Best 7 Machine Learning Frameworks for 2020

Artificial Intelligence, Internet of Things, News

Nowadays, Machine Learning is one of the fastest-growing technologies globally. Applications and projects based on machine learning are gaining popularity in the tech industry. Most of the big companies are moving to machine learning to automate their operations, optimize the process, and most importantly, to be ahead in the competition. In 2020 many machine learning frameworks come to light. But it is hard to choose which framework fits project needs.

 Well, here, we will discuss the best machine learning frameworks, along with the pros & cons of each framework. But first, let’s understand what machine learning is all about?

What is Machine Learning?

 Machine learning is a sub-part or subset of Artificial Intelligence that allows an application to examine the data, then make decisions based on that without any human interaction. It uses some algorithms to study data. Nowadays, machine learning is used mostly in every sector like; government, healthcare sector, transportation, travel, real estate sector, retail industries, and many more. Machine learning is of 3 types; Supervised, Unsupervised, and Reinforcement Learning.

  • Supervised Learning – Supervised learning uses labeled data for training. Labeled data helps to predict the result of unpredicted data. This method helps to store and produce an output of data from the previous result.
  • Unsupervised Learning – Unsupervised learning uses unlabelled data. Hidden data. This method helps to find all unknown patterns, features of data.
  • Reinforcement Learning – this learning determines what to do with a given assignment or task. Reinforcement learning learns from its experience because of the scarcity of datasets.

Best 7 Machine Learning Frameworks

1. Tensor Flow
2. Apache Mahout
3. Sci-Kit Learns
4. PyTorch
5. Google Cloud ML Engine
6. H2O
7.Microsoft Cognitive Tool

1. Tensor Flow

Tensor Flow

Developed by Google, Tensorflow is an end-to-end open-source machine learning platform. It offers comprehensive, flexible tools and libraries that make it easier for the researchers to push information to the ML models and developers to build and deploy applications. 

Features of Tensorflow includes:

  • Easy Model Building: Leverage high-level APIs like Keras with eager execution to stimulate quick model iteration and easy debugging. 
  • Robust ML Production Anywhere: Tensorflow makes it easier to train and deploy models in the cloud, on-premise, in browser or in the device regardless of the language.
  • Powerful Experimentation for Research: Facilitates a simple architecture that makes it easier to conceptualize and code them as well as publish faster. 

Pros of Tensor flow:

  • Easier to use, with a lot of tutorials to get users on board.
  • Tooling around TensorFlow such as TensorBoard is very easy.
  • Backed by strong community support
  • Provides robust experimentation for researching
  • Flexible

Cons of TensorFlow:

  • Not so suitable for beginners, hard to fix bugs.
  • Graphic interface to create layers can help beginners.
  • Detailed tutorials on what goes behind the scenes in each layer. Currently, the tutorials don’t focus on that.
  • Better support to integrate with files on the cloud.

2. Apache Mahout

Apache Mahout

Apache Mahout is a robust, scalable machine learning library that runs on Hadoop MapReduce. It is popularly known as a distributed linear algebra framework and mathematically expressive Scala DSL, specifically tailored for mathematicians, statisticians, and data scientists to quickly implement their own algorithms.

Features of Apache Mahout  include:

  • As Mahout algorithms are written on the top of Hadoop, they operate well in a distributed environment. Therefore, it uses the Apache Hadoop Library to scale effectively in the cloud. 
  • It offers an easy to use framework that makes data mining for large chunks of data easier.
  • Data analyses, even for large data sets, can be done effectively and quickly. 
  • Include many MapRudece enabled clustering implementations such as k-means, fuzzy k-means, Canopy, Dirichlet, and Mean-Shift.
  • Supports distributed Naive Bayes and Complementary Naive Bayes classification implementations.
  • Comes with distributed fitness function capabilities for evolutionary programming.
  • Includes matrix and vector libraries.

Pros of using Apache Mahout:

  • Offers the coder a read-to-use framework for mining a large amount of data
  • Makes analysis of large data sets effective and quick.
  • Comes with distributed fitness function capabilities for evolutionary programming.

Cons of Apache Mahout:

  • Poor visualization and less support for scientific libraries.

3. Sci-Kit Learns

Sci-Kit Learns

Scikit-learn is a free-to-use machine learning module built on SciPy to be used with Python. It is a simple but effective data mining and data analysis tool. Released under the BSD license, it can be utilized for both commercial and personal purposes. Through scikit-learn, users can conduct various types of tasks such as preprocessing, clustering, model selection, and a lot more. This module is equipped for completing implementations as well. Furthermore, scikit-learn can fulfill a wide variety of purposes such as hotel bookings, music streaming, and others. Thanks to this, users can incorporate algorithms in the system to their very own application.

Pros of Sci-kit Learns:

  • Supervised learning algorithms: Any supervised Machine Learning algorithm that you may have heard of has a very high possibility of belonging to the scikit-learn library. The scikit-learn toolkit has a repertoire of such supervised learning algorithms, which includes – Generalized linear models such as Linear regression, Decision Trees, Support Vector Machines, and Bayesian methods. 
  • Unsupervised learning algorithms: This algorithm collection includes factoring, cluster analysis, principal component analysis, and unsupervised neural networks.
  • Feature extraction: Using scikit-learn, you can extract features from text and images.
  • Cross-validation: The accuracy and validity of supervised models on unseen data can be checked with the help of scikit-learn.
  • Dimensionality Reduction: With this feature, the number of attributes in data can be reduced for subsequent visualization, summarization, and feature selection.
  • Clustering: This feature allows the grouping of unlabeled data.
  • Ensemble methods: The predictions of several supervised models can be combined by using this feature.

