Developers and modelers encounter challenges in finding and validating data, collaborating across groups, and transferring work to an enterprise platform.
Utilizing a self-service, on-demand compute environment for data analysis and machine learning models can enhance productivity and performance while reducing IT support and costs.
In this Q&A, Joe Madden, Senior Product Manager at SAS, explains the benefits of a workbench environment for developers and modelers.
Q: Why use a workbench?
Joe Madden: A developer workbench is a robust, cloud-native environment that streamlines analytical development, allowing for efficient and cost-effective experimentation, iteration, and deployment of models while ensuring data security and interoperability.
Q: How do developers and modelers benefit?
Madden: Developers and modelers benefit from increased efficiency, scalability, collaboration, and quality of results. The workbench simplifies the process of writing, testing, and optimizing code for machine learning models, facilitates seamless collaboration, and handles large data sets and complex computations effectively.
The workbench also provides tools for building, training, and evaluating models, enabling users to experiment with different algorithms and hyperparameters while ensuring reproducibility and transparency.
Q: What about access to data and data security?
Madden: Access to quality data is crucial and managed within the workbench environment to avoid data wrangling. Data management is centralized in the tool, ensuring that sensitive data remains secure behind a firewall, especially in regulated industries.
Q: How important is a multilanguage environment?
Madden: Flexibility is key, as developers and modelers may have different language preferences. Supporting a multilanguage environment in the workbench allows users to work with their preferred programming languages, enhancing collaboration and productivity.
Q: Can this tool work with generative AI?
Madden: Yes, the workbench can support generative AI by allowing users to generate code through natural language prompts. While this accelerates productivity, human intervention is necessary to fine-tune the model for meaningful results.
Q: Can you share how this could be applied in a real-world setting?
Madden: For instance, a retail company analyzing customer behavior can leverage the workbench to preprocess data, create features, and build predictive models to improve customer satisfaction and accelerate time to value.
Q: What about applications across industries?
Madden: The workbench has broad applications across industries, enabling users to control compute resources and scale based on project needs for greater efficiency and cost control.
Q: What about the next generation of users?
Madden: The customization and flexibility of the workbench appeal to the next generation of users, offering open-source integration and multilanguage support for diverse user needs, allowing users to self-provision environments and choose compute power for projects.
Faster, high-performance models deployed
A workbench tailored for developers and modelers accelerates model development and deployment in an on-demand, scalable, secure environment with trusted outputs.
