease.

What are the tools used for Machine Learning?

A few of the most common tools used for the Machine Learning platform include Knime, Accord.net, Weka, and RapidMiner.

Is TensorFlow written in Python programming language?

TensorFlow is Google’s open-source AI framework for machine learning and high-performance numerical computation. It is written in three languages such as Python, C++, CUDA. We hope that we have answered your question of what is Tensorflow.

Which are the best deep learning framework?

A few deep learning framework examples would include TensorFlow, PyTorch, Keras, and Sonnet. Apart from these, there are other deep learning popular frameworks also available, but these four are highly popular in terms of features.

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Conclusion

Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, cloud based software development using ML, ML algorithms from scratch, how this technology is helpful for your business, and libraries for machine learning? Just fill our contact us form. We, as a leading mobile app development agency, will get back to you shortly and resolve your queries.

Author Bio
Jigar Mistry
Jigar Mistry
Designation: Vice President (Technologies)

Jigar Mistry is Vice President (Technologies) at Space-O Technologies. He has 14+ years of experience in the web and mobile app development industry. He has expertise in different mobile app categories like health and fitness, eCommerce, and on-demand.