For Computer Weekly, I am writing a feature looking at the storage requirements for AI, machine learning and analytics technologies.
This will include:
the demands these technologies make on storage infrastructure
The types of storage are best for the varying workloads in these areas (file, block, object, cloud
What storage vendors recommend for AI/ML/analytics use cases, and by workload
The deadline for leads is Wednesday, 16 March, with interviews the week after.
Please get in touch via the usual email address.
For Computer Weekly, I am looking into this developing area of enterprise applications and workflows.
A brief outline is below.
In the first instance, I am keen to hear from experts in the field. Please email with your credentials and background in RPA, and links to relevant research or case studies. I will then follow up with a questions or an interview request.
Click here to email
, no later than Friday 12th March.
Robotic process automation promises to seamlessly handle arduous workflows, linking disparate business processes, which normally require human intervention. Simpler process flows can be automated this way but there are few manual processes that only require someone rekeying information into systems that should really have been more tightly integrated. There is a level of intelligence, which cannot easily be shifted to a machine. While RPA is deterministic, an AI is probabilistic. We look at how RPA and bots that follow predetermined scripts are being made more intelligent.
For Computer Weekly my next feature will look at the specific demands placed on storage architecture by artificial intelligence, machine learning, and analytics.
The piece will ask:
What different approaches to providing storage are there for these technologies?
What limits, performance considerations and bottlenecks exist with the different approaches?
What ways of providing storage for analytics are we likely to see in future?
The article will cover both on-premises and cloud-based storage, where relevant. I’m keen to include some real-world use cases if possible.
I am open to comment from industry professionals, consultants, analysts and CIOs working with AI. ML and analytics.
Deadline for leads: 1700hrs BST, Tuesday 23 June. As ever, please email in the first instance.
I am writing two articles for Computer Weekly’s storage section, one on storage and data compliance for the enterprise, and the other on the growing field of high-performance object storage.
This piece will look at the top 5 UK compliance concerns in 2020.
What are the five key laws/regulations that must be adhered to by UK organisations in 2020, including both current and upcoming legislation. For each we will look at the implications of the law/reg for storage, backup, and archiving.
This could, for example, include legal search and e-discovery, or the Right to be Forgotten under GDPR.
We will also look at how the cloud fits in.
High performance object storage
Object storage has been known as a good way of storing lots of unstructured data, but with less emphasis on performance.
But AI and analytics workloads are prompting storage architects to look at performance too. The feature will cover:
- Where object storage is heading in performance terms and what’s driving it.
- Which performance metrics matter
- How have object storage vendors improved performance?
- Who are the key object storage vendors that are tackling the challenge of better performance and what do they offer?
The deadline for leads for both articles is Friday 20th March, please contact me by email if you can help.