Bridging the Gap from Data to Value with ML Workload Orchestration
June 6, 2023As enterprises accelerate their investments in AI and ML, demanding higher and faster ROI from these investments. JP Morgan, for example, expects $1.5 billion in incremental value in AI/ML driven business impact1.
However, most organizations attempting to achieve value from their ML efforts soon find bottlenecks preventing them from realizing the ROI they seek.
Common bottlenecks include
- Data scientists and ML engineers spending too many cycles on the data plumbing and scheduling needed just to get models to run and produce business reports – often because they are using tools (e.g. Airflow) not designed for ML workloads.
- Data engineers are spending many cycles supporting data scientists as they try to run and analyze ML pipelines instead of building robust upstream data pipelines to ensure business continuity.
- The resulting, do-it-yourself workarounds are not repeatable, scalable, or easy to manage.
This means AI/ML initiatives can’t scale with the available resources and enterprises can’t deliver ROI, let alone respond quickly to market changes.
While home-grown, ad-hoc solutions might be manageable for a handful of use cases, as enterprises scale their models to 3X or 5X the initial deployment, they create weeks-long feedback loops from model deployment to business insight, all while increasing the need for engineering time. The lack of repeatability and efficiency of production ML workflows creates unnecessary costs, delays, and unsatisfactory performance of the ML initiatives.
The ideal solution is easy-to-use tools that enable repeatable processes, simplifying and automating production ML pipelines so enterprises can get to business value fast because AI teams are focused on business outcomes, not plumbing.
Wallaroo ML Workload Orchestration
That’s why Wallaroo.AI created robust, low-code ML Workload Orchestration capabilities for our Enterprise Edition software. Now enterprises have an easier way to address a very common set of challenges that AI teams face with production ML workloads.
Workload Orchestration provides the ability to easily define, automate, and scale recurring production ML workloads that ingest data from predefined data sources, runs inferencing, and deposits the results to a predefined location. Continuous observability means teams can run inference via multiple ML pipelines and send the results to predefined destinations, ensuring a tight feedback loop from data to business insights.
Customers with early-access to the Wallaroo ML Workload Orchestration features have seen significant benefits. Their data scientists and ML engineers freed up an average of nearly 40% of their weekly time that were previously devoted to basic tasks, allowing them to shift their focus to delivering and scaling additional, value-generating initiatives. In one case, the client had a 4-8x time savings, when you factor in the team size. This meant the client was able to potentially take on 4-8x more projects that drove value to the business.
This was possible because, by integrating the Wallaroo platform in their data & AI ecosystem, AI teams can automate recurring production ML workloads where data and compute orchestrations are necessary to ensure faster cycles along the entire ML production process, from data ingestion for running inferences to analyzing business outcomes.
Stand-alone orchestration tools (such as Airflow and Conductor) are designed more for building bespoke data engineering pipelines (e.g ETL jobs) than the unique needs of machine learning workloads. Wallaroo’s ML Workload Orchestration capability, however, is purpose-built for production ML workloads.
It features full integration into data ecosystems (cloud datastores and AI notebooks), secure data connections and authentication management, as well as automation and scripting for end-to-end production ML operations. This is possible because, unlike point solutions, cloud-based products, or home-grow tools, Wallaroo is a unified enterprise-ready ML platform built from ground up with all facets of the ML production process already in mind.
Wallaroo.AI customers can accelerate the process of going from data to value by launching ML initiatives in production with no infrastructure bottlenecks, leading to 5-10x faster ML time-to-value combined with 4-8x optimized compute consumption for high-volume inferences.
Wallaroo is architected to solve and future-proof real world ML operations challenges with:
- Distributed computing and low latency to achieve optimal performance for ML inference.
- User-centered design to abstract away unnecessary infrastructure complexity for data scientists and ML engineers.
- Flexible and modular architecture to enable operationalizing ML in any type of environment (cloud, edge)
- API-based architecture to make the platform extensible to enterprise processes, tools and standards.
Interested in learning more? Speak to an expert.
1 – https://twitter.com/edsim/status/1660707354701361152
