Understanding the Importance of ModelOps and how they help in removing risks in AI integration

In the past few years, multinational companies and other institutes have been escalating up their artificial intelligence and machine learning efforts. And to apply models to several of the organizational application, companies need to operationalize their machine learning models across the organization. While model-based automation has unlocked many avenues of enhanced productivity and profitability, managing models at scale has challenges of its own, along with designing efficient model operations (ModelOps), especially in the financial sector. Market Research Firm, IDC says that only 35 percent of analytics models are used in business applications. This is because most organizations lack a systematic way to track the performance of the models they do use. Hence the consensus is model operationalization is the need of the hour.

So the best solution to delineate this: ModelOps. ModelOps is a holistic approach for rapidly and iteratively moving models through the analytics life cycle, so they are deployed faster and deliver expected business value. It is a DevOps variation and follows DevOps principles to ensure IT compliance, security, and manageability. The team that runs ModelOps a nexus of data scientists, data engineers, application owners, and infrastructure owners. Though DevOps is motivated on application development, ModelOps is about getting models from the lab through validation, testing, and deployment phases as quickly as possible, while ensuring quality results. It also focuses on ongoing monitoring and retraining of models to ensure peak performance. It also empowers companies with better model risk management (MRM) by mitigating model and calibration risks.

The stakes in better model risk management are increasing per day. At present, it has become a routine custom to make adjustments affiliated to an institution’s risk governance framework to deliver desired or optimal results. McKinsey cites that financial institutions have already invested millions in developing and deploying sophisticated MRM frameworks based on artificial intelligence and data science. With digitization and automation, more models are being integrated into business processes, exposing institutions to higher model risk and consequent operational losses. The danger lies equally in defective models and model misuse. A flawed model caused one leading financial institution to suffer losses of several hundred million dollars when a coding error distorted the flow of information from the risk model to the portfolio-optimization process. Further, the lack of transparency around these processes can wreak havoc for governance.

ModelOps helps institutes weed out the pain points by strong governance, faster deployment, continuous performance monitoring across all platforms, and ensures that analytic investments add and deliver business value at a faster speed. It allows companies to operationalize the work across the ModelOps Lifecycle with the entire data science project team. Other than that, ModelOps also help to deploy new or updated models, drive agility, and administer models to safeguard compliance within regulatory and business risk requirements. Lastly, it can automate alerting to know when performance benchmarks are no longer met, so you can quickly retrain, revise or retire the model, as appropriate

In gist, ModelOps assures to deliver a streamlined workflow between the data science and IT operations teams, and enable enterprises to scale up their Artificial intelligence initiatives and connect both DataOps and DevOps.