Lili: Financial Platform Delivers AI Driven Customer Value with Qwak MLOps

A key aspect of Lili’s business strategy is harnessing data and machine learning to enhance customer solutions. This case study explores how Qwak, hosted on AWS, empowers Lili to leverage advanced machine learning capabilities and drive better customer outcomes.

About Lili

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Lili is a business finance platform designed for small businesses. With business banking, smart bookkeeping, unlimited invoices & payments, and tax planning tools–business owners always know where their business stands.

Lili

With JFrog ML's intuitive AI Infra platform and AWS, we achieve operational efficiency and smarter personalized experiences. We get to make data-driven decisions impacting the customers and driving the company metrics.

Challenges

Implementing and operationalizing machine learning models at scale requires a robust infrastructure and efficient deployment pipelines. Lili’s research team uses AWS SageMaker for model exploration and development, leveraging its managed notebook capabilities. However when it get’s to deployment LiLi required a more comprehensive approach.

Implementation

Solutions

Qwak’s end-to-end MLOps  platform  provides a unified environment for data scientists, engineers, and business stakeholders to collaborate seamlessly and accelerate the development, deployment, and monitoring of machine learning models. Lili’s team have full visibility for all models and model versions sent to production via Qwak analytics and monitoring capabilities.

Data scientists now access and prepare data using Qwak's data ingestion capabilities, and integration with various data sources and real-time streaming. Qwak's automated feature engineering tools enable efficient feature selection and extraction, facilitating faster model development.Hosted on AWS, Qwak allows a scalable and reliable infrastructure to support Lili’s growing needs.

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