If you are trying to scale AI/ML inside the enterprise, end-to-end MLOps solutions are not the answer
While theoretically end-to-end (or “all-in-one”) MLOps platforms provide data wrangling, model development, and deployment in a single platform, unless you are willing to undergo a complex and lengthy migration to move business teams from different data platforms to a single platform, each separate platform will require its own dedicated ML engineering resources for production ML (figure 1).
Now consolidation of data platforms seems like a good idea on its face because ITOps will always prefer fewer vendors to support versus more, but it’s important to understand why most enterprises do not have a single, unified data platform.
In general, enterprises build up different data lakes, data warehouses, and (more recently) lake houses over years as different business units have different data and tech requirements for their different use cases. This is why in large enterprises, in particular, data science is decentralized to the different business functions (e.g., finance and product teams each have their own data scientists reporting up through the business rather than through a central data science team).
Each of these different data science teams in turn usually have their own preferred model training framework based on the use cases they are solving for, meaning a one-size-fits-all training framework for the entire enterprise may not be tenable.
From an ITOps perspective, it was still manageable to support different data warehouses for basic BI analytics since these data warehouses would not require active manual intervention once set up. However, the advent of machine learning has made these arrangements untenable since operationalizing ML requires additional ongoing intervention to spin up a new inference endpoint every time a new model is deployed, even with solutions providing a Python SDK for deploying models, such as Amazon SageMaker.
Even centers of excellence in the enterprise tasked with operationalizing ML will have to hire and train ML engineers who specialize in each of the separate all-in-one MLOps platforms. Considering job openings for ML engineers are growing 30x faster than IT services as a whole, these are among the hardest to find and most highly compensated roles. This then becomes one of the primary bottlenecks preventing applied AI/ML from scaling across the enterprise and making the investment in data science profitable,
How can Wallaroo help?
Unlike end-to-end MLOps platforms, whether from the cloud providers (e.g., SageMaker from AWS, Vertex from GCP) or SaaS vendors (e.g., Databricks, DataRobot), at Wallaroo…
- We are agnostic about where the models were trained or where they will be deployed
- You can use the same SDK to deploy to a variety of environments
- You get far better production inference performance than any other solution (up to 12x faster inference on 80% less compute)
- We are hyper-focused on being as simple and powerful as possible
As a result, enterprises using Wallaroo for their last mile of ML get a single, standardized platform to deploy, run, and observe machine learning models in production that fits the reality of the modern enterprise ecosystem (figure 2).
Wallaroo is designed to blend into your ecosystem and seamlessly connect with everything around it. Our Connector Framework neatly plugs our platform with your incoming and outgoing data points and takes care of the integration to get you up and running in no time (figure 3). We can integrate with your existing authorization/security mechanisms, log analysis systems, alerting systems, deployment, and release management workflow, as well as assist with QA and governance.
To see how Wallaroo can help scale your MLOps without requiring additional headcount, reach out to us at deployML@wallaroo.ai.