Top 10 Best Machine Learning Tools (Pros and Cons)

Top 10 Best machine learning tools (Pros and Cons). Machine Learning (ML)is the latest trend in the field of Computer Science and anyone with any inclination towards Data Science. Today’s environment requires modern tools to achieve goals in less time . Tools like automation, efficient handling of data, reliable and accessible tools will aid organizations in reaching their goals faster.


Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithm to make classifications or predictions, sooner and it is an important component of the growing field of data science.


Below is a list of Top 10 Best machine learning tools (Pros and Cons). These tools will assist you in developing, maintaining, securing and upgrading your IT infrastructure.

How machine learning works

  1. A Decision Process is based on input data, which can be marked or unmarked, the algorithm will produce an estimate  pattern in the data


2. An Error Function evaluates the prediction 

3. An Model Optimization Process where in time the model adjusts to reduce the discrepancy between the example already known and example model. The algorithm repeats this evaluation and improves the process until accuracy gets better. 

Top 10 Best Machine Learning Tools (Pros and Cons)

1. Microsoft Azure ML

First on the list in Top 10 Best machine learning tools (Pros and Cons) is Microsoft Azure. It is the main environment for dataset management, model training and deployment.

The platform provides a Machine Learning Studio, a web based and low code environment to quickly configure machine learning operations and pipelines.

Azure offers integration with Jupyter and it’s to write and run your code in ML Studio.

Microsoft Azure is a cloud assistance program used for boosting and controlling machine learning models. It allows users to deploy predictive statistics into the application effortlessly.

Pros of Microsoft Azure Machine Learning

  • Azure Machine Learning designer is a graphic drag and drop UI for ML studio that provides access and controls to the platform’s features.
  • Modular pipelines to construct a custom data.
  • Microsoft Azure is created for all types of users. It is easy to use because it supports the language R. 
  • It has no constraints regarding data importation from azure and provides the drag and drop facility to the users. 
  • As it is a cloud based tool, it renders efficient results in minimum processing time. 
  • It can debug errors from big data through consistent connections with HDInsight. 
  • Tt is reasonable, you don’t have to pay for unrequired hardware and software.

Cons of Microsoft Azure Machine Learning

  • Microsoft Azure has a huge disadvantage of confined storage provided in the free version. 
  • Data Transfer fees are high.
  • Complicated Pricing.
  • The constrained amount of algorithms and techniques impede users from creating their own model. 
  • It demands a seamless network connection while being operated. And azure does not support and execute Python codes.

2. IBM Watson Studio

IBM Watson is premier enterprise artificial intelligence solution that offers companies the capability to expedite their research and development. IBM Watson offers full range of tools and services so that you can build, train and deploy Machine Learning models.

IBM Watson provides tools such as AutoAI experiment builder, Notebooks provides an interactive programming environment and Deep Learning experiments that automates running hundreds of training runs while tracking and storing results. IBM Watson Studio provide tools  for code based and visual data science. Work in languages such as Python, Rails and Scala.


This learning machine tool simplifies AI lifecycle management and accelerates time with an open, flexible multicloud architecture.

Pros of IBM Watson

  • Industries prefer IBM Watson because of its capability of handling complex data and ability to recognize patterns via its advanced Artificial intelligence. 
  • Adaptive customer experiences and Leveraging Employee Intelligence.
  • GitHub integration
  • Support to run SQL queries on Cloud.
  • Collects insights from complex data that help in envisaging future interconnections (Chatbots) are incorporated in such way that a human cannot recognize whether it is a machine or a human interacting with them. 
  • Data security. The cybersecurity of IBM Watson keeps an eye on every activity and quickly debugs all the issues.
  • Handles enormous quantities of data.

Cons of IBM Watson

  • Barriers like stopping users from exploring tools such as confined languages, IBM Watson is based in English language only.
  • The deployment and processing of data take ample time. It is also not reliable and affordable to divert into Watson Commerce.
  • Seen as disruptive technology.
  • Doesn’t process structured data directly.
  • Takes time to integrate Watson and it’s services into a company.

3. TensorFlow

Google TensorFlow  is free and  opensource machine learning concept and offers a high level and abstract approach to organizing low level numerical programming. 

It is easily available, does not require programming and database expertise. TensorFlow eases the computations of the machine and deep learning. 

It does not support applications that are not related to google. The Node.js bindings provide a backend for TensorFlow.js 

TensorFlow is a publically accessible program that is specially used for instructing and analyzing a deep Neural network. Tensors are multidimensional arrays that are used to operate large data sets.

