From an early age, technology has had an extreme influence on the trajectory of my life. What started out as an interest in video games, such as Asteroids on Atari or Super Mario on Nintendo, has translated into a 35+ year career in the industry. My fascination continues to evolve with the field every single day.
Clearly, technology options are not the same now as they were when I was a child. Over the years, I have tried to keep tabs on all the “hot” innovations in order to better serve end-user organizations. In particular, the development of artificial intelligence (AI) and machine learning (ML) has piqued my interest.
Managing AI and ML Data Today
The last ten years of my career in data storage and management has led to several conversations around AI and ML — unsurprisingly so. From mapping the human genome to knowing which team’s jersey you would buy if you had a 10% off coupon, AI and ML is woven into the fabric of our everyday lives.
Most recently, AI has played a significant role in the battle against COVID-19. Government and healthcare officials across the globe used intense data analysis to determine hotspots and improve reaction times to develop strategies to mitigate outbreaks.
In order to compete in today’s increasingly digital world, it is absolutely essential that enterprises consider using solutions to help better digest AI and ML technologies’ data. Let’s take a look at why, and the challenges behind migrating this information.
AI and ML’s Role is Critical to Business Operations
As more and more organizations incorporate AI and ML projects into their budgets, we are starting to see significant changes in how organizations operate. In most cases, companies want to use the technologies’ data to reduce costs, boost overall efficiency, and improve the quality of work life for employees.
Data from AI and ML technologies can also be critical to how an organization develops products or services for customers. As a result, it is imperative that organizations work with vendors who know where the data should live and how to get it there.
For example, I recently had the chance to work with a research group that was building out their first data lake on object storage. The goal was to ingest north of a petabyte of genomic data (to start) into the object store for analysis for a cancer research project. They were looking to further develop and personalize cancer treatments. The data scientist explained to me that this technology could tell us which patients would react positively to chemotherapy and those upon which it would likely fail.
A big challenge with populating and refreshing datasets in a data lake is the actual transport of data from various source systems. Not only does the data need to be copied as quickly and efficiently as possible but it is of paramount importance that the data be completely accurate. Even trace amounts of silent corruption can negatively impact the results generated. That being the case the ability to leverage software that has core design elements rooted in data integrity along with scalability and performance is key. Datadobi provides the performance and verification capabilities required for efficient seeding and updating of these critical datasets.
Many organizations are following a similar trajectory as the above customer and choose to deploy their AI/ML data sets into the public cloud for analysis. Unfortunately, this presents additional challenges as IT administrators work to better understand data access patterns, the performance constraints they might encounter, and strategies required to manage the life cycle of the data that has been copied into the data lake.
With that said, Datadobi is the industry leader in unstructured data management. We developed the world’s first vendor-neutral unstructured data management and mobility engine and have been helping customers manage their unstructured data at scale since 2010.
With file level verification, advanced integrity protection, and chain of custody technologies, we can not only provide the fastest data transfer speeds in the industry but we can also verify the integrity of the data when it lands on the target. Accuracy, speed, scalability, automation, and ease of use are the key pillars of all Datadobi software offerings. These pillars are especially valuable when considering the management of data to populate and refresh datasets consumed by AI/ML oriented applications.
Want to Learn More About our AI and ML Solutions?
Check out how Datadobi can help transition AI/ML workloads into your public or private cloud storage environments by contacting one of our team members today.