The adoption of AI has invigorated businesses to look at new-age problems with a new perspective and solve them by leveraging the power of AI. That said, not all AI projects succeed. In a Gartner CIO survey conducted in 2018, whose findings are still relevant today, the research firm said that through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them. In many cases, scaling the AI models into mainstream operations and moving AI models beyond the initial proof of concept and prototype to full-scale production remains a huge challenge. This is the reason why despite the early adoption of AI by many organizations, few have managed to reap consistent business value on their AI investments.
With constant evolution in the type of data, which is getting collected, there is a need for timely maintenance and monitoring of the model along with operationalizing the models. It’s like any car which we own, we need to have it maintained properly and monitor its signals via telemetry. Similar is the case of managing AI models; in order to scale them properly, we need to monitor the signals of the models and ensure that the model is in the best of health for delivering business value. Simultaneously, other components of the data ecosystem like feature engineering, model training, testing and monitoring cohesively are also equally important.
In a typical AI journey, we face a lot of challenges which hinder the prospect of model scalability. These challenges are typical in nature and range from inability to show business value due to lack of ground truth evaluation parameters, to data infrastructure issues resulting due inability to manage data efficiently. The result is lack of drift mechanism, to unmonitored models leading to lack of information in managing the model output. This can also cause issues with respect to model governance, serviceability, and deployment.
The end result being in this juggernaut, the models fail to scale, and these manual interventions increases the time-to-market. Now, with cloud computing becoming the new normal by 2024, it is expected that almost 70% of all the AI models will be cloud based the need is to have an efficient mechanism of scaling AI models.
The need is to have a holistic framework for operationalizing the AI models and bring together the various key technology enabled components under one umbrella which is bundled with a people and process enabled target operating model.
The focus must be on having a centralized mechanism to standardize and streamline the overall AI/ML model lifecycle and help organizations to achieve time to market and scalability by providing:
- An end-to-end model life cycle management including experimentation, training, deployment and serving of the models across all key cloud hyperscalers
- The capability to ensure Infrastructure as a code by automating resource provisioning for training, scoring, etc. leveraging pre-built pipelines for the models
- The ability to decipher insights related to model explanations and importance of features for a particular model and bring in a responsible AI practice
- A centralized view of all models in the production to help monitor model drift, data drift, service health along with a re-training mechanism
- A cohesive governance mechanism for the models wrapped with persona based access mechanism and adherence to well-defined guidelines principles of responsible AI
- An automated model comparison and registry by extending DevOps pipelines, along with continuous re-training mechanism for the models
- The capability to attain business insights on the models deployed by leveraging key business metrics to capture performance and RoI on the models
Bundling all these key elements into a pre-defined target operating model will strategically enable business and AI teams to collaborate in a frictionless environment, which will ensure that the models can scale seamlessly from proof of concept to full scale production.
