We highlight the 5 best machine learning tools for businesses and explain how they can be used to generate business value. Don't miss it!

As machine learning becomes more popular and its use becomes more widespread, software vendors are expanding their offerings of machine learning platforms and tools with more advanced and easier-to-use capabilities. 


As we already mentioned in the article 'How Do Machine Learning Algorithms Work?', machine learning is positioning itself as a cutting-edge technology in the business world. However, working with machine learning algorithms is complex and usually requires specialised experts. Because of this, more and more software providers are expanding their offer of machine learning tools, platforms and systems specially designed for businesses. This type of technologies allow that even users with no experience in data science can work with machine learning algorithms.

Would you like to take advantage of machine learning and artificial intelligence, but don't know where to start?

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A continuación, repasamos algunas de las mejores plataformas de machine learning para empresas y usuarios no expertos en ciencia de datos. Estas herramientas automatizan todo el flujo de trabajo del machine learning, desde la preparación de los conjuntos de datos hasta el entrenamiento del modelo, la evaluación y el despliegue de la producción.


Below, we review some of the best machine learning tools for businesses and non-data science users. These tools automate the entire machine learning workflow, from dataset preparation to model training, evaluation and production deployment.


1. Azure Machine Learning

Azure Machine Learning (Azure ML) forma parte de la arquitectura integral de herramientas de Big Data de Microsoft y admite algoritmos clásicos de machine learning supervisado y no supervisado, así como algoritmos de deep learning.

Azure Machine Learning (Azure ML) is part of Microsoft's holistic architecture of Big Data tools. It supports classic supervised and unsupervised machine learning algorithms, as well as deep learning algorithms.

The tool allows coding in Python and R as well as working with machine learning models in other programming languages using SDKs and even working with little or no code using Azure ML Studio. In addition, the tool's environment encourages collaborative work, can be easily integrated and allows users to effortlessly build, train and track machine learning and deep learning models.

Azure Machine Learning can be integrated with other frameworks such as TensorFlow, PyTorch or Scikit-learn, so that the models developed in these frameworks are imported into Azure ML without the need to modify the code.

2. Scikit-learn (Python)

Scikit-learn is the most popular Python machine learning package due to its simplicity and variety of usability. It supports the most common machine learning algorithms such as decision trees, linear regression, random forest, K-Nearest neighbours, support vector machines (SVMs) and stochastic gradient descent.

Scikit provides model analysis tools including the confusion matrix to evaluate the performance of each model.

Scikit-learn is an ideal environment to get started in the world of machine learning and start working on simple tasks to later venture into more complete options such as Azure Machine Learning.

3. IBM Watson

IBM Watson's machine learning product offering can help you easily use data from a wide range of sources without losing confidence in the predictions and recommendations produced by your artificial intelligence models. 

The brand offers access to a complete portfolio of AI capabilities focused on business deployment. In this regard, IBM Watson does not only allow you to create machine learning models, but you can also leverage the toolset to accelerate time-to-value by pre-creating applications.

4. Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service, although it is targeted at experts in data science. The platform allows data scientists and developers to quickly and easily create and train machine learning models and to directly deploy them in production environments.

SageMaker provides an integrated Jupyter Notebook instance, which provides easy access to data sources for exploration and analysis, without the need to manage a server. It also provides generic machine learning algorithms optimized to efficiently run on large datasets in a distributed environment.

Like Azure Machine Learning, SageMaker natively supports the most popular machine learning and deep learning frameworks.

5. MLflow

MLflow is an open source platform that manages the entire machine learning lifecycle, including experimentation, deployment, and a central model registry. It can be used with all machine learning libraries and programming languages.

MLflow's main capabilities are: 

  • Tracking: The API and user interface that records parameters, code versions, artefacts and metrics, allows visualisation and analysis of the results of machine learning codes once executed.
  • Projects: MLflow packages machine learning code in a reusable and reproducible format, making it easy to share with other data scientists or deploy in production.
  • Models: The platform manages models from different machine learning libraries and deploys them to inference platforms.
  • Model registry: Through the central repository, the entire lifecycle of models can be managed, including version control, transitions and annotations.

These are some of the most commonly used machine learning tools in business. However, more than deciding which tool to choose, when working on a machine learning project, the most important thing is to understand what machine learning is, how it works and how it can be applied to generate business value

Do you want to implement a machine learning project, but don't know how to do it to generate business value?

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In business, machine learning has as many different applications as there are tools, technologies and systems. After all, machine learning allows machines to perform operations autonomously thanks to massive data tagging, surpassing human analytical capabilities. However, the programming of machine learning codes is created by humans, so for now, machines have not yet surpassed people.

Posted by Núria Emilio