Machine learning is a method of data analysis that automates the creation of analytical models. It is a discipline of Artificial Intelligence based on the concept that systems can learn from data, identify patterns and make decisions without or with minimal human intervention. As data is constantly being produced, machine learning solutions adapt autonomously, learning from new information as well as from previous processes.

Most companies that handle big data are recognizing the value of machine learning (for example, industrial learning, which obtains information from sources as diverse as the Internet of Things, sensors, etc.).

If you want to get the most out of your business data and automate processes like you have never imagined before, now is the time to apply a machine learning strategy in your organization. To guarantee success in this process, these are 9 keys to successfully implement machine learning in your company:

1- Machine learning is a cultural change: The technology associated with machine learning and algorithms evolve very quickly, and it is not easy to keep up with them. The main change must, therefore, take place in the company culture: collaboration between the different business areas and the shared use of information must be encouraged in order for the implementation of machine learning to be successful.

2- Define an objective as clear and specific as possible: It is important that the teams that are going to tackle the machine learning projects identify the problems that they want to face, applying the maximum degree of precision: for example, it is not the same to have the objective of increasing online sales by a certain percentage than to specify what is the desired Increase of online sales percentage by monitoring the site’s visitors.

3- Make sure you have reliable data: data quality is essential for machine learning tools to carry out their work efficiently. If you opt for a supervised learning model, this source data must also be labeled so that the algorithm can learn to predict the correct exit label, in this case, the company must have previously implemented a sound and economically viable data acquisition and labeling strategy. If you opt for an unsupervised learning model, it will not be necessary to have labeled data, but it must be 100% reliable.

4- Trust an integrated platform: the most profitable investment in a first machine learning project is the platform to carry it out. It is highly recommended to trust one with fully integrated tools, such as Google Cloud Platform, instead of building an application environment from different manufacturers and whose integration capacity is yet to be demonstrated. In the case of Google Cloud Platform, its specific tools for the development of machine learning projects are of great interest.

5- Always look for simplicity: whenever possible, it is better to seek simplicity in any area of ​​a machine learning project than to build complex and expensive neural networks.

6- Start with small projects: it is highly recommended to start with projects of small size or that address very specific points in the business processes. In this way, they will be executed and refined until the team can tackle larger machine learning projects, and you will discover other points to solve with machine learning tools.

7- Form multidisciplinary teams: If the project is only developed by the IT team, the efficiency of the machine learning project is reduced. Bringing together the different business areas involved in the affected processes provides a larger umbrella of observation and adds fundamental considerations for the success of the project. These teams will decide the best way to achieve the proposed objective by implementing the following steps:

• Algorithms: useful for solving a class of new problems.
• Libraries: these are the ideal point that offers you the most flexibility such as TensorFlow, which allows you to create new machine learning models and solve unique business problems rapidly and in a user-friendly approach.
• API: the advantage of using packaged APIs if assigned directly to your need without requiring any modifications.
• View, verify and control versions: data is the fundamental part of a machine learning project, and to manage this huge volume of information, it is necessary to trust tools such as those offered by Google, such as Google Cloud Dataprep and BigQuery, to visualize the data and verify the results in each phase of the process. It is also important to apply version control, to clearly identify the data that is acted on and avoid costly mistakes.