Fig 1: Mentions of AI in corporate earnings calls, 2011–2020

ROI in AI: It’s not just about the model

  • Repeatable: The most common complaint we hear from customers is that standing up new AI use cases feels like reinventing the wheel each time. The more data teams can rely on standardized and automated processes, even across different data environments and use cases, the more willing different business functions will be to test and adopt AI into their own operations. But this will require MLOps that can regularly accomplish in seconds what previously required weeks/months — so for example, can you quickly and easily conduct data access audits for compliance in such highly regulated industries like financial services or healthcare & life sciences?
  • Scalable: If your compute costs and headcount increase linearly as you increase your AI, you will often lose any advantages gained from AI. Running (aka, inferencing or scoring) is server intensive with live data pipelines. It can become cost prohibitive as you scale up the number of models in production, or analyze big data, or manage complex models like neural networks. Additionally, the first instinct when ML investments fail is to throw more headcount at it — more data scientists or ML engineers. But we actually take a different approach — what tools can you use so that you need less headcount for the intense but simple work that should be automated?
  • Measurable: For continued buy in, leaders need to evangelize how returns from AI take different forms such as cuttings costs (e.g., reducing OpEx and CapEx through better predictive maintenance), incremental revenue (e.g., reaching more profitable customers through improved segmentation), or loss avoidance (e.g., preventing costly security breaches). What’s key is that these are goals for the broader business — not just the IT organization, which primarily measures ROI through cost or risk reduction.

Success with AI needs executive engagement

Fig. 2: High level overview of the ML lifecycle
  • Is it an upstream data engineering issue around being able to ingest and process the data needed for the problem we are looking to solve? E.g., if we are perhaps looking to build a predictive maintenance model, are our data pipelines set up for streaming IoT sensor data?
  • Or is it a midstream issue around the difficulty developing ML models? E.g., do we have data scientists with the right industry expertise to not just pull a demand forecasting model off the shelf but ones that understand the seasonal dynamics to our products?
  • Or is the bottleneck downstream in the inability to replicate the performance of models in a dev environment in production or else the inability to scale up the processes for deploying, monitoring, and managing dozens or even hundreds of models at once?
  • How repeatable or efficient are the processes at each stage in this lifecycle? What can I automate? What does this cost me in people and time if I go from 1 model to 5 to 20?

The Last Mile of the the ML Lifecycle

  • Deployment: Deploy models with a single line of code. Wallaroo provides a high level Python SDK and lower level APIs to give you the widest range of integration options for your model deployment strategy, all from the convenience of your familiar tools and workflows.
  • Running/Inferencing: Scale to handle more models, more data, or more complexity with speed and ease. Wallaroo’s advanced resource management ranges from manual, to basic, to advanced autoscaling.
  • Observability & Insights: Monitor and quickly identify any sources of model performance degradation. Additionally the Wallaroo platform provides simplified monitoring capabilities, with full auditing view into who accessed which model and where.
  • Optimization: Update models without downtime to the business. Easy capabilities for A/B testing and experimentation.
Fig 3: Wallaroo focuses on the last mile of ML — deploying, running, observing and optimizing ML models in production

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