Before You Embark on Your Machine Learning Journey: Some Key Considerations

With all the buzz around Big Data and AI, many companies feel pressured to jump in and adopt AI & machine learning tools for their own business processes. After all, employing the right machine learning tools can help your business achieve higher revenues, reduce operating costs, make better decisions and build stronger customer relationships. Yet, many businesses struggle with understanding how to use AI and machine learning technology to solve their business problems.

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 approach, the algorithm is trained based on input data to classify new data into predetermined categories. Banks use the classification approach to evaluate their customers’ creditworthiness in the form of credit scores. Facebook’s algorithms can recognize faces based on the data they have on millions of people that have been tagged.

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. The advantage of platforms is that it provides you all the resources you need to run a project unlike libraries that provide tools for specific purposes. WorkFusion’s Robotic Process Automation (RPA) platform, for example, uses its own neural network to sample from trillions of potential machine learning algorithms for each task before recommending automation.

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 and automation 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.