Production AI made simple at any scale and now compute 12.5X faster with an 80% lower cost: Wallaroo

Founded in 2017, Wallaroo is a group of veteran software and data engineers. Together, Wallaroo has got more than a century of distributed computing, open-source development and low-latency systems engineering experience. The Wallaroo platform makes it simple, fast, and very low cost to get AI algorithms live against production data. The company’s platform is built on four core components: MLOps, Distributed Processing Engine, Data Connectors, and Audit and Performance Metrics. Wallaroo is designed to enable data scientists to quickly and easily deploy their ML models against live data, whether to testing environments, staging, or prod. Wallaroo supports the largest set of machine learning training frameworks possible. You’re free to focus on developing and iterating on your models while letting the platform take care of deployment and inference at speed and scale.

Wallaroo’s underlying distributed data processing engine provides a highly scalable, high performance environment for production model scoring, as well as pre- and post-processing. Wallaroo has built its engine from the ground up to add minimal overhead. It compiles to native code and can easily be deployed in a wide range of environments, whether cloud, on-premise, or at the edge. Scaling is as easy as adding or removing workers from the cluster. Wallaroo’s Data Connectors enable integrations with popular data sources and sinks (e.g. Kafka, Postgres, and S3), as well as custom integrations with your own in-house solutions. This allows Wallaroo to get data in to and out of a wide variety of systems. Wallaroo provides observability to data scientists, operations, compliance/risk teams, and business heads and finance leads. Wallaroo supports event-by-event audit logs, compute and model performance metrics, and A/B testing comparisons.

Machine Learning’s “last mile problem”

Data science is a modern-day superpower, and enterprises around the world know it. Over 90% of Fortune 1000 companies are investing in Big Data, analytics, and artificial intelligence (AI), reaching over $700 billion being poured into teams of data scientists and engineers to revolutionize the way they do business. Yet machine learning is hard, and the last mile of ML - getting the models into production to impact the bottom line - is especially hard. If businesses can’t do this easily or at scale, their AI initiatives will fail, resulting in significant costs in terms of budget, manpower, and disillusionment. According to Gartner, less than half of AI prototypes make it to production, and in the end, only about 10% generate substantial ROI. Deployment solutions — whether containerization, cobbling together various existing technologies, or customizing an analytics workhorse like Apache Spark — are cumbersome, limited in scope, expensive at scale, prone to failure, and unable to run ML models against batch and streaming data. With investments in AI only trending upwards, companies hoping to turn a profit with their data will never reach their full potential as the long deployment lead times and high cost to run and maintain the necessary infrastructure often outweigh the benefits.

Wallaroo is a breakthrough platform for the last mile of ML, providing a simple, secure, and scalable deployment capability that fits into your end-to-end workflow. Wallaroo gets your ML to business results faster, easier, and with a far lower investment. By streamlining the deploy/run/observe parts of an ML lifecycle and giving data scientists the freedom to use the tools they already know, Wallaroo enables your team to:

• Deploy models in seconds
• Analyze data up to 12.5X faster
• Reduce compute costs by 80%
• Iterate quickly and scale easily

Enterprises will often look to all-in-one MLOps platforms such as SageMaker, Databricks, or DataRobot to simplify deployment. However, these platforms force data teams to standardize on proprietary tools, processes, and formats. These tools will then lead to complexity as different business units within the same company might use different data platforms. One of Wallaroo’s customers, for example, is all-in-one on a certain cloud, but because of mergers & acquisitions, its data engineering teams are supporting different deployment processes for multiple clouds. In response, companies will spend countless resources building their platform in-house, cobbling together open-source technologies such as Spark and MLflow, which might work within the current ecosystem but at the expense of performance and model observability.

Wallaroo is designed to click into your ecosystem and seamlessly connect with everything around it. Wallaroo provides a standardized process that ML engineering teams can use to deploy, run and observe models across platforms, clouds, and environments (in the cloud, on-premises, or at the edge). Wallaroo’s Connector Framework neatly plugs its platform with your incoming and outgoing data points and takes care of the integration to get you up and running in no time. Wallaroo is a platform that enables the future of AI and analytics Wallaroo always wished it had: one where cutting-edge AI and ML can be deployed in seconds, and data teams can deliver a higher value at a lower cost. Wallaroo has built it so your team can spend less time making your data work with your software, and more time making your data work for your business.

Vid Jain, Founder and CEO

“Wallaroo facilitates the last-mile of your machine learning journey, getting ML into your production environment to impact the bottom line, with incredible speed and efficiency.”