Top 5 Machine Learning Frameworks You Must Try in 2021

machine learning frameworks

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 5 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.


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 different types of ML frameworks 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 ML 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 software library for data-based programming across multiple tasks. 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 a 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 into 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 to 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 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 startups 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.


    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.


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,, 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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Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, 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.

This page was last edited on December 3rd, 2020, at 10:31.

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.


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