“A data strategy with a scalable, real-time cloud data platform as a central pillar is key”
Data scientists need clean, up-to-date, and consolidated datasets to unlock the potential of machine learning. However, they often face challenges due to siled and stale infrastructure and data constraints. A data strategy with a scalable, real-time cloud data platform as a central pillar is essential to address these challenges.
To provide a roadmap, two industry veterans, Shaji thomas, Vice President of Cloud and Data Engineering at Ugam; and Swagata Maiti, Technical Architect of IP and Data Products at Ugam, joined us at Deep Learning DevCon 2021 (DLDC) to engage in a conference on the topic titled “To Data Preparation or Data Science “. That’s the question’. The duo delved into the topic and suggested seven techniques to help data scientists create a scalable data platform.
The presentation started with a basic question from attendees: How do data scientists spend the most time preparing data or building scalable ML models?
Over 90 percent of those surveyed nodded in agreement with Shaji that collecting and preparing data consumes a large part of their time. Several bottlenecks exist in the preparation or collection of data; This includes:
- Data silos and infrastructure constraints
- Inability to find the right data
- The repeated effort to engineer features
- The data is not clean
- Inability to handle streaming data
- Lack of data protection of personally identifiable information (PII)
- Testing and deployment is error prone
Scaling in Seven Steps
Despite the visible challenges, Shaji says that it is possible to have solutions to these problems, that too in a short period of time. Shaji said, âIt’s possible, as long as you or your organization has a strong data strategy in place, adopted a set of techniques that created a scalable data platform that could accelerate the entire cycle. life of data science. He further suggested having:
- A scalable cloud data warehouse that guarantees multiple benefits, such as a central data repository that can scale storage and compute separately, support copyless clones, provide full DevOps and data support third-party access.
- A data catalog guarantees a structured way of discovering data. Having a data catalog can improve productivity because it allows for rapid discovery of data, resulting in continuous updating of metadata and, finally, helping to get more context into the data.
- A feature store to be able to define, find, and reuse features. In addition, it helps to track model performance and feature drift.
- The automated data retention and validation process can help define business rules that can normalize data and keep it in the pipeline.
Speaking of continuous data ingestion, Swagata Maiti said, âAccording to research, only 40% of manufacturers use inventory management software, and the remaining 60% still rely on Excel or offline methods. As a result, on average, a lot of manpower is lost with great imprecision. In addition, large data sets become a daunting task for most organizations, hence by adapting streaming data ingestion, one can achieve a massively scalable, fault-resistant and highly available platform for real-time data streaming and complex problem handling in the cloud.
Last but not least, Swagata says there is a need to adopt hashing technology to protect PII data. It assists in the automatic removal of PII data from in-flight streaming systems and assists in the anonymization of customer data. The methodology used here is presented below.
It’s about understanding that a data science lifecycle is a series of data science steps that you take to complete a project or analysis. Because every data science project and team is unique, every data science lifecycle is also unique. From understanding business issues to collecting data, preparing data, modeling data and deploying data, all of these steps are of equal importance and should be considered.
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