Agile Machine Learning Operations for Technical Leaders in CPG

  • Model deployment takes a very long time: Ad-hoc ML models take time to deploy, making the enterprise less agile to environmental changes. These delays in deployment often render the model ineffective in feeding the business good predictions due to the changing business environment.
  • Model deployment cannot be standardized globally: Most global CPG brands have a central data science Center of Excellence (CoE) and few regional data science teams to deploy the model with regional differences. This leads to non standard deployments and model management in production, further limiting the scalability of data science efforts.
  • No easy way to perform A/B tests for models in a production environment: One cannot assume that the current market conditions match the conditions on which a model was trained. As a result, the best test for a new model’s accuracy is in production, which can cause breakages in the consumer experience.
  • No visibility into when and why a model stops working: When the environment changes because of a sudden spike in supply prices or shifting consumer preferences, the model needs to be changed to be effective. However, once a model goes into production, data scientists lose visibility into the ongoing performance of the models. Most deployment processes and tools do not consider collaboration between data scientists and ML engineers, so when a model starts to drift there is no communication.

The Wallaroo Platform For Agile Last Mile Model Operations

When the Wallaroo team launched the first product in 2017, it was focused on building a high-performance compute engine for the purpose of analyzing large amounts of data via computational algorithms. While the customers could efficiently analyze their data and use it to run ML models at scale, they soon pointed out their next biggest challenge: bringing those models online easily and then understanding how the models were performing to sustainably generate business value. The team quickly discovered that the maximum value of AI/ML doesn’t come from just building precise models but also from the last mile: having ML in production, at scale, driving business results.

  • Make deployment easy no matter the environment: While other MLOps deployment platforms require lengthy and costly migrations of the entire life cycle to provide standard deployment processes, Wallaroo fits into any data ecosystem. That is, your data scientists can continue using their favorite tools and frameworks to build their models in any environment, and then use Wallaroo to deploy those models in any cloud, on-prem or at the edge. And with the SDK, API, and dashboard options, deploying a model is as simple as a single line of python (more to come on our autoconvert capabilities to avoid complex model reengineering).
  • Provide powerful testing capabilities so the consumer experience is not broken: Wallaroo has advanced testing frameworks like A/B/n* testing and shadow deployments so data scientists can see how models would perform in an actual production environment. This includes edge ML testing capabilities to run A/B tests across portions of an IoT fleet or do shadow testing for use in manufacturing or logistics.
  • Automate visibility and alerts into ongoing model performance: Agile ML requires immediate insights into when the environment or consumer preferences change. Wallaroo’s model insights and observability capabilities track ongoing model performance via a stream of comprehensive audit logs, and our Model Insights automatically flag when a model is failing. We continue to invest in model explainability to bring transparency to formerly black box algorithms, so the business can understand what’s driving predictions, which is particularly important for CPG brands’ ESG efforts.
Image courtesy of Diego Delso

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Wallaroo

Wallaroo

Wallaroo enables data scientists and ML engineers to deploy enterprise-level AI into production simpler, faster, and with incredible efficiency.