There is currently a lot of excitement surrounding artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Despite the fact that these technologies have existed for decades, new algorithmic developments coupled with advancements in compute power have made these technologies more attractive to enterprises. Organizations are adopting advanced analytics technologies in order to […]
There is currently a lot of excitement surrounding artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Despite the fact that these technologies have existed for decades, new algorithmic developments coupled with advancements in compute power have made these technologies more attractive to enterprises. Organizations are adopting advanced analytics technologies in order to better understand customer behaviors, improve operational efficiencies and gain a competitive advantage. Businesses want more accurate insights and the ability to quickly respond to change. Machine learning allows them to build systems that are capable of learning from data to recognize patterns and predict future outcomes with minimal human intervention. How can enterprises get the most accurate results using machine learning on issue after issue? How can organizations empower business analysts to utilize the predictive powers of machine learning? What do these businesses need to know? This post explores some of the best practices enterprises can employ to get the most out of machine learning.
Machine learning is a powerful tool that enables organizations to gain insights into several kinds of behaviors. It is utilized in vertical and horizontal applications to help enterprises become more proactive. If it is possible to structure a set of ‘if-then scenarios’ or rules to handle the problem in its entirety, then the need for machine learning is invalidated. Additionally, if there is no precedent for a successful outcome after applying machine learning to a specific issue, its may not be the best foray into the world of machine learning. Focusing objectives on specific use cases that will have a meaningful impact for the business is a key to success. Once you have the data and a solid idea of what you are trying to accomplish, move on to machine learning. Bluntly put, if you have a $100 million problem, spending $20 million is not a big deal.
“Clean data is better than big data” is a phrase that is regularly echoed amongst data science professionals. Some people assume that a large volume of data for machine learning negates any data quality concerns. I you have mounds of disjointed and unstructured data; you will have to ‘clean’ it before you can gain any insights from it. Good quality data is crucial for models that are in production; otherwise the ML models will deteriorate quickly. The effects of poor data quality are not limited to the degradation of machine learning algorithms, they also affect reporting, decision making, and operational efficiencies. If limited data is available, enterprises should first start by applying supervised machine learning, and use existing labelled training data to begin finding insights.
It is not uncommon for enterprises to initially make mistakes when building out their training data. Training data sets need multiple example predictor variables to predict or classify a response. In machine learning, the predictor variables are referred to as features while the responses are referred to as labels. The best way to go about it is working backward from the solution, explicitly defining the problem and mapping out the data required to populate the models. Capturing temporal variations in your data training set is crucial. The data can be biased by the model; ideally, you should introduce an aspect of exploration or randomness so as to get less biased samples.
AI-based tools need help to unlock the valuable information lurking in the data generated by your systems. A comprehensive data strategy that focuses on data availability, acquisition, labelling, and the technology needed to pull data from disparate systems, is a good place to start. Data quality and data governance are like two sides of a coin; one is not plausible without the other. This means that a data governance process that is inclusive of practices and policies has to be put in place. Existing governance practices may have to be revamped or expanded as well, but this should be a joint effort between the business and IT.
Machine learning models have to be continuously monitored and updated to prevent degradation over time. Depending on the business issue at hand, a model may have to be frequently updated. Keeping track of the model also helps maintain institutional knowledge. Organizations should consider solutions that centrally monitor, analyze, configure and execute tasks like replication across multiple endpoints. This facilitates capacity planning, performance management, and troubleshooting. A consolidated command center ensures that data remains available, and ready for machine learning analytics.
Machine learning models require both data and time to adapt, grow, and be informed by experience. This is why an ML solution will typically be incorrect a certain percentage of time, particularly when its being informed by varied or new stimuli. Building a solid machine learning solution requires time to carefully think and test out selecting data, labelling data, selecting algorithms and testing in a production environment. There are no ‘’off-the-shelf” ML solutions for complex and unique business use cases. If you task has absolutely no room for error, then machine learning is not the best solution for the job.
