Today, competitive organizations have high demands for data science teams to deliver business impact. Can you meet and exceed those demands with your current tech stack? Can you manage teams across data science platforms? Can you easily work with AutoML AI platforms within your processes? If your answer to any of these questions is no, read on.
Champion Data Science Productivity
With Domino’s Enterprise MLOps platform you can say goodbye to backlogs and hello to proactively driving results.
- Your teams have the flexibility to use the data science tools they already use (e.g, Jupyter, RStudio, SAS, MATLAB, Spark) in an integrated environment that eliminates distractions so they can focus on developing and deploying models.
- You have the benefit of a single platform with holistic visibility and standardized processes so you get the information you need to manage teams and prioritize work more efficiently.
- You can develop a culture of collaboration and continuous learning to drive an ever-increasing flow of business value.
If you don’t have the right infrastructure - you’re lost - we really needed a ML platform. You need a version of code running, who deployed what, and freedom to choose what open source tools they want. They’re free to do what they need to do, and they never have questions about Domino - because they just get on with it. Like playing a piano, or a violin.
Data Science Challenges Restrict Scalability
Let’s face it, scaling data science is hard and that has serious implications. Productivity suffers. Models don’t get deployed. Outdated models make outdated predictions. In fact, a recent survey found that only 21% of businesses are gaining a major competitive advantage through the use of data and analytics tools across their enterprise. The problem? It’s not about finding the right data science tool… it’s really a matter of finding the right way to scale adoption of your data and analytics tools, to then elevate your competitive advantage.
There are three categories of data science challenges that get in the way of effective data science management:
- Lack of access to tools and infrastructure. Output is limited with lack of access to infrastructure and tools. Projects are delayed waiting for infrastructure and software provisioning. Risk spreads as unsanctioned tools and processes are used to overcome rigid requirements.
- Silos that inhibit knowledge sharing. Limited collaboration leads to duplicate work and diminished productivity. Data science work gets bottled and shelved, rather than shared and reused. There is difficulty managing your data science portfolio with limited visibility of work across teams.
- Complex processes to operationalize models. Manual work across the data science lifecycle slows progress and frustrates your data scientists. Onboarding of new employees onto teams and projects is slower with non-standardized processes. Monitoring models is difficult, reduces business value and trust in your models, and distracts data scientists.
Why Choose Domino?
Beating competition, driving unprecedented growth and finding opportunities in unforeseen places - while upending industries - will belong to the organizations that put models at the heart of their business.
Domino’s Enterprise MLOps platform - built for data science management at scale - is the change agent in your data science practice that will help you drive your transformation to a model-driven business. With Domino, leaders can finally spearhead pervasive deployment of models throughout their businesses and realize the benefits of data science at scale.
Domino differentiators for scaling data science management successfully:
- Openness & Flexibility: Future-proof your infrastructure to leverage new techniques and data science tools alongside your current tool set, while enabling IT to centralize and govern infrastructure on the open Domino platform.
- Reproducibility & Collaboration: Find past work, easily reproduce results, and freely collaborate to unlock new ideas and drive disruption with a single data science system-of-record.
- Model Velocity: Increase disciplined model velocity through consistent, integrated workflows across the data science lifecycle, independent of data science tools.
- Enterprise Scale: Centralize and orchestrate all data science work on one platform with enterprise grade security, governance, compliance and policies to scale safely and universally across your organization.
Domino’s MLOps platform delivers a 542% return on investment and is favored by over 20% of the Fortune 100. Domino can save tens of millions of dollars in costs, and has unlocked research that’s generated hundreds of millions in revenue. Read more in Forrester’s Total Economic Impact of the Domino Enterprise MLOps Platform.