It seems like just yesterday that your data science team was a handful of people, actively having to seek out cool use cases for experimentation. Now, like many companies, you likely have the opposite problem. Teams across the organization are asking “how can AI help me?” and you have a long list of backlogged requests with the hope of finding new sources of revenue or cutting costs. As your demand grows, you may consider investing in a data science platform.
What Is a Data Science Platform?
Gartner defines a data science and machine learning (DSML) platform as a “core product and supporting portfolio of coherently integrated products, components, libraries and frameworks (including proprietary, partner-sourced and open-source).* A data science platform is designed to create efficiencies in operations related to ML and data science projects, facilitate better collaboration between data scientists and stakeholders, and ultimately create controls and build trust in data.
When Should You Consider Investing in a Data Science Platform?
Here are three key reasons to consider a platform:
1. Your Experts Are Overwhelmed
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You need to distribute work across the team: Your expert data scientists are in high demand, but notoriously difficult to hire and retain. A data science platform should empower you to offload some ML tasks (like simple regression with AutoML) to up-and-coming citizen data scientists (read: domain experts) so that your experts focus on areas where their expertise can most help.
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They need to spend less time on data management: Often data scientists spend the majority of their time on data management tasks — they may spend hours of back and forth simply to gain access to needed permissions to access data, or crafting code to clean and prepare data. A data science platform should both make it easier to access needed data sources, while embedding automation and reusability to reduce time spent on data prep.
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They need to spend less time on code overhead and model maintenance: Once models are in production, data scientists have to maintain them. This means resolving conflicts with dependencies and debugging, or monitoring models in production to ensure data drift is quickly addressed. Data science platforms should have built-in monitoring and alerts so that data scientists can quickly address issues before they become bigger challenges, and make it easy for data scientists to make updates in tools and frameworks they’re most comfortable with.
2. You Have Trouble Scaling Data Science Operations
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You need better visibility and governance: Often when the number of ML models scales, it becomes difficult to keep track of all the projects in production and ensure good MLOps and AI Governance best practices. A good data science platform should have built-in features to ensure visibility across all ML projects, including audit trails, robust monitoring, and appropriate documentation.
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You need standardization and reuse: Often data scientists complain that they can’t reuse work from six months ago because they can’t find it, the employee has left, or the context for previous projects is missing. A data science platform has various methods of reusing past work through things like an integrated feature store, labeling and searching for projects, and auto-documented data flows.
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You need better communication across teams: From gaining data access from IT or the business, to analyst preparation of data, to ML development, to getting stakeholders comfortable with models before deployment, data science involves several handoffs of communication that can be complicated when teams can’t collaborate in the same tools and spaces. With a data science platform, you can bring everyone together in a centralized location to facilitate optimal collaboration and remove silos.
3. You Are Struggling to Show Results (or Are Seen as a Cost Center)
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You need to get more work into production: Often production may be slowed down due to lengthy deployment processes, IT concerns around model cost or misalignment with tools used, lack of model explainability, or lack of business stakeholder understanding. A data science platform will speed deployment, ensure optimal costs through elastic compute, and have built-in tools to facilitate stakeholder alignment and communication.
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You need to focus on the right projects: How do you prioritize the right projects based on business impact? A good data science platform will have methods to prioritize and gain consensus around ML projects so that you always focus on the projects with the strongest business impact.
Dataiku: A Data Science Platform Built for Everyday AI
Dataiku is a data science and analytics platform built to empower teams to expand data science to more people across the organization, get more work done, and easily scale for better results. With all the features above and more, it’s designed to work with your current technology investments while adding layers of collaboration, acceleration, and trust to your AI projects.
*Gartner - Data Science and Machine Learning Platforms Reviews and Ratings, https://www.gartner.com/reviews/market/data-science-and-machine-learning-platforms