An Essential Requirement for Businesses Leveraging Machine Learning In Production
What Is Model Governance?
There are many issues that are brought about as a result of the nascent nature of Machine Learning (ML) which manifests as inefficient processes and lack of standards. Consequently, Machine Learning (ML) models require governance, just like any other model — although, there is slightly more emphasis on the required amount of governance needed for ML models.
Take an ML model that is designed to improve automatically as it accumulates more experience. The innate ability for the model to learn and unlock greater accuracy and predictability may be a blessing for any institution, however, this feat may possibly greatly boost model risk which results in bias being introduced into the model.
Therefore, model governance may be expressed as a set of baseline practices, rules, or validation procedures that a Machine Learning practitioner must abide by while developing Machine Learning models. These practices procedures can be set up by company heads or the government.
It’s important we highlight the significant stages during the Machine Learning model life cycle since it’s possible that model risk may occur at any stage. The ML model life cycle comprises of the following 4 phases of which related model governance procedures can be introduced at each step, for instance:
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#1 Business Requirement — A business case has risen in which Machine Learning is deemed fit to combat the problem. The hypothesis is validated, data sources approved and an agreement is reached with the stakeholders.
#2 Designing and Developing the Model — Minimum validation steps are done, reviews of the formulation and methods, fairness, out of sample tests created by non-stakeholders.
#3 Deployment —A Service Level Agreement is decided. The model is deployed in Shadow mode (or Dark Launch according to Google). Also perform A/B tests.
#4 Model Review — The model then faces continuous monitoring and validation reports are generated to provide documented feedback.
As many teams come to discover during the ML workflow, the model lifecycle is more than simply deploying models, which in itself is already a very time-.consuming and manual task.
Why do we need Model Governance?
Harnessing the power of Machine Learning is beginning to show increased advantages for the businesses and organizations that were able to adapt the idea to transform large volumes of data into new actionable insights and information quickly. Consequently, many have acknowledged that the predictive power of ML, when united with large data (of good quality), can be a source of competitive advantage.
For those that fail to embrace the growing presence of ML, they can be sure of increased competition and unsustainable operating models, regardless of their industry. With that being said, the necessary time to begin effective model governance is now.
This is because the essence of Machine Learning is extremely dynamic. Hence, there is an inherent need for more frequent model performance monitoring, continuous reviews of data and benchmarking, better documentation, actionable plans for unprecedented events, for anyone harnessing Machine Learning in their business or organization.
Therefore, Using the previous example of an ML model that improves automatically, we at Datatron believe it’s of vital necessity that meticulous model governance is established in order to rapidly detect when an ML model is beginning to fail without forgetting to include definitive operating controls on input and output data.
Example Use Case
Regardless of the industry that a business derives its trade, it’s fair to say model governance should be an essential feat when Machine learning is involved. However, the most obvious example of where it is essential to incorporate model governance is within the financial sector.
With many processes that were once done manually by humans now being transferred over to machine learning models, model governance is of immense importance in the financial sector since any wrongful implications made by the model may potentially have severe effects on millions of people.
Take credit scoring for example. Many, if not all, banking institutions have incorporated ML models to assist their bank in making automated informed decisions during the loan approval process. This sort of problem comprises risks that can affect both parties (that being the banking institution and the customer applying for a loan). If the model develops a bias in favor of the customers, the bank may potentially begin recklessly loaning out money to customers who display a poor track record which could be detrimental to the bank’s financials. Model governance would intercede before this fault ever becomes a problem that could cause severe damage since the credit scoring model would continuously be audited for any biases introduced while the model is in production.
The ability of large companies to govern their ML models has far exceeded their infrastructure and the bandwidth of their Engineering and Data Science teams. Building models isn’t the problem — it’s organization. We’re building Datatron to fix this, by speeding up deployments, detecting problems early, and increasing the efficiency of managing multiple models at scale.
Here at Datatron, we offer a platform to govern and manage all of your Machine Learning, Artificial Intelligence, and Data Science Models in Production. Additionally, we help you automate, optimize, and accelerate your ML models to ensure they are running smoothly and efficiently in production — To learn more about our services be sure to Request a Demo.
Thank you for reading! This blog post was written by Kurtis Pykes. Connect with me to learn to read more Data Science and Artificial Intelligence related articles.