article thumbnail

Unmatched scalability and security of Dynatrace extensions now available for all supported technologies: 7 reasons to migrate your JMX and Python plugins

Dynatrace

are technologically very different, Python and JMX extensions designed for Extension Framework 1.0 address these limitations and brings new monitoring and analytical capabilities that weren’t available to Extensions 1.0: What’s available now and what’s coming later We’ve already started to migrate Dynatrace-developed Extensions 1.0

article thumbnail

Hawkins: Diving into the Reasoning Behind our Design System

The Netflix TechBlog

Stranger Things imagery showcasing the inspiration for the Hawkins Design System by Hawkins team member Joshua Godi ; with art contributions by Wiki Chaves Hawkins may be the name of a fictional town in Indiana, most widely known as the backdrop for one of Netflix’s most popular TV series “Stranger Things,” but the name is so much more.

Design 230
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

The Ultimate Guide to Database High Availability

Percona

To make data count and to ensure cloud computing is unabated, companies and organizations must have highly available databases. This guide provides an overview of what high availability means, the components involved, how to measure high availability, and how to achieve it. How does high availability work?

article thumbnail

Auto-adaptive thresholds for AI-driven quality gating

Dynatrace

Fast feedback cycles on model improvements While the Site Reliability Guardian was originally designed to validate new software releases, Dynatrace has internally extended its application area to include validation of models for Davis AI. A series of models are continuously trained on Dynatrace tenants to effectively set objectives.

Metrics 215
article thumbnail

Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Since its inception , Metaflow has been designed to provide a human-friendly API for building data and ML (and today AI) applications and deploying them in our production infrastructure frictionlessly. There are several ways to provide explainability to models but one way is to train an explainer model based on each trained model.

Systems 226
article thumbnail

Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. The LLM then synthesizes the retrieved data with the augmented prompt and its internal training data to create a response that can be sent back to the user. million AI server units annually by 2027, consuming 75.4+

Cache 202
article thumbnail

Detecting Scene Changes in Audiovisual Content

The Netflix TechBlog

To address the first challenge, we use pre trained sentence-level embeddings, e.g. from an embedding model optimized for paraphrase identification , to represent text in both sources. However, it presupposes the availability of high-quality screenplays.