The Case Study: Using Control Charts to Monitor ML Model Performance One way to do that is by using control charts. Therefore, data scientists and companies must monitor their deployed machine learning models. Today, machine learning models are being deployed in all kinds of industries. We can think of a deployed machine learning model as a virtual process. We also have virtual processes that don’t necessarily have a physical component and whose data is entirely on the cloud. Today, we have sensors that can automatically gather data about any process.
XBAR CONTROL CHART MANUAL
In the earlier days, most data gathering had to be manual (i.e., physical data gathering). However, I believe that recent computational advancements have made control charts more practical than ever. Some think of control charts as an antique technique only suitable for manufacturing applications. In this article, I will present a case on how we can use the most basic control chart to monitor a deployed machine learning model. Although somewhat antique, I believe control charts are a valuable methodology for monitoring deployed machine learning models. Then, you can monitor your process across time using those bounds. The main idea of control charts is to determine if a process is under statistical control by setting lower and upper bounds (i.e., control limits) based on the probability distribution of your quality characteristic. This methodology was introduced by the statistician Walter Shewhart in the early 20th century and has found many applications in industry settings (most notably in the manufacturing sector). Online Green Belt certification course ( $499).Control Charts for Machine Learning Using Python IntroductionĬontrol charts are a visual mechanism used to monitor a process by tracking independent observations of a quality characteristic across time. Online SPC certification course ( $350) or In his online SPC Concepts short course (only $39), or his Learn more about the SPC principles and toolsįor process improvement in Statistical Process Controlĭemystified (2011, McGraw-Hill) by Paul Keller, Process capability is only meaningful when the process is stable, since we cannot predict the outcome of an unstable process.įixed or Varying Subgroup Sizes for X-Bar charts If the process shows control relative to the statistical limits and Run Tests for a sufficient period of time, then we can analyze process capability relative to requirements. (This can be done automatically using the Auto Drop feature in our SPC software). Remove the statistical bias of the out of control points by dropping them from the calculations of the average X-bar and X-bar control limits. Brainstorm and conduct Designed Experiments to find those process elements that contribute to sporadic changes in process location. If there are any out of control points on the X-bar Chart, then the special causes must be eliminated. Never consider the points on the X-bar chart relative to specifications, since the observations from the process vary much more than the subgroup averages. Once the effect of the out of control points have been removed from the Range chart, look at the X-bar Chart.Īfter reviewing the Range chart, interpret the points on the X-bar chart relative to the control limits and Run test rules. In this case, look at how you measure the variable, and try to measure it more precisely. If there are values repeated too often, then you have inadequate resolution of your measurements, which will adversely affect your control limit calculations. (This can be done automatically using the Auto Drop feature in our SPC software).Īlso on the range chart, there should be more than five distinct values plotted, and no one value should appear more than 25% of the time. Remove the statistical bias of the out of control points by dropping them from the calculations of the average Range, Range control limits, average X-bar and X-bar control limits. Brainstorm and conduct Designed Experiments to find those process elements that contribute to sporadic changes in variation.
If there are any, then the special causes must be eliminated. On the Range chart, look for out of control points and Run test rule violations. The control limits on the X-bar chart are derived from the average range, so if the Range chart is out of control, then the control limits on the X-bar chart are meaningless.
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