Pros of TensorFlow

  • Tensorflow is easily accessible to all the developers. 
  • Tensor board enables users to remove/alter any node, through this it allows rapid changes without going through the whole code. 
  • The good connection between TensorFlow and Keras reduces complexities while high-level coding. 
  •  It also supports many languages like C++, Python, Javascript, etc
  • Data visualization through graph visualizations.
  • Library management:
  • Debugging.

Cons of TensorFlow

  • The persistent release of updates disturbs the workflow because users have to constantly update their programs. 
  • Its architecture TPU doesn’t allow training the model. 
  •  It has limited support for GPU, only NVIDIA and Python support for GPU programming. 
  • It doesn’t support Windows Operating System.
  • Computation Tests.
  • Missing symbolic loops. 

4. Amazon SageMaker

Next on the list of Top 10 Best machine learning tools (Pros and Cons)is Amazon Sagemaker. It is a tool offered by Amazon Web Services that allows users to create, run, and establish a model at any level. Amazon is the hub of e commerce, artificial intelligence, Cloud computing and digital streaming. 


AWS SageMaker aims to turn those fragmented tools into a cohesive packaged product that allows companies to quickly, scalably and cost effectively train and monitor machine learning models.

Pros of AWS Sagemaker

  • Sagemaker renders efficient deployment through Sagemaker NEO. 
  • It is known for expediting end-to-end machine learning deployment. 
  • It contains a variety of tools, framework, and libraries that makes it more comprehensive and also enable users to host different models simultaneously. 
  • It doesn’t require coding while programming any use-cases.

Cons of AWS Sagemaker

  • Sagemaker is expensive and has an issue of poor documentation. 
  • Lack of Flexibility.
  • Takes time when executing a large data set. 
  • It has no capability on metrics logged whilst training, also users can not schedule training jobs.
  • Lack of Community.

5. Weka

Weka is developed by the University of Waikato, New Zealand. Weka Server is an acronym for Waikato Environment of Knowledge analysis and is open source data mining software. It does not only support machine learning algorithms, but also data preparation and meta learners like bagging and boosting. The aim was to create an interface that provides society with a diversity of machine learning techniques. Written in java, so it can be run on any platform

Pros of Weka

  • Whole range of data preparation, feature selection and data mining algorithms are integrated.
  • Weka is lightweight, the implementation of any algorithm can be more easily done.
  • It provides you with readymade code, you don’t have to write the whole code.
  • It contains a collection of already processed data and techniques.
  • It confers its users with online free courses that assist them in learning ML and Data mining.
  • It can be executed on any platform because it is based/implemented on the JAVA programming language.

Cons of Weka

  • It contains confined analysis options.
  • It does not implement the newest techniques.
  • Only small collections of data are managed by Weka, which causes OutofMemory error when a few megabytes are accumulated.
  • Lack of documentation and online support.

6. PyTorch

PyTorch is a free library, created by Facebook’s AI research lab (Meta AI). PyTorch mainly used in computer vision, deep learning, and natural language processing applications. PyTorch allows users to develop a compound neural network by implementing PyTorch as a core data structure. For machine learning and Artificial Intelligence enthusiast, PyTorch is easy to learn and will be very useful to build models.

Pros of PyTorch

  • Pytorch is understandable because its patterns are similar to the native language and the documentation is also done nicely. 
  • It supports cloud platforms.
  • It can be effortlessly deployed in Windows. 
  • Rich set of powerful APIs to extend the Pytorch Libraries.
  • It is efficient in processing complex data and renders abundant libraries to the users. 
  • It has access to PDB and IPDB tools to detect and delete errors. 
  • Pytorch allows multiple executions of several tasks concurrently by transmitting it among GPU and CPUs.

Cons of PyTorch

  • Due to minimum users; maintenance is lacking. 
  • The visualization of PyTorch is not up to mark.
  • Absence of monitoring and visualization tools.
  • The developer community is small.

7. Apache Mahout

Another choice for Top 10 Best machine learning tools (Pros and Cons)is Apache Mahout which was founded by Apache Software. Apache  Mahout helps mathematicians, statisticians and data scientists for executing their algorithms. has bestowed its users with a variety of open-source software projects, is free of cost, and enables you to add more features. But on the other hand, it takes immense time while debugging.

Mahout provides prefabricated algorithms and frameworks to the users that are used in resolving machine learning problems and developing upgradable machine learning algorithms.