Cons of Scikit-learn:

  • Not suitable for deep learning.-

4.  PyTorch


PyTorch’s creators say that they have a philosophy – they want to be imperative. This means that we run our computation immediately. This fits right into the python programming methodology, as we don’t have to wait for the whole code to be written before getting to know if it works or not. We can easily run a part of the code and inspect it in real-time. For me as a neural network debugger, this is a blessing!

PyTorch is a python based library built to provide flexibility as a deep learning development platform. The workflow of PyTorch is as close as you can get to python’s scientific computing library – NumPynumpy.

This framework is developed by Facebook, which is an open-source and scientific computing framework. Pytorch is a scripting language that is based on the LUA programming language. Pytorch offers various range of algorithms and scripting languages.

Features of PyTorch include:

  • Its API is very simple to use.
  • PyTorch uses python integrations coupled with a data science stack.
  • It helps in building computational graphs whenever you want and in a simple way.
  • You can change your graph if you want to use PyTorch even at runtime.
  • It offers a simple interface with APIs. Operations and execution are similar to Python.
  • It leverages services and operations provided by Python.

Pros of PyTorch:

  •  Flexible
  • Easily customizable
  • Used to design neural networks
  • Better than tension flow

Cons of PyTorch:

  • Complex
  • Required lots of coding

5. Google Cloud ML Engine

Google Cloud ML Engine

Google Cloud Machine Learning (ML) Engine is a managed service that allows developers and data scientists to build and deploy cutting-edge machine learning models. It offers training and prediction services that can be either used individually or in collaboration. It is a leading machine learning framework choice used by enterprises to overcome challenges such as identifying clouds in satellite images, food safety, and improving time to respond to customer emails at least four times faster. 

Features of Cloud ML includes:

  • HyperTune
  • Cloud Datalab
  • Portable Models
  • Prediction Services
  • TensorFlow Framework
  • Machine Learning Samples Sharing

Pros of Google Cloud ML Engine:

  • Huge community
  • A rich documentation
  • Easy to integrate with other software
  • Free and open-source
  • Powerful features
  • Constantly getting better with community contributions.

Cons of Google Cloud ML Engine:

  • The learning curve is a little bit steep, however, there are many free resources on the Internet.
  • The product is getting lots of changes and contributions from the community, so it’s hard to follow what is going on with the product all the time

6. H2O


AutoML or Automatic Machine Learning is the process of automating algorithm selection, feature generation, hyperparameter tuning, iterative modeling, and model assessment. AutoML make it easy to train and evaluate machine learning models. Automating repetitive tasks allows people to focus on the data and the business problems they are trying to solve.

Pros of H2O:

  • Excellent analytical and prediction tool
  • In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
  • Readily available algorithms, easy to use in your analytical projects
  • Faster than Python scikit learn (in machine learning supervised learning area)
  • It can be accessed (run) from Python, not only JAVA, etc.
  • Well documented and suitable for fast training or self-studying
  • In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
  • It can be used as open-source

Cons of H2O:

This is not really a drawback, but rather a warning – the Driverless AI is not a replacement for a data scientist yet, and will not replace data scientists in the next decade neither. The Driverless AI feature delivers reliable results only if the analyst is sure about the meaning of input data. The data quality is usually a major issue and no tool can detect the meaning of data in the input. Data scientists are also required for the business interpretation of the findings. So be careful, and do not rely on this feature without a good understanding of what it really does in each step.

 7. Microsoft Cognitive Tool

 Microsoft Cognitive Tool

Microsoft Cognitive Services are a purpose-built collection of AI algorithms and application programming interfaces (APIs) to help developers add AI capabilities to websites, apps, and AI agents. Cognitive Services allow enterprises lacking the necessary AI research staff, infrastructure and budget to select AI tools for their needs. Users can choose from a library of Microsoft’s AI algorithms for Cognitive Services on Azure, Microsoft’s public cloud computing platform. Like most major cloud service providers, Microsoft has become a cloud-based provider of AI services. Cognitive Services represent one of the largest suites of AI capabilities and include deep learning capabilities, machine vision, and sentiment evaluation.

Most tools included in Cognitive Services are compatible with Microsoft’s natural language understanding (NLU) AI chatbot. Offered as Azure Bot Service, the chatbot is positioned as a tool to take service and reception calls and moderate websites and forums.

  1.     Pros – It deals with multi-dimensional and large-scale data sets, uses C++ and python.
  2.     Cons – Slightly slower than other machine learning frameworks.

Wrapping up

 With this, we have come to the end of our best Machine Learning Frameworks list. Now you have enough options to select the right framework for your Machine learning app development. If you started using Machine learning for the first time in 2020, then it is the best starting of your projects. For more assistance, feel free to reach out to us. We, at Matellio, offer the best solutions for Machine Learning at an affordable price.