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RudderStack isn't just an alternative to Segment, but a different approach for businesses who want to turn their customer data into a competitive advantage.View the full article

There is no AI without data Artificial intelligence is the most exciting technology revolution of recent years. Nvidia, Intel, AMD and others continue to produce faster and faster GPU’s enabling larger models, and higher throughput in decision making processes. Outside of the immediate AI-hype, one area still remains somewhat overlooked: AI needs data (find out more here). First and foremost, storage systems need to provide high performance access to ever growing datasets, but more importantly they need to ensure that this data is securely stored, not just for the present, but also for the future. There are multiple different types of data used in typical AI systems: Raw and pre-processed data Training data Models Results All of this data takes time and computational effort to collect, process and output, and as such need to be protected. In some cases, like telemetry data from a self-driving car, this data might never be able to be reproduced. Even after training data is used to create a model, its value is not diminished; improvements to models require consistent training data sets so that any adjustments can be fairly benchmarked. Raw, pre-processed, training and results data sets can contain personally identifiable information and as such steps need to be taken to ensure that it is stored in a secure fashion. And more than just the moral responsibility of safely storing data, there can be significant penalties associated with data breaches. Challenges with securely storing AI data We covered many of the risks associated with securely storing data in this blog post. The same risks apply in an AI setting as well. Afterall machine learning is another application that consumes storage resources, albeit sometimes at a much larger scale. AI use cases are relatively new, however the majority of modern storage systems, including the open source solutions like Ceph, have mature features that can be used to mitigate these risks. Physical theft thwarted by data at rest encryption Any disk used in a storage system could theoretically be lost due to theft, or when returned for warranty replacement after a failure event. By using at rest encryption, every byte of data stored on a disk, spinning media, or flash, is useless without the cryptographic keys needed to unencrypt the data. Thus protecting sensitive data, or proprietary models created after hours or even days of processing. Strict access control to keep out uninvited guests A key tenet of any system design is ensuring that users (real people, or headless accounts) have access only to the resources they need, and that at any time that access can easily be removed. Storage systems like Ceph use both their own access control mechanisms and also integrate with centralised auth systems like LDAP to allow easy access control. Eavesdropping defeated by in flight encryption There is nothing worse than someone listening into a conversation that they should not be privy to. The same thing can happen in computer networks too. By employing encryption on all network flows: client to storage, and internal storage system networks no data can be leaked to 3rd parties eavesdropping on the network. Recover from ransomware with snapshots and versioning It seems like every week another large enterprise has to disclose a ransomware event, where an unauthorised 3rd party has taken control of their systems and encrypted the data. Not only does this lead to downtime but also the possibility of having to pay a ransom for the decryption key to regain control of their systems and access to their data. AI projects often represent a significant investment of both time and resources, so having an initiative undermined by a ransomware attack could be highly damaging. Using point in time snapshots or versioning of objects can allow an organisation to revert to a previous non-encrypted state, and potentially resume operations sooner. Learn more Ceph is one storage solution that can be used to store various AI datasets, and is not only scalable to meet performance and capacity requirements, but also has a number of features to ensure data is stored securely. Find out more about how Ceph solves AI storage challenges: Find out more about Ceph here. Additional resources What is Ceph? Blog : Ceph storage for AI Webinar : AI storage with Ceph White paper – A guide to software-defined storage for enterprises Explore Canonical’s AI solutions View the full article