We’re still in the early innings of AI adoption, yet we’ve already seen it transform industries. Companies like Netflix, Spotify, and Uber have scaled internal teams from a handful of data scientists and machine learning (ML) engineers to hundreds. These teams, and the models they built to inform business and product decision-making, have shaped how consumers watch television, listen to music, and hail rides.
As AI use cases abound, ML teams face growing challenges around how to build, deploy, and maintain their models. Practitioners demand the flexibility to optimize each component of a model and, like software development, use specific tools to address the various phases of the model development lifecycle. In an attempt to bridge the development gap in ML, a new framework called MLOps has emerged. MLOps is derived from core DevOps principles and represents one of the fastest growing markets in technology today.
To date, MLOps has largely centered on data preparation, model training, and model deployment; however, building and deploying models is only the beginning of the journey. What happens once a model is running in production? The reality is most companies still lack the necessary tools to scalably monitor and understand their live models. Moreover, as these models become more complex, troubleshooting issues gets harder and both upstream and downstream problems compound.
In order for AI to achieve long-term sustainability, companies must improve model transparency, understanding, and performance. Enter Arize, a ML observability platform built by practitioners to help unpack the proverbial AI black box and optimize the performance of models in production.
Shaping the future of AI infrastructure
As the various components of the model lifecycle standardize, many companies have started to orient away from expensive in-house builds and less flexible integrated platforms. Instead, practitioners are opting for best-of-breed MLOps solutions from focused vendors like Arize to gain additional control over their modeling workflow.
Arize sets itself apart as one of the few platforms that sits at the center of production ML. Within MLOps, there has been a flood of investment in tools addressing data preparation all the way through to the model deployment stage, but few tackle the reality that all live models face over time: degradation. Without real-time monitoring and observability, ML teams spend countless hours pouring over anomalies and trying to understand problems in the data, software, and / or model itself. Arize’s observability platform seamlessly plugs into any MLOps stack and provides a scalable solution to monitor the performance of models, explain what the models are trying to do, and diagnose data and drift issues without going back to square one.
In speaking to Arize’s customers, which include many of the world’s most sophisticated ML teams, it’s clear observability is seen as a core pillar of AI infrastructure and represents a natural progression in how they think about model lifecycle management. There’s a reason adjacent observability solutions like Datadog and Monte Carlo exist in other areas of IT, and we believe ML will be no different.
Built by practitioners
Arize’s founders, Jason Lopatecki and Aparna Dhinakaran, first met at TubeMogul, where Jason was a founder who helped build out the company’s ML team and Aparna was a data scientist. Jason would eventually guide TubeMogul through a successful IPO and sale to Adobe while Aparna went to work for Uber as part of its famed Michelangelo team.
Jason and Aparna stand out in a MLOps space where many founders hail from academia. Both draw from deep practitioner roots and have experienced firsthand the heartache of spending months building and training models, deploying them to production, and having no insight into how the models actually performed once deployed. Independently, they came to the conclusion that something was fundamentally missing in the MLOps toolchain. Together, they are now focused on bringing transparency, understanding, and performance to production ML through Arize’s dedicated observability platform.
Our partnership with Arize
We’re thrilled to announce that TCV has led Arize’s $38M Series B alongside our friends at Battery Ventures, Foundation Capital, and Swift Ventures.
At TCV, we gravitate towards founders that are culture and product obsessed. Jason and Aparna blend multi-stage company-building experience with firsthand knowledge of a real customer pain point. We’re incredibly excited to partner with the Arize team on their mission to make AI work and work for the people. If you are interested in joining Arize for the journey ahead, please visit their website to learn more about current career opportunities.