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Dynatrace introduced numerous powerful features to its Infrastructure & Operations app, addressing the emerging requirement for enhanced end-to-end infrastructure observability. These enhancements are designed to empower IT operations and SRE teams with more comprehensive visibility and increased efficiency at any time.
This article is the first in a multi-part series sharing a breadth of Analytics Engineering work at Netflix, recently presented as part of our annual internal Analytics Engineering conference. Subsequent posts will detail examples of exciting analytic engineering domain applications and aspects of the technical craft.
What began with an engineering plan to pave the path towards our first Live comedy special, Chris Rock: Selective Outrage , has since led to hundreds of Live events ranging from the biggest comedy shows and NFL Christmas Games to record-breaking boxing fights and becoming the home of WWE.
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Attracting over 20 million members each month, Tudum is designed to enrich the viewing experience by offering additional context and insights into the content available on Netflix. Client applications like web, mobile, and TV devices, act as rendering engines for SDUI data.
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This latest integration with Microsoft Sentinel expands our partnership, providing joint customers with a holistic view of their entire cloud environment; from application to infrastructure, data, and security. “As The Davis AI engine automatically and continuously delivers actionable insights based on an environment’s current state.
This has been a guiding design principle with Metaflow since its inception. Subsequent versions of the model will result from experimenting with hyper parameters, tweaking feature engineering, or conducting feature diets. demo.branch_demox.demo_features_f workflows/demo.main.sch.yaml (binding=default): cluster=sandbox, workflow.id=demo.branch_demox.main
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Key insights from this shiftinclude: A Data-Centric Approach : Shifting focus from model-centric strategies, which heavily rely on feature engineering, to a data-centric one. This approach prioritizes the accumulation of large-scale, high-quality data and, where feasible, aims for end-to-end learning.
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This creates a whole new set of challenges that traditional software development approaches simply weren’t designed to handle. With the advent of generative AI, therell be significant opportunities for product managers, designers, executives, and more traditional software engineers to contribute to and build AI-powered software.
Matthew Liste, Head of Infrastructure at American Express, shared insights at QCon London 2025 on building robust cloud platforms in financial services. By Steef-Jan Wiggers
Tech services firms are instead applying AI to their own offerings to do things like accelerate the reverse-engineering of existing code and expedite forward engineering of new solutions. McKinsey last week posted a blog touting their proprietary AI platform for rejuvenating legacy infrastructure). Not the customers business.
Best for On-Premise Monitoring Zabbix for MySQL Agent-based tracking, configurable alerts, strong monitoring for in-house infrastructure. Tools like Paessler PRTG Network Monitor provide comprehensive solutions by combining database monitoring with broader IT infrastructure oversight.
I’ve known folks at CitSec for many years now (including some who participate in WG 21) and have long known it to be a great organization with some of the brightest minds in engineering and beyond. To all of those experts: Again, thank you !
Nothing is more discouraging than the idea that it will take tens of millions of dollars to train a model and billions of dollars to build the infrastructure necessary to operate it. What about computing infrastructure? Jevons paradox has a big impact on what kind of data infrastructure is needed to support the growing AI industry.
Refreshed look and feel The Dynatrace Community has long wished for a modern design, and thanks to the Dynatrace platform, its finally here. Analyzing status changes over time can exclude temporary infrastructure issues or find patterns. Once again, Extensions comes in handy. There is no easier way to enter it than by the front door.
This has serious implications for how we design, deploy, and govern AI systems across institutions, economies, and everyday life. Even with well-intentioned design, these systems can easily cross into overreach if they’re not built with human experience in mind. Fei-Fei doesn’t describe AI as a feature or even an industry.
Distributed systems are designed to improve performance, provide redundancy, and support scalability. Despite their complexity, distributed systems are designed to provide transparency to present a unified interface to users without exposing underlying intricacies. Automated alerts and failure recovery mechanisms are essential.
Essentially youre designing the interface that the AI will see. By documenting it, you also help AI prompt engineers know how to prompt the model. Theres active research and engineering going into making AI agents more reliable (techniques like better prompt chaining, feedback loops, or fine-tuning on tool use).
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Symptom Machine Learning engineer Luca Pozzi reported to our Data Platform team that their JupyterLab UI on their workbench becomes slow and unresponsive when running some of their Notebooks. Be part of shaping the future of Data Security and Infrastructure, Data Developer Experience, Analytics Infrastructure and Enablement, and more.
This is the kind of thing the terminal shows as it was coding the conciousness engine. Let me update my todo and spawn the agents: ● Update Todos ⎿ ☒ Read consciousness-engine-guide.md to understand requirements ☒ Analyze current project structure and dependencies ☐ Execute: work through plans/consciousness-engine-guide.md
New forms of interaction will take a long time to develop, and the improvements in infrastructure are dwarfed by the improvements in model efficiency. Persona answer: As I see it, the next major leap in AI will likely be driven by a combination of better models, new forms of human-AI interaction, and infrastructure innovation.
We no longer need to spend loads of time training developers; we can train them to be “prompt engineers” (which makes me think of developers who arrive on time), and they will ask the AI for the code, and it will deliver. As AI improves, it will probably even give you an answer that works. This is great!
Teams with thoughtfully designed data viewers iterate 10x faster than those without them. Their product manager, a learning design expert, would create detailed PowerPoint decks explaining pedagogical principles and example dialogues. Shed present these to the engineering team, who would then translate her expertise into prompts.
Our work focuses on the challenges that come with bringing PoCs to production, such as scaling AI infrastructure, improving AI system reliability, and producing business value. As Steve Yegge says, you have to demand that the AI writes code that meets your quality standards as an engineer.
