Enabling Self-Service MLOps and Faster ML Delivery at monday.com

Monday.com is a work operating system (Work OS) that allows organizations of any size to create the tools and processes they need to manage every aspect of their work. The platform intuitively connects people to processes and systems, empowering teams to excel in every aspect of their work while creating an environment of transparency in business. Monday.com has teams in Tel Aviv, New York, San Francisco, Miami, Chicago, London, Kiev, Sydney, São Paulo, and Tokyo. The platform is fully customizable to suit any business vertical and is currently used by over 152,000 customers across over 200 industries in 200 countries.

Monday.com, a work operating system (Work OS) that allows organizations to manage every aspect of their work, faced significant challenges in implementing machine learning (ML) solutions. The company's data team, BigBrain, was responsible for the data and analytics platform and ML initiatives. However, as demand for ML solutions grew, the data scientists found themselves heavily reliant on engineers to bring models to production. This resulted in a high time to value, with models often waiting for deployment until a developer was available to set up the infrastructure. Furthermore, the data scientists were siloed and had a disconnected workflow between where the model was trained, deployed, and monitored, creating unnecessary complexity. Key pain points included excessively high time to value due to production bottlenecks, dependency on developers and engineers for deployment, missing critical MLOps capabilities, inability to consolidate distinct endpoints into a multi-model endpoint pattern, and disjointed workflow due to each data scientist working with different machine learning tools.

Monday.com turned to cnvrg.io to address these challenges. This platform provided the data science team with all the MLOps capabilities they needed, along with a user-friendly interface. It allowed them to focus on research rather than learning Docker and Kubernetes. The solution offered experiment tracking and management for easily reproducible results, the ability to compare different model hyperparameters configurations and training runs, and a unified system to track model evaluation metrics and store and manage model artifacts. It also simplified the encapsulation and orchestration of models with docker images and created a CI/CD re-training pipeline that updated the model based on performance accuracy. Furthermore, cnvrg.io enabled the seamless chaining of algorithms and custom code written in any language and provided customizable endpoints in one click.

• With cnvrg.io, the data scientists at monday.com were able to get models into production on their own, instead of waiting on developers to deploy. This increased the data scientists' ownership of the end-to-end ML lifecycle and reduced time waiting to understand model performance. With cnvrg.io's MLOps capabilities, the BigBrain team has been able to take on more projects that have directly impacted business units from marketing to customer support, as well as enhanced the product experience with personalization and summarization applications. The solution also made it easier to adopt a multi-model endpoint pattern for efficient serving and shortened the time to see results from the modeling work.

• Cut engineering bottlenecks by 100%

• Accelerated time to consumption

• Reduced time spent on technical configurations and infra setup by 80%