Overcoming the challenges of implementing ML today
Data growth is both an opportunity and a challenge, and organizations are looking to extract more value from their data. Line-of-business users, data analysts, and developers are being called upon to use this data to deliver business outcomes. These users need easy-to-use tools and don’t typically have the skill set of a typical data scientist nor the luxury of time to learn these skills plus being experts in data management. Central IT departments are overwhelmed with analytics and data requirements and are looking for solutions to enable users with self-service tools delivered on top of powerful systems that are easy to use. Following are some of the main challenges:
- Data is more diverse and growing rapidly. We have moved from analyzing terabytes to petabytes and exabytes of data. This data typically is spread across many different data stores across organizations. This means data has to be exported and then landed...
