The New Stack Podcast

Bryan Jacquot of HPE tells a story in this episode of The New Stack Makers about a company in the healthcare space he visited recently that has an application dating back to the early 1990s. Now this application from the big hair era faces competition from companies deploying more sophisticated, more modern services.

So what are these companies to do that have these relatively ancient software infrastructures that have less relevance than ever before? The challenge comes with how developers in larger companies that are largely building out monolithic technologies.  Part of the answer comes with building ancillary services. But that's just barely touching on the issue.

Bryan Jacquot of HPE tells a story in this episode of The New Stack Makers about a company in the healthcare space he visited recently that has an application dating back to the early 1990s. Now this application from the big hair era faces competition from companies deploying more sophisticated, more modern services.

So what are these companies to do that have these relatively ancient software infrastructures that have less relevance than ever before? The challenge comes with how developers in larger companies that are largely building out monolithic technologies.  Part of the answer comes with building ancillary services. But that's just barely touching on the issue.

Bryan Jacquot of HPE tells a story in this episode of The New Stack Makers about a company in the healthcare space he visited recently that has an application dating back to the early 1990s. Now this application from the big hair era faces competition from companies deploying more sophisticated, more modern services.

So what are these companies to do that have these relatively ancient software infrastructures that have less relevance than ever before? The challenge comes with how developers in larger companies that are largely building out monolithic technologies.  Part of the answer comes with building ancillary services. But that's just barely touching on the issue.

Bryan Jacquot of HPE tells a story in this episode of The New Stack Makers about a company in the healthcare space he visited recently that has an application dating back to the early 1990s. Now this application from the big hair era faces competition from companies deploying more sophisticated, more modern services.

So what are these companies to do that have these relatively ancient software infrastructures that have less relevance than ever before? The challenge comes with how developers in larger companies that are largely building out monolithic technologies.  Part of the answer comes with building ancillary services. But that's just barely touching on the issue.

Here’s something that probably doesn’t cross most developers’ minds:  In a distributed system whose components don’t share state with one another, how does one produce an application whose stated goal, if you will, is to create and maintain a state — specifically, something learned?

We already know that artificial intelligence routines such as decision trees don’t need to have state pre-assessed for them, to render results that seem rational enough.  Chess move algorithms, for example, don’t have to retain a concept of the active chessboard in memory, to rate the quality of a potential move.  They may not produce the best chess programs on the market, but they can evaluate moves, and they can beat amateurs.

Well, by definition, a machine learning application is expected to retain something — that’s what the entire “learning” part is supposed to mean.

“The thing that you really want to do from the get-go is, make a couple of decisions,” Bonsai co-founder and CEO Mark Hammond told The New Stack Makers.  “The first is going to be, are you going to have a system where online learning matters — that is to say, where the system needs to learn concurrently, with users utilizing the system?  Or can there be a lag in that?  Is it possible for you instead to record the data that has come in, as users use the system, and then learn on those recordings in a more batch-style operation layer?”

Here’s something that probably doesn’t cross most developers’ minds:  In a distributed system whose components don’t share state with one another, how does one produce an application whose stated goal, if you will, is to create and maintain a state — specifically, something learned?

We already know that artificial intelligence routines such as decision trees don’t need to have state pre-assessed for them, to render results that seem rational enough.  Chess move algorithms, for example, don’t have to retain a concept of the active chessboard in memory, to rate the quality of a potential move.  They may not produce the best chess programs on the market, but they can evaluate moves, and they can beat amateurs.

Well, by definition, a machine learning application is expected to retain something — that’s what the entire “learning” part is supposed to mean.

“The thing that you really want to do from the get-go is, make a couple of decisions,” Bonsai co-founder and CEO Mark Hammond told The New Stack Makers.  “The first is going to be, are you going to have a system where online learning matters — that is to say, where the system needs to learn concurrently, with users utilizing the system?  Or can there be a lag in that?  Is it possible for you instead to record the data that has come in, as users use the system, and then learn on those recordings in a more batch-style operation layer?”