Pros of Mahout

  • Apache Mahout contains a Hadoop framework which is non-proprietary software that makes the execution of large data unchallenging. 
  • It provides algorithms for Pre-processors, Regression, Clustering, Recommenders, and Distributed Linear Algebra.
  • It delivers readymade frameworks to the user for the extraction of big data. 
  • Java libraries are included for common math operations.
  • It has the ability to deal with future updates.

Cons of Mahout

  • Apache Mahout is inefficient compared to other frameworks. 
  • Some algorithms are missing.
  • It doesn’t embrace many scientific extrapolations. Furthermore, there is a shortage of enterprise support and storage room.

8. Colab

Colab is also a product of Google so as Tensorflow. Google Colaboratory (Colab) is a free Jupyter Notebook environment allowing you to do machine learning (ML) work in a notebook environment. One of the best things about Colab is that you don’t need to install anything beforehand.  It is free, easily accessible, does not require programming and database expertise. Due to lack of customization, it is not a suitable website for business purposes.

Colab; an online/free cloud-based platform is associated with Jupyter notebook. It enables users to create and run Python in the browser. In addition, it provides access to GPU.

Pros of Colab

  • Pre installed libraries.
  • Data versatility. With the assistance of Python, Colab makes the computation of millions of rows in a spreadsheet more convenient and reduces the time of collecting and running monotonous data.
  • You can run TensorFlow programs in google Colab.
  • Seamless Collaboration and Access (Google docs, Google Slides, Google Sheets).
  • It is easy to share and can be easily saved in Github. 
  •  Code snippets as an alternative to the whole code. Beginners can easily make changes in the code all thanks to Colab’s comment feature.

Cons of Colab

  • Users can not write and run their own code in google Colab. 
  • Colab does not provide liveediting and persistent storage to its users.
  • It has only 15GB of available storage.

9. Scikit-Learn

Scikit-Learn is a free software machine learning library for the Python programming language . It features simple and efficient tools for data mining and data analysis. It has various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN so scikit-learn is not the best choice for deep learning.It is mainly used in analysis and commercials programs. Furthermore, it has a vast quantity of algorithms such as regression, classification, clustering, etc.

Pros of Scikit-Learn

  • Highly supervised machine learning algorithms are mostly part of scikit-learn.
  • Easy to use, supports most practical tasks.
  • It provides a rigid accuracy rate of any data. 
  • Parameters for any specific algorithm can be changed while calling objects.
  • It helps in data mining and data analysis.
  • It enables users to combine any algorithm with their platform via API documentation.

Cons of Scikit-Learn

  • It is inapplicable to deep learning


Last on the list of top 10 best machine learning tools is KNIME. It is an open source and non proprietary platform that develops comprehensive data and structural data science workflows. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. It uses the power of the Eclipse platform and a set of other powerful extensions designed for machine learning and data mining to facilitate users in discovering potential hidden data, predict new rules, and mine fresh information. Knime has powerful analytics, local automation and workflow difference.

Pros of KNIME

  • KNIME doesn’t require coding for running data. 
  • Big Data extensions.
  • Data blending.
  • It is comprehensible because it is interlinked to a common language like Python and R for tailoring. 
  • It provides solutions/sample workflow of any problem. Moreover, it is license-free.

Cons of KNIME

  • It has no association with the Jupyter Notebook. 
  • It has confined features for tools. And allows limited sharing of domains with KNIME users.

Top 10 best machine learning tools and their pros and cons Conclusion

In this article, we have explored machine learning an their pros and cons. Selection of the tool depends on your requirement for the algorithm, your expertise level, and the price of the tool.


Machine learning library should be easy to use. Most of these libraries are free to use. TensorFlow is more popular in machine learning, but it has a learning curve. Scikit-learn and PyTorch are also popular tools for machine learning and both support Python programming language. Also TensorFlow are good for neural networks.


These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics.

Most of the tools mentioned above are free and easily accessible while some are paid yet come with more premium features. And all of the tools are mainly used for the same purpose, however, users should be aware of their target and the path they want to go on. Because each and every tool has its own features and accessibilities.

Hope this article will enhance your knowledge and assist in selecting the best tool for your program.

Avatar for Emad Bin Abid
Emad Bin Abid

I'm a software engineer who has a bright vision and a strong interest in designing and engineering software solutions. I readily understand that in today's agile world the development process has to be rapid, reusable, and scalable; hence it is extremely important to develop solutions that are well-designed and embody a well-thought-of architecture as the baseline. Apart from designing and developing business solutions, I'm a content writer who loves to document technical learnings and experiences so that peers in the same industry can also benefit from them.

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