The New Stack Podcast

The supply chains of modern enterprises are transitioning — or may have already transitioned — to an API-like model.  The services that departments provide each other are being governed by interfaces that specify their types and contents, and regulated by service-level agreements that stipulate the expectations of all sides of the transaction.

Whether business managers know it or not, they’re adopting a methodology that stems from the realm of software development.  As Red Hat Director of Product Strategy Brian Gracely told The New Stack Makers, he and his colleagues are discovering those business managers suddenly showing up in meetings about the architecture of their business.

Learn more about what Gracely and the OpenShift team have discovered about integrating the modern software development mindset into business and industry, in 'Kubernetes’ Impact on the Supply Chain.' 

Read more at: https://thenewstack.io/red-hats-brian-gracely-kubernetes-impact-supply-chain/

The supply chains of modern enterprises are transitioning — or may have already transitioned — to an API-like model.  The services that departments provide each other are being governed by interfaces that specify their types and contents, and regulated by service-level agreements that stipulate the expectations of all sides of the transaction.

Whether business managers know it or not, they’re adopting a methodology that stems from the realm of software development.  As Red Hat Director of Product Strategy Brian Gracely told The New Stack Makers, he and his colleagues are discovering those business managers suddenly showing up in meetings about the architecture of their business.

Learn more about what Gracely and the OpenShift team have discovered about integrating the modern software development mindset into business and industry, in 'Kubernetes’ Impact on the Supply Chain.' 

Read more at: https://thenewstack.io/red-hats-brian-gracely-kubernetes-impact-supply-chain/

If every application in the world could be easily containerized with an automated process, it might already have happened.  We’ve talked before in The New Stack about the many approaches to maintaining state — which, in a less esoteric, more honest-sounding world, is the same thing as “having one’s own database.”  For a stateless system, a stateful construct is a pretty neat trick.  Or at least for now, it’s your choice of several pretty neat tricks, and maybe a few more not-so-neat ones.

But machine learning is a different order of beast.  By implication, something you learn is something you retain.  If you can accept the notion that a machine can “learn” anything, then the whole notion is pointless if the machine is also capable of wiping its own slate clean with each iteration.  Not even a virtual thing can be real unless it retains the same virtues.

Hear more about Dunning’s vision for “REST for ML” in 'The Intersection Between Machine Learning and Containers,' the latest edition of The New Stack Makers podcast.

If every application in the world could be easily containerized with an automated process, it might already have happened.  We’ve talked before in The New Stack about the many approaches to maintaining state — which, in a less esoteric, more honest-sounding world, is the same thing as “having one’s own database.”  For a stateless system, a stateful construct is a pretty neat trick.  Or at least for now, it’s your choice of several pretty neat tricks, and maybe a few more not-so-neat ones.

But machine learning is a different order of beast.  By implication, something you learn is something you retain.  If you can accept the notion that a machine can “learn” anything, then the whole notion is pointless if the machine is also capable of wiping its own slate clean with each iteration.  Not even a virtual thing can be real unless it retains the same virtues.

Hear more about Dunning’s vision for “REST for ML” in 'The Intersection Between Machine Learning and Containers,' the latest edition of The New Stack Makers podcast.

If every application in the world could be easily containerized with an automated process, it might already have happened.  We’ve talked before in The New Stack about the many approaches to maintaining state — which, in a less esoteric, more honest-sounding world, is the same thing as “having one’s own database.”  For a stateless system, a stateful construct is a pretty neat trick.  Or at least for now, it’s your choice of several pretty neat tricks, and maybe a few more not-so-neat ones.

But machine learning is a different order of beast.  By implication, something you learn is something you retain.  If you can accept the notion that a machine can “learn” anything, then the whole notion is pointless if the machine is also capable of wiping its own slate clean with each iteration.  Not even a virtual thing can be real unless it retains the same virtues.

