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 with or without minimal human intervention. As data is constantly being created, machine learning solutions adapt autonomously to new information as well as learning from previous processes.
Most companies that handle big data value machine learning (for example, industrial education, which draws information from a variety of sources such as the Internet of Things, sensors, etc.).
If you want to make the most of your business data and automate processes you never imagined before, now is the time to implement a machine learning strategy in your organization. To guarantee success in this process, here are the 9 keys to successfully implementing machine learning in your company:
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1- Machine learning is a cultural change: The technology associated with machine learning and algorithms evolves very quickly, and it is not easy to keep up with them. Therefore, the main change in company culture should be: collaboration between different business sectors and the shared use of information should be encouraged for machine learning to be successful.
2- Define an objective as clear and specific as possible: it is important that teams dealing with machine learning projects identify the problems they want to face, applying maximum accuracy: for example, it is not The aim is to increase online sales by a certain percentage rather than specifying a desired increase in online sales percentage by monitoring site visitors.
3- Make sure you have reliable data: Data quality is essential for machine learning tools to perform their jobs efficiently. If you choose a supervised learning model, this source data must also be labeled so that the algorithm can learn to predict the correct exit labels, in which case, the company has previously implemented a sound and economically viable data acquisition and labeling strategy. Will be If you choose the unsupervised learning model, labeled data will not be needed, but it should be 100% reliable.
4- Rely on a unified platform: The most profitable investment in a first machine learning project is the platform to complete it. It is highly recommended to rely on fully integrated tools such as Google Cloud Platform, rather than building an application environment from different manufacturers and whose integration capability is yet to be demonstrated. In the case of Google Cloud Platform, its specific tools for developing machine learning projects are of great interest.
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5- Always look for simplicity: Whenever possible, it is better to look for 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 small sized projects or to address very specific points in business processes. That way, they'll be implemented and refined until the team tackles larger machine learning projects, and you'll discover other points to solve with machine learning tools.
7- Build multidisciplinary teams: If the project is developed by the IT team only, the efficiency of the machine learning project decreases. Bringing together the different business areas involved in the affected processes provides a broad umbrella of overview and adds fundamental ideas to the success of the project. These teams will decide the best way to achieve the proposed objective by implementing the following steps:
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