With all the excitement about Big Data and AI, many companies feel the urge to dive in and use AI and machine learning tools for their operations. After all, using the right machine learning tools can lead to increased revenue, lower costs, improved decision-making, and better customer relationships. However, figuring out how to apply AI and machine learning technology to address business challenges is a challenge for many companies.
Figuring out the right machine learning approach
Fortunately, there are only four main approaches to machine learning. Once you boil down your business problems to their essence, analyze which approach is applicable to your needs.
1. Feature extraction: This is an important pre-processing step when analyzing complex data with a large number of variables. Feature extraction involves choosing features or variables that describe the data with sufficient accuracy. For example, while analyzing text data stop-words such as ‘the’, ‘an’, ‘and’ may be ignored.
2. Clustering: This “unsupervised” approach is commonly used for statistical data analysis. It helps assign data that is similar into non-predetermined (hence unsupervised) clusters. For example, market researchers use cluster analysis to partition consumers into market segments for better product positioning.
3. Classification: In this supervised learning method, the algorithm learns from input data to sort new data into set categories. Banks use classification to assess their customers’ creditworthiness by generating credit scores. Facebook’s algorithms can identify faces by analyzing millions of tagged images.
4. Prediction: Predictive models predict the probability of an outcome or outcomes based on input data. For example, insurance companies assign policy holders risk of incidents based on information obtained from policy holders. E-retailers such as Amazon.com use predictive analytics to recommend products to customers based on their past orders.
Each of these approaches may be carried out by a single algorithm or a combination of algorithms. You may need a combination of these approaches to obtain the results you’re looking for.
Platform vs Library
Machine learning tools may be available from libraries or may be part of a platform. A platform provides all the resources you need to run a project unlike libraries that provide tools for specific purposes.
It’s all about the data
To achieve meaningful results it is critical that you have the right data and that it is of high quality. The more complex the problem, the more diverse and more comprehensive data you’ll need. You will also need to monitor and document data inputs and outputs throughout the process.
In-house vs. outsourcing
Setting up your own AI team and infrastructure is likely to be a costly endeavor in terms of both time and effort. By outsourcing to a vendor you can leverage their data analytics expertise without incurring any engineering costs.
Apart from assessing a vendor’s technology expertise and industry experience, it is important to ensure that their solutions align with your company’s growth strategy. The right vendor can guide you towards developing a machine learning strategy that is optimal for your business.