From Black Friday to Boxing Day: Unique MLOps Challenges of the Holiday Surge
The holiday season is here, and for retailers, this means a major influx in sales and distribution. The recent pandemic brought a newfound appreciation for e-commerce and localized service delivery among consumers. Customers now expect a personalized, seamless experience that is tailored to their needs. They demand relevant results for all their product searches, dynamic inventory management, and customized product recommendations based on purchases and holiday trends. Research has shown that nearly 48% of online customers abandon websites due to unsatisfactory product curation.
The holiday season presents an opportunity for retailers to deliver superior customer experiences to their customers. Approximately 80% of enterprises have adopted ML models as a means of improving customer experiences, generating more revenue, and streamlining their operations. Machine learning has provided a way for retailers to enable personalized service delivery on a larger scale, yet the successful integration of such a tool comes with its own obstacles.
Why most MLOps platforms fail during retail surges
Despite the numerous opportunities that ML presents to retailers during the holiday season, integrating MLOps can be a costly venture riddled with challenges in its adoption. ML models built for retail are constantly bombarded with real-time events to score due to the varied data sets from inventory reports, market trends, consumer behavior, and demand predictions. This normal flow of data is difficult enough for models to contend with, but with the surges during the holiday season, it can add severe lag to your MLOps using average deployment platforms. With nearly 50% of online customers abandoning retail sites with load speeds over 6 seconds, enterprises that can’t manage the model latency that accompanies holiday data traffic will miss out on substantial sales and revenue.
While models used for e-commerce may provide a more visible manifestation of your inefficient MLOps, there are also physical infrastructure costs that can be detrimental to your retail enterprise. Retailers handle millions of daily transactions both on and offline that only intensify during the holiday seasons. This can lead to costly investments due to the server requirements for most ML deployment solutions to keep pace. Add to that, the fact that consumer preferences will inevitably change from season to season, and demand for those products will vary every year, models will need to be frequently retrained in order for models to match the demand that is constantly evolving. Without observability and explainability in your ML deployment platform anomalies and drifts in your data may only be detected in batches far after the damage has been done.
A Deployment Solution to Cure the Holiday Surge Blues
Retailers looking to drive conversions and scale their business during this busy season need a deployment solution that can easily process thousands of data points, and test models, while in production, and allow for faster inferencing while simultaneously reducing infrastructure costs. This can allow retailers to scale during the festive peaks, unifying the increased user data to provide a more accurate predictive analysis. The explainability of the large data sets generates insights that allow retailers to optimize their operations and personalize their services to tailor their outreach to consumer preferences. The ability to monitor the retail market in correlation with all the crucial metrics of your business allows you to detect any model drifts and take timely action before incurring major losses.
However, as mentioned capturing offline transactions and in-store experiences are important as well and your deployment platform should have the ability to deploy models in a variety of environment be it on-premises, cloud, or at the edge to allow for better integration of all transactional data points. Although due diligence must be taken when selecting your deployment solution as you will need a powerful compute engine to maintain efficient runtimes and a latency-free customer experience despite scaling your data. Access to monitoring and observation tools is also key to managing model performance. Experimentation like A/B testing can help generate the most advanced version of your current model allowing for optimization while your model remains in production. This can help sort out the often dynamic nature of the retail industry, but enterprises would also benefit from the ability to validate the data as well.
Wallaroo is one such deployment solution as it allows retailers to optimize their customer experiences, make informed decisions, and drive profitability while upholding brand loyalty. While personalization of services across a larger scale would incur substantial conversion costs with other platforms, Wallaroo provides a model deployment solution that maximizes your ROI. Contact our brilliant team of experts to explore more MLOPs solutions tailored to your business operations this holiday season.
