Across sectors and geographies, companies are seizing the power of Artificial Intelligence (AI) and Machine Learning (ML) to glean insights for competitive advantage. However, a recent study by McKinsey shows that very few organizations have successfully scaled ML deployments beyond the pilot stage, even though the benefits amplify in myriad ways when implementations are done at scale.
The Issues with Current ML Programs
There are hundreds of AI and ML models powering business-critical decisions today. Managing these in real-time is crucial. Models powered by stale data no longer make predictions that reflect the state of the world. This ‘drift’ is a major challenge as an astonishing 82 percent of organizations make decisions based on stale data.
Large-scale ML initiatives also have many moving parts – from managing datasets and pipelines over various periods to monitoring models and building the necessary processes. An absence of the right expertise to bring it all together leads to implementation challenges.
Many companies experiment with AI/ML projects in silos. This causes a lack of shareable and repeatable processes to monitor and manage the models at scale. Moreover, running a successful ML program is further complicated by new variables creeping into the business and operational environment.
Machine Learning Operations (MLOps) could be the key to resolving these issues. This advanced capability is necessary for organizations looking to implement ML programs at scale. MLOps combines emerging best practices and underpinning technologies to provide a centralized and governed means to automate, manage and scale ML deployments in production environments.
An Effective MLOps Strategy
MLOps-enabled companies have a well-defined data architecture, a robust Continuous Integration and Continuous Deployment (CICD) pipeline, and an intelligent training model optimized for ML purposes. The in-built capabilities allow the algorithm to re-train and adapt to sudden shifts in data and business.
To execute this, enterprises are looking for the following:
-
One product for all deployments: Using a single product for all pilots embeds replicability, facilitates model integration and improves governance
-
Production monitoring for all models: Using automation to monitor model health saves time and resources
-
Automated model lifecycle management: Continuous updating of the model using the CICD pipeline extends its performance period and helps avoid model decay
-
Enterprise governance model: The pitfalls of following different deployment and modeling processes can be avoided by adopting a governance framework. It sets the base for continuous monitoring and feedback on the quality of data, fairness and explainability of the data model
However, since MLOps is an emerging approach, there is limited expertise in the market. It can take years of trial and error with MLOps to generate measurable value from AI. Therefore, collaborating with organizations that have experience in implementing DevOps is the right way forward. Familiarity with DevOps offers a head start in MLOps, as many features – such as automated pipelines, continuous testing and continuous monitoring – are common to both approaches.
A Collaborative Approach to Achieving Scale
A well-planned MLOps implementation can deliver considerable benefits in efficiency, besides unlimited scalability and significant risk reduction.
For the best outcomes, MLOps must smartly blend human and machine capabilities. It calls for a holistic approach to managing data pipelines, version controls, knowledge repositories, ease of deployment, and monitoring and re-training models.
Partners with advanced capabilities in data science and experience in managing complex ML environments understand the nuances behind operationalizing ML at scale. Collaborating with such technology partners will go a long way in ensuring an organization derives the most value from its ML investments.