Hear more about Dunning’s vision for “REST for ML” in 'The Intersection Between Machine Learning and Containers,' the latest edition of The New Stack Makers podcast.

A pull request is a potential contribution back to a project on Github. It can be difficult to manage all of the requests that come in and the need to automate the approval of pull requests has led to the creation of several open source projects. LGTM (Looks Good to Me)is one such automated system, built around GitHub.  It locks pull requests from being merged upstream until a given number of approvals have been received.

LGTM does not come pre-configured for being bolted into certain implementers’ existing tool chains.  Now, being a general-purpose project, perhaps no one should expect it to be configured this way.  But then what’s the purpose of the open source development process if not to open up integration capabilities to implementers?

At the last OSCON conference, Capital One lead software engineer Jon Bodner tells the story of how LGTM’s principal developer gave his team his blessing to produce a fork that adds functionality that may be more specific not just to Capital One or another financial institution, but any organization of its magnitude.

Watch on YouTube: https://www.youtube.com/watch?v=p7mWHq-ONZA

A pull request is a potential contribution back to a project on Github. It can be difficult to manage all of the requests that come in and the need to automate the approval of pull requests has led to the creation of several open source projects. LGTM (Looks Good to Me)is one such automated system, built around GitHub.  It locks pull requests from being merged upstream until a given number of approvals have been received.

LGTM does not come pre-configured for being bolted into certain implementers’ existing tool chains.  Now, being a general-purpose project, perhaps no one should expect it to be configured this way.  But then what’s the purpose of the open source development process if not to open up integration capabilities to implementers?

At the last OSCON conference, Capital One lead software engineer Jon Bodner tells the story of how LGTM’s principal developer gave his team his blessing to produce a fork that adds functionality that may be more specific not just to Capital One or another financial institution, but any organization of its magnitude.

Watch on YouTube: https://www.youtube.com/watch?v=p7mWHq-ONZA

A pull request is a potential contribution back to a project on Github. It can be difficult to manage all of the requests that come in and the need to automate the approval of pull requests has led to the creation of several open source projects. LGTM (Looks Good to Me)is one such automated system, built around GitHub.  It locks pull requests from being merged upstream until a given number of approvals have been received.

LGTM does not come pre-configured for being bolted into certain implementers’ existing tool chains.  Now, being a general-purpose project, perhaps no one should expect it to be configured this way.  But then what’s the purpose of the open source development process if not to open up integration capabilities to implementers?

At the last OSCON conference, Capital One lead software engineer Jon Bodner tells the story of how LGTM’s principal developer gave his team his blessing to produce a fork that adds functionality that may be more specific not just to Capital One or another financial institution, but any organization of its magnitude.

Watch on YouTube: https://www.youtube.com/watch?v=p7mWHq-ONZA

James Ford is a chief architect at ADP, an $11 billion company and a pillar in the human capital management world. Ford and his team have made significant investments in Docker, going from about 1000 containers in use across the enterprise to now more than 3,000. It's just the beginning of scale out for the company but already they have done enough to feel comfortable about containers, in particular with their security.

The discussion with Ford in this podcast reflects the considerations enterprise companies have to take when changing to a container architecture approach. The value for ADP is in the way Docker may be used across multiple environments including virtual machines, mainframes, bare metal, Amazon Web Services and Azure.

Watch on YouTube: https://youtu.be/UiZL57x7U44

James Ford is a chief architect at ADP, an $11 billion company and a pillar in the human capital management world. Ford and his team have made significant investments in Docker, going from about 1000 containers in use across the enterprise to now more than 3,000. It's just the beginning of scale out for the company but already they have done enough to feel comfortable about containers, in particular with their security.

The discussion with Ford in this podcast reflects the considerations enterprise companies have to take when changing to a container architecture approach. The value for ADP is in the way Docker may be used across multiple environments including virtual machines, mainframes, bare metal, Amazon Web Services and Azure.

Watch on YouTube: https://youtu.be/UiZL57x7U44