Organizations are increasingly adopting machine learning, a type of artificial intelligence that allows software applications to get better at predicting outcomes on their own. While these models can help businesses make better decisions, they have a major weakness. Once the data that support machine learning models become outdated, so too do the models—a problem known as “data drift.”
“The impact on business is profound. To avoid this, you need to keep track of whether or not your models are stale,” Google Cloud’s Dale Markowitz and Craig Wiley write in Forbes. “But knowing which of your models are in use and what they are doing is something many companies struggle with. Consider several features all drifting at the same time. This might seem like simple housekeeping compared to the hard math of building neural networks, but maybe that’s why it’s so often overlooked.”
How Can You Avoid Data Drift?
You can prevent data drift by building machine learning operations (MLOps). As we discussed in a previous post, an appropriate MLOps establishes a governance framework around machine learning that helps organizations make these models work not only technically but also operationally. According to Chida Sadayappan, Lead Specialist for Data Cloud and Machine Learning at Deloitte Consulting, MLOps allows you to more effectively manage the operation of your data.
“MLOps isn’t an algorithm, but it does operationalize the algorithm to simplify the predictive process,” Sadayappan writes in insideBigData. “MLOps enables the appropriate uses of ML algorithms to teach systems how to identify and classify data today and ‘learn’ new, more effective techniques to do so in the future. These decision-making ML algorithms help businesses recognize patterns that predict consumer preferences, identify fraud, monitor financial performance, and reimagine customer experience, to name a few use cases—and become operationalized with MLOps.”
Four Elements of Effective MLOps
There are four basic components for building an effective MLOps framework. According to Sadayappan, these include:
- Versioning the model: Explore various data sets and algorithms that can fix the same business problems. “Reproducibility is critical, and versioning each data set, algorithm, and ingestion pipeline is essential to creating results that can be reproduced.”
- Autoscaling: Your MLOps model should have the ability to quickly scale up or down as necessary. “That’s essential because large organizations may eventually create thousands of data models.”
- Constantly monitor and train your models: Continuously monitoring and training model performance ensures they provide correct results. “That’s because external factors like economic conditions are constantly in flux, which can make obsolete the data used in the initial training process. Monitoring helps evaluate model output and track drift and effectiveness over time.”
- Retraining and redeployment: As model drift happens, “be prepared to retrain the model using new data and then redeploy it.”
Data drift can become a serious problem for any organization looking to improve its business with machine learning. However, building sound machine learning operations with best practices for continuously monitoring and retraining your models on new data can go a long way in avoiding this problem.
Understand Machine Learning
By providing AI with the ability to learn from its experiences without needing explicit programming, machine learning is important to developing the technology. Covering machine learning models, algorithms, and platforms, Machine Learning: Predictive Analysis for Business Decisions, is a five-course program from IEEE.
Connect with an IEEE Content Specialist today to learn more about this program and how to get access to it for your organization.
Interested in the program for yourself? Visit the IEEE Learning Network.
Sadayappan, Chida. (29 July 2021). Turning Big Data into better data with MLOps. Inside Big Data.
Markowitz, Dale and Wiley, Craig. (19 May 2021). Why MLOps Is Critical To The Future Of Your Business. Forbes.
[…] we discussed in previous posts, the reasons for this run the gamut. They range from the absence of governance frameworks to data drift, a problem in which the data that machine learning models are built on become […]