Wallaroo.AI, a leader in scaling production machine learning (ML) on premise, in the cloud, and at the edge, announced early access of ML Workload Orchestration features in its unified production ML platform. This unique capability facilitates automation, scheduling and execution of combined data and ML inferencing workflows across the production process, enabling AI teams to scale their ML workflows by 5-10x while also freeing up 40% of their weekly time, based on customer data.
Data scientists, data engineers, and ML engineers no longer need to waste time trying to set up the basic elements of data and ML pipelines with unwieldy tools. Removing these unnecessary and time-consuming steps accelerates the feedback loop from model deployment to business value so organizations can troubleshoot and tune models to respond more quickly to unsatisfactory performance of the model and market changes.
With these ML Workload Orchestration features, enterprises can now also be data-source agnostic, ensure business continuity with portable ML pipelines that move from development through to production, and scale ML use cases.
As enterprise AI teams upload their models, they can now define their ML workload steps and set up a schedule with just a few lines of code, if they use the Wallaroo.AI platform with the new Workload Orchestration features. Behind the scenes, this new technology orchestrates scheduling and infrastructure utilization, data gathering and inferencing while ensuring resilience. Teams can then monitor workloads and review results as needed.
Learn more at www.wallaroo.ai.
