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Vid Jain, founder and chief executive officer of Wallaroo.AI said "Going from ML prototype to production is a huge challenge and even when enterprises succeed with ad hoc approaches, most don't have the efficiency, flexibility, or repeatability in their processes that they need to scale their ML". With these new features, enterprises can now be data-source agnostic and ensure business continuity with portable ML pipelines that move from development through to production.
The Wallaroo.AI platform now includes support for data connections across the three major cloud datastores - Google Cloud, Amazon Web Services, and Microsoft Azure - as well as Wallaroo SDK and Wallaroo API support. Enterprises can now ingest data from predefined data sources to run inferences in the Wallaroo.AI platform, chain pipelines, and send inference results to predefined destinations to analyze model insights and assess business outcomes.
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The new technology orchestrates scheduling and infrastructure utilization, data gathering and inferencing while ensuring resilience. Teams can then monitor workloads and review results as needed with just a few lines of code if they use the Wallaroo.AI platform with the new Workload Orchestration features. The platform also includes support for custom/arbitrary python scripts and chained ML models and pipelines for automation purposes.
Wallaroo's customers have reported that this unique capability has enabled them to scale their ML workflows by 5-10x while also freeing up 40% of their weekly time due to removing unnecessary steps which accelerates feedback loop from model deployment to business value so organizations can troubleshoot and tune models more quickly to respond to unsatisfactory performance of the model or market changes.
Security is also ensured with authentication management at the platform level for all data connections used within workspaces where ML Workload Orchestration management lives.
Overall this new feature set provides an easy-to-use fully integrated experience which minimizes operational overhead so organizations can scale quickly and efficiently while responding more quickly to market changes or unsatisfactory performance of models deployed in production environments.
Filed Under: Business
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