Big data has changed the way companies do business forever. There are massive amounts of data waiting on businesses to harvest and use them to enhance their best practices and products. It would take an impossible amount of people to process all the data available to us, but with machine learning, almost anything is possible.
Machine learning is a form of artificial intelligence. It uses automation to collect and analyze data based on the parameters set by business users and data scientists. In this brief article, we’ll tell you how you can implement ML in your business. By the end of this article, you’ll know how to use machine learning to take your company to new heights.
Decide what machine learning can do for your company.
Before you can implement machine learning, you must know what it is you want to learn from the available datasets. Netflix and other streaming apps are prime examples of using machine learning to get insights. You could say that Netflix wants to learn from its customer data what movies each of their subscribers like. By knowing what you want to achieve from your data, you can set the parameters for data collection and model learning.
The data scientist collects data and develops a machine learning model.
Once the data scientist knows what kind of data and analytics you need, they can begin the process of data discovery. Data discovery can be a lengthy and labor-intensive process. Moreover, you won’t even know if it’s worth the effort until the scientist has completed their work.
Next, the scientist begins experimenting with training models to yield the desired results. During this step, they develop training models from complex algorithms. If their work is successful, the ML algorithm moves on to the ML engineer for the implementation of a pipeline that will enable streamlining and automation.
The data engineer begins implementing an ML pipeline.
After the data scientist completes the first part, it’s time for the machine learning engineer to take over. The machine learning engineer uses code to create an application capable of running the algorithm and automating the analysis of training data.
It’s important to implement logging during this process so you can know which ML models you’ve used on what training data. This ensures that you can easily find and identify your data and training models when needed.
Continued monitoring of the algorithm is necessary for data quality.
The best way to ensure the efficacy of your machine learning operations is through constant monitoring. We live in an ever-changing world, and the training data you used to create your ML model will eventually be incompatible with current realities. However, as long as you use an ML framework, which promotes scalability, you can adjust your algorithm to match the evolution of data, and it will continue to provide business value.
As companies continue to find new ways to use big data, you can expect the importance of machine learning in business to continue to grow. With ML and automation, your company can collect and analyze infinitely more data points than humans could on their own.
The first step to implementing machine learning in your company is to consult a data scientist to find out how ML can take your business to the next level. From there, it’s time to start searching for and collecting data and searching for actionable insights. Next, the scientist creates an ML model, and the engineer goes to work implementing a pipeline to streamline its deployment. Once the ML pipeline is in place, the data scientist and engineer monitor it to improve it and prevent drift. Implementing machine learning as part of your company’s infrastructure can be challenging, but the benefits are countless.