Machine learning solutions will be built and deployed at a massive scale in the coming years, and there will be a huge demand for people who can create and execute them. Current tools used to educate managers and executives to these solutions are either too complicated or too confusing. Online guides are mostly theory thus making it difficult for actual implementation.

Here’s a simple eight-step guide to help you decode machine learning solutions for your business:

Collect data and work on feature engineering

Multitude forms and types become comprehensible through feature engineering, which is defined as the process of “transforming raw data into features that better represent the underlying problem”. This helps algorithms and predictive models to improve accuracy and usability.

Shortlist key features

Once feature engineering process is completed, shortlist and prioritize from the overwhelming list of features to pick ones that are most important for key business decisions.

Filter out duplicate data

After the above step, next logical step is to weed out similar variables and duplicate data. It is critical to make all features unique in order to avoid any overlaps.

Pick the most suitable solution

Based on above shortlisted features, you will need a data scientist to understand your business variables and suggest the most suitable machine learning model. You may tweak it slightly and fine tune it to parameters that are relevant to you.

Tune model parameters

This is a cumbersome step but can work wonders for success of a model. It requires running experiments and reviewing results in a short span of time in order of assess the efficacy of the model. Based on the results, choose and freeze the right parameters.

Train the model

Next step is to train the model on data at hand. Don’t go full throttle right away, train first on a subset and then on the entire data, ensure model and set parameters go well with each other to give accurate results.

Streamline and get started

Run the model against the data points and ensure that the entire process, right from development to production is smooth.

Oversee and monitor operations

Once the model starts running, monitor the results for the first few weeks or months, until you are sure that everything is running just fine.