Have you ever experienced watching a series on Netflix that captivated you, and then the platform starts recommending similar shows or those with a similar context? This is thanks to Machine Learning.

Machine learning has become an indispensable tool driving innovation and transforming the way we interact with technology.

Its main objective is to solve day-to-day problems and make people and machines work together.

Keep reading to find out what machine learning is, how to use it in your business, and how major industries are implementing it to improve their operations.

What is machine learning?

Machine Learning, or in Spanish, “Aprendizaje Automático,” is based on data analysis and algorithms that allow computers to learn based on trends observed in this data.

It belongs to a branch of artificial intelligence and is crucial for businesses since, through statistics obtained from collected data, tasks can be automated, and decisions can be made based on real information.

As machines acquire information, they improve their performance and learn on their own.

Why is it important for businesses?

Those who possess accurate information have a competitive advantage in developing more effective strategies and achieving their goals in a shorter timeframe.

When a company decides to delve into the field of machine learning, it must understand that this represents a significant change. This change involves a transformation in the philosophy and culture of all those who will be affected by the results derived from the implementation of this technology.

The various involved departments must fully grasp the importance of teamwork and sharing information with all stakeholders.

In this way, when it is necessary to make implementations based on the obtained results, everyone will be committed and able to create successful strategies.

Steps to implement machine learning in your business

Prediction models based on data help businesses obtain various pieces of information. Therefore, it is essential to start by defining what we want to achieve to know what information we need to gather.

• Set goals based on needs: This involves precisely knowing certain points we detail here: What is the objective I want to achieve in my company? From which sources can I obtain the necessary information, among others?
• Define the project’s scope: From the beginning, project boundaries must be established While machine learning helps in many areas of the company, it is good to delimit and separate tasks and have a clear understanding of how far this AI can collaborate with us and separate projects..
• Specialized talent – Do I have it available, or should I outsource? Many times, companies have trained personnel who can take charge of the project because they have the expertise in the solution to be used or experience with similar tools.

In other cases, outsourcing such services to expert companies that guarantee the investment and have the necessary experience to obtain reliable and usable data may be more cost-effective

• Choose the appropriate application to start analyzing data:

Here are some platforms that provide solutions for applying machine learning:

• • Azure: With this solution, developers can easily and quickly deploy models in production.
  • AWS: It offers support at every stage of the ML adoption process through artificial intelligence (AI) and ML, infrastructure, and deployment resources.
  • TensorFlow 2.0: Developed by Google, it allows users to create and train learning models..
  • Google Cloud: It provides the basic infrastructure, data analytics, and Google’s machine learning system.
  • Scikit-learn: Easy to use, its algorithms and tools favor data mining and predictive analysis. It is used for machine learning with Python.
  • H2O.ai: An open-source platform with tools for large-scale data analysis.
• Data collection: It is important to know which data the company can obtain and from which platforms or channels they are being generated.
• Select machine learning algorithms: Some algorithms include regression, classification, clustering, and neural networks.
• Create multidisciplinary teams: This point is relevant for the project’s success. The team should consist of members from each area affecting the process. This allows for a broader view of each team’s requirements, where data can be observed from different perspectives.
• Train the system: There is a pre-training phase where the algorithm must be adjusted to make predictions and decisions based on training data.
• Validate and evaluate: After verifying that the model is functioning, data is evaluated and validated to confirm that it is adjusted based on training data.
• Deployment: When the model has been successfully evaluated, it can be used to make predictions or decisions.

Examples of how large companies are using machine

• Uber: Uses it to reduce waiting times and optimize driver routes.
• Amazon: Optimizes its supply chain, predicts product demand, and improves the customer experience.
• Netflix: Recommends movies and TV shows to its customers.
• Tesla: For the automation of its cars to enable them to drive autonomously.

Conclusion:

Adopting Machine Learning improves efficiency, drives innovation, reduces costs, and helps increase company revenue. Having information at your fingertips helps make better decisions for the execution of precise strategies in many business sectors..

Our team of experts is ready to collaborate with you on implementing machine learning models tailored to your needs. Let’s talk.

https://becolve.com/blog/como-donde-usar-machine-learning-para-la-optimizacion-de-produccion/

https://op.europa.eu/en/publication-detail/-/publication/f089bbae-f0b0-11ea-991b-01aa75ed71a1

https://www.iebschool.com/blog/herramientas-business-intelligence-2021-big-data/