MLOps Innovator Series: The Role of Synthetic Data for Data-Centric AI
Data-centric AI is a paradigm shift on the horizon. MLOps teams are recognizing the weaknesses of focusing entirely on the functionality of their models and seeing the importance of treating their data as first-class citizens in the machine learning life cycle.
Yet, alongside the realization above, emerge new challenges. How do you ensure that your data is of high quality? What if you don’t have enough data? In this webinar with Fabiana Clemente of YData, we explore the answers to these questions and the role synthetic data plays in the solution.
The data-centric approach to AI recognizes the importance of data and the challenges that come along with it. Despite the increased adoption of AI in many fields, studies suggest that many projects never make it to production. One underlying cause of this failure rate is the lack of the right data and access.
Working with data can be complicated, with different privacy and security requirements. Cleaning and organizing raw data can be arduous. Datasets are typically labeled manually, a cost and time-intensive endeavor. By leveraging synthetic data, organizations can fill the gaps and deliver results at a fraction of the cost and time needed to generate real-world data.
Synthetic data are artificially generated data that an organization can utilize as an alternative to authentic data in training AI models. Because this data is generated, data scientists control every facet of it, enabling them to create on-demand, scale operations, and streamline processes. This optimization can drive model performance, resulting in more accurate results.
There are several use cases of synthetic data in the business field. For example, the financial sector utilizes synthetic data to improve fraud detection and risk analysis. It’s also used to drive innovation without compromising user data privacy. Using synthetic data, healthcare and financial companies eliminate the risk of compromising customer and patient confidentiality.
As the industry recognizes the need to shift to data-centric AI, synthetic data is a much-needed organizational ally. The power of synthetic data makes it possible to create higher-quality AI models in the future.
Pachyderm: The Foundation You Need for a Data-Centric Approach to AI
The use of synthetic data in the industry highlights the need to ensure data quality. Organizations can stay ahead and start adopting the data-centric approach to AI by leveraging the power of Pachyderm. Pachyderm’s data-driven ML pipelines and data versioning can help your MLOps team scale their operations, automate processes and evaluate the impact of data on model performance. See Pachyderm in action by requesting a demo today!
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