The Netflix TechBlog
OCTOBER 18, 2022
As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Motivation Scalability and usability are essential to enable large-scale workflows and support a wide range of use cases.
The Netflix TechBlog
JUNE 6, 2022
Our goal is to manage security risks to Netflix via clear, opinionated security guidance, and by providing risk context to Netflix engineering teams to make pragmatic risk decisions at scale. a dynamic Asset Inventory that understands the nuances of our bespoke engineering ecosystem and how our applications and data relate to each other.
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The Netflix TechBlog
FEBRUARY 9, 2021
Plans & Offers Definitions Let’s define what a plan and an offer is at Netflix. Join Growth Engineering and help us build the next generation of services that will allow the next 200 million subscribers to experience the joy of Netflix. A plan is essentially a set of features with a price.
Dynatrace
JULY 18, 2023
To handle this challenge, enterprises need to automate and streamline the onboarding and lifecycle of tool configurations in the software development processes, including aspects of observability, security, alerting, and remediation. Development teams must set up tailored configurations for each tool and component they’re responsible for.
Dynatrace
APRIL 20, 2023
Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams.
Dynatrace
MARCH 3, 2020
For software engineering teams, this demand means not only delivering new features faster but ensuring quality, performance, and scalability too. One way to apply improvements is transforming the way application performance engineering and testing is done. Here is the definition of this model: ?.
O'Reilly
OCTOBER 19, 2021
This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. The new category is often called MLOps. This approach is not novel.
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