Expanding on MLOps for the Enterprise
Organizations have realized that even if they have implemented some level of MLOps, there are still things standing in the way of safely and universally scaling data science.
- Inflexible Infrastructure. Data scientists are unproductive without access to powerful compute, high-value data, and the latest open-source tools. Even worse, time spent on DevOps tasks with bespoke tools and hardware reduces innovation. Many surveys have established that data scientists work with data and infrastructure 80% of their time, leaving little bandwidth for analysis and insights.
- Wasted Work. Data scientists often work independently and with many different tools. Low standardization and visibility of work create duplicate effort, barriers to collaboration, and poor reproducibility. A recent Forrester survey of 467 enterprises found that 39% of respondents claimed IT and developers "don’t collaborate at critical stages of the AI journey if they ever collaborate at all.”
- Production Pitfalls. Recent Gartner research shows that only 53% of projects make it from AI prototypes to production. Many data science models stop performing well in production due to issues such as data drift. The lack of repeatable processes from deployment to monitoring adds hidden costs and unnecessary complexity, delays and compliance risk.
Tackling these three challenges requires a discipline that looks beyond the deployment portion of the data science lifecycle, which is where MLOps platforms have focused to date. It requires enterprise-grade capabilities that allow projects to progress through the end-to-end data science lifecycle faster and provides for safely and universally scaling data science with the requisite security, governance, compliance, reproducibility, and auditability features. For these reasons, leading organizations are adopting Enterprise MLOps practices and enabling platforms.
Capabilities of an Enterprise MLOps Platform
An Enterprise MLOps platform needs to serve the requirements of all of the different members of the MLOps team, the organization's management, its workflows and lifecycles, and the continued growth of the organization as a whole. Enterprise MLOps capabilities can be thought of in two ways: tooling enhancements and process transformations.
Tooling enhancement capabilities include:
- On-demand access to data and scalable compute
- On-demand access to centralized tooling
- User access control and security
- Version control and reproducible research
These capabilities dramatically increase productivity for data science and IT teams as well as provide storage and organization of all data science artifacts including data sources, data sets, and algorithms for reproducibility and reusability. They allow IT to manage infrastructure and costs, govern and secure technology and data, as well as enable data scientists to self-serve the tools and infrastructure they need.
Process transformation capabilities include:
- End-to-end orchestration of the data science lifecycle
- Project management
- Knowledge management and governance.
These capabilities are what allow organizations to safely and universally scale data science by making the most efficient use of resources, building on prior work, providing context, and enhancing learning loops. Everyone uses consistent patterns and practices regardless of how or where the model was developed. All together they eliminate manual, inefficient workflows across all the activities of the data science lifecycle creating momentum that increases model quality, reduces the time required to deploy successful models from months to weeks, or days, and instantly notifies of changes in model performance so models can be quickly retrained or replaced.
Everyone learns from the successes and failures. Collaboration also includes engaging with the business in a non-technical manner so they can understand the projects and outcomes. Finally, data science leaders can easily manage workloads and track project progress, impact and cost.
When these tooling and process transformation capabilities are all available, an Enterprise MLOps platform optimizes the throughput across the data science lifecycle, driving more models from development into production faster, while keeping them at peak performance and providing the tools and knowledge needed to repeat the cycle.
Core Components of the Domino Enterprise MLOps Platform
The Domino Enterprise MLOps platform is feature-rich and designed to handle the needs of model-driven organizations using state-of-the-art data science tools and algorithms. The platform provides three critical functions for modern data science teams:
As a system of record, Domino captures all data science work in a central repository, so your team can easily find, reproduce and reuse work. Gone are the days of data scientists starting projects from scratch only to find out another team member is working on the same problem. Instead, knowledge is compounded with reusable code, artifacts, and learnings from previous experiments, integrated project management capabilities, and the ability to replicate development environments.
As an integrated model factory, Domino supports the end-to-end data science lifecycle from ideation to production: explore data, train machine learning models, validate, deploy, and monitor. Then rinse and repeat – all in one place. Enable repeatable processes and workflows that get models into production faster, enable automated monitoring, retrain and republish models more often, and much more – all designed to reduce friction and increase model velocity on your way to becoming a model-driven business.
And finally, as a self-service infrastructure portal, Domino automates the time-consuming DevOps tasks required for data science work at scale. With only a few clicks you can spin up a development sandbox pre-loaded with your preferred tools, languages, and compute, including popular distributed compute frameworks. Jump between environments, bring in more data, compare experiments, deploy and iterate on models, and just be more productive with a platform optimized for code-first data science teams.
Benefits of Domino's Enterprise MLOps Platform
Customers who have adopted the Domino Enterprise MLOps platform consistently point to three primary reasons that have allowed them to effectively scale data science:
Open & Flexible
Domino supports the broadest ecosystem of open-source and commercial tools and infrastructure. Unlike SageMaker which is AWS-specific, or Databricks which is tied to Spark, Domino is an open system. Domino’s unique architecture supports on-premise, cloud and hybrid environments for maximum flexibility. Domino supports the latest tools, packages, and compute frameworks such as Spark, Ray, and Dask.
Built for Teams
Domino is designed for data science at scale. Teams using different tools can seamlessly collaborate on projects and rely on Domino to automatically track all data science artifacts. Domino establishes full visibility, repeatability, and reproducibility at any time for every use case. Dashboards let managers set project goals and inspect in-flight work.
Domino integrates workflows to accelerate the end-to-end data science lifecycle from experimentation to production. For example, Domino automatically sets up prediction data capture pipelines and model monitoring for deployed models to ensure peak model performance. Domino’s integrated approach ensures everyone involved in data science can maximize their productivity and impact.
The Model-Driven Future with Domino Data Lab Enterprise MLOps
In just a few short years, data science has brought us self-driving cars, risk analysis engines, Alpha Go, movie recommendation engines, and even a photorealistic painting app. Where data science takes us from here is anyone's guess (specifically, an innovative and well-researched guess).
The companies that scale ML innovation over the next decade will be those that are model-driven, making money on their projects, building on each subsequent success, learning faster, developing more efficiently, reducing costs, and minimizing poor outcomes.
Does your company strive to become model-driven? Work with Domino Data Lab to ensure your company's success. To see the Domino Enterprise MLOps Platform in action, you can watch a demo or try it for yourself with a free trial.