Overview: Automated Machine Learning: (AML)

How valuable would it be to predict the winners of the Super Bowl, World Series, The Grammys, or even political elections? A company called DataRobot an enterprise artificial intelligence platform using Automated Machine Learning did predict all the winners of the 62 Grammy Awards.

Delivering accurate predictions can have significant implications for companies. How many apple iPhones will people buy next year? What motivates people to post on Instagram or communicate on Twitter? Why is customer loyalty so high for Amazon? How much will salespeople generate next quarter? Although these types of questions do not capture who will come out on top, they enable companies to anticipate results and how they can leverage these predictions in their decision-making.

Automatic Machine Learning is a mainly supervised approach that explores and selects models using different algorithms and compares their predictive performance.  It is a process that automates the task of developing analytical models with machine learning. When applied AutoML has helped to overcome the shortage in advanced data analytics skills enabling more businesses to benefit without the traditional manual approach of standard machine learning. Several of the new technology platforms automate the ultimate analysis process support compared comparing predicted for our performance is enabled businesses of all sizes to accurate predictive models. Even though the process has some automatic features users still must understand the underlying elements involved in developing this model.

Marketers need to remember that implementing AutoML does not replace human expertise. The AutoML performance is only capable of analytical discovery of relationships that are actually present in the dataset. Understanding the context of this data and ensuring that it means quality standards will result in the most effective models.

AutoML in Marketing

Companies using Salesforce CRM tools can then discover hidden insights in predicting customer relationship outcomes so customers can better manage in real time. Salespeople using the auto ML system can quickly develop lead scores for prospects, calculate the likelihood of sales for a customer, determine overall brand engagement, and assess the likelihood of closing a deal.

Data Preparation

Success using AutoML begins with data preparation. During model building, tens of models are usually built automatically after the analyst specifies the dependent (continuous or categorical) variable of interest. AutoML uses preestablished modeling techniques that create access for anyone from beginners to data science experts at the click of a button. Sometimes the best approach is to combine different algorithms blending information from one then one model into a singular supermodel this type of model is referred to as an ensemble model. It combines the most favorable elements from all models into a single model the process reduces issues such as noise, bias, and inconsistent or skewed variants that cause prediction problems. As a result, the ensembled model usually generates the best overall predictive performance. Although a simple model offers several advantages is important to keep in mind that understanding how different variables have contributed to an outcome can be different. Model recommendations multiple perspective models are examined in the model with the most accurate predictions is recommended. Accuracy is earned by observing how well a model emphasizes relationships and patterns in a data set and uses this knowledge to predict the outcome. Higher levels of accuracy are measured based on better predictions of observations not in the original data set used to develop this model. The most accurate prediction models are then used by companies to make better business decisions.