Here’s something that probably doesn’t cross most developers’ minds:  In a distributed system whose components don’t share state with one another, how does one produce an application whose stated goal, if you will, is to create and maintain a state — specifically, something learned?

We already know that artificial intelligence routines such as decision trees don’t need to have state pre-assessed for them, to render results that seem rational enough.  Chess move algorithms, for example, don’t have to retain a concept of the active chessboard in memory, to rate the quality of a potential move.  They may not produce the best chess programs on the market, but they can evaluate moves, and they can beat amateurs.

Well, by definition, a machine learning application is expected to retain something — that’s what the entire “learning” part is supposed to mean.

“The thing that you really want to do from the get-go is, make a couple of decisions,” Bonsai co-founder and CEO Mark Hammond told The New Stack Makers.  “The first is going to be, are you going to have a system where online learning matters — that is to say, where the system needs to learn concurrently, with users utilizing the system?  Or can there be a lag in that?  Is it possible for you instead to record the data that has come in, as users use the system, and then learn on those recordings in a more batch-style operation layer?”

Here’s something that probably doesn’t cross most developers’ minds:  In a distributed system whose components don’t share state with one another, how does one produce an application whose stated goal, if you will, is to create and maintain a state — specifically, something learned?

We already know that artificial intelligence routines such as decision trees don’t need to have state pre-assessed for them, to render results that seem rational enough.  Chess move algorithms, for example, don’t have to retain a concept of the active chessboard in memory, to rate the quality of a potential move.  They may not produce the best chess programs on the market, but they can evaluate moves, and they can beat amateurs.

Well, by definition, a machine learning application is expected to retain something — that’s what the entire “learning” part is supposed to mean.

“The thing that you really want to do from the get-go is, make a couple of decisions,” Bonsai co-founder and CEO Mark Hammond told The New Stack Makers.  “The first is going to be, are you going to have a system where online learning matters — that is to say, where the system needs to learn concurrently, with users utilizing the system?  Or can there be a lag in that?  Is it possible for you instead to record the data that has come in, as users use the system, and then learn on those recordings in a more batch-style operation layer?”

HPE OneSphere is a platform built for a time when Kubernetes has established itself as a stable enough technology that makes it capable of serving IT operations and developers in one SaaS deployment.

With Kubernetes under the hood, HPE OneSphere speaks to the new dimensions of IT and developer experiences that share the same environments, have container technologies for packaging and version control to allow for the use of multiple components across different infrastructure.

Portability is the essence of what makes a multi-cloud approach doable. Containers are inhertently portable and can be managed in a SaaS platform such as with HPE OneSphere. Kubernetes makes it possible to run HPE OneSphere as a networked platform to run across clouds services and on-premise infrastructure.

We recently sat down with Bryan Jacquot, Director, Distinguished Technologist, and Chief Design Architect at HPE to tell us more about HPE OnesSphere and the concepts that it upholds. In particular, how it upholds the beliefs that customers are digitally aware and businesses as a result have to be digitally driven in the architectures they establish for application development, deployment and management.

HPE OneSphere is a platform built for a time when Kubernetes has established itself as a stable enough technology that makes it capable of serving IT operations and developers in one SaaS deployment.

With Kubernetes under the hood, HPE OneSphere speaks to the new dimensions of IT and developer experiences that share the same environments, have container technologies for packaging and version control to allow for the use of multiple components across different infrastructure.

Portability is the essence of what makes a multi-cloud approach doable. Containers are inhertently portable and can be managed in a SaaS platform such as with HPE OneSphere. Kubernetes makes it possible to run HPE OneSphere as a networked platform to run across clouds services and on-premise infrastructure.

We recently sat down with Bryan Jacquot, Director, Distinguished Technologist, and Chief Design Architect at HPE to tell us more about HPE OnesSphere and the concepts that it upholds. In particular, how it upholds the beliefs that customers are digitally aware and businesses as a result have to be digitally driven in the architectures they establish for application development, deployment and management.