From Concept to Reality: Utilizing Machine Learning for Real-World Results

 Machine learning almost sounds too good to be true, and this is because the science is deceptively complex. You would think that once a machine takes in enough information and enough variables, it will be able to implement your model flawlessly. However, this is not always the case. To facilitate practical and practicable machine learning models, you must learn right alongside your computer and fine-tune your datasets to best reflect the reality you wish to achieve. Here are a handful of tips to help your engineers and researchers go from a conceptual model to the applicable real-world results.

Set Metrics and Stick to Them

 Every experiment has some sort of metric for success or failure, or to put it in a nicer way, for measuring the variables. The same can be said of machine learning models. As the computer runs your data and information through its systems, more information and results will be generated; it is up to your engineers to determine the parameters for measuring that information and which information to look for in the first place. Once they know which factors –such as accuracy or log loss - they are looking for, setting metrics to gauge the computer's performance becomes much easier. This is crucial as it will allow your engineers to measure the efficacy of their current model and tweak processes from there.

 Simplicity is Strong, So Keep the Model Simple

 While it may be impressive to generate a highly complex model, it is easier to measure the metrics of a simple model that eases the process of interpretation. It is also easier to set up a solid infrastructure in the beginning and evolve that infrastructure later, so for the best results, your engineers should create a simple model with a solid infrastructure to build upon. Also, a good infrastructure can be tested independently of the machine learning, so remember this as you move forward.

Find Problems Before You Finalize Your Model

To create the best model you can, your engineers should solve any problems with the model before they export it. As long as the model is simple to interpret, debugging it and solving problems will go smoothly.

 New Features Should be Easy to Understand

 When upgrading and combining existing features to make new ones, remember to combine these features in ways that are understandable to the people who will be using the program - people who do not necessarily understand machine learning as well as your engineers do.

 Learning is Exciting, New Information is Helpful

Once your model ends up slowing down, it is time to feed it new information. The newer the information, the more likely the computer is to learn something new and grow from there. The point of machine learning projects is to get machines learning, after all, so facilitate that learning with as much relevant new data as you can get into your model. This will keep things fresh for the computer, and growth will ideally stem from these new datasets.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at www.PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at www.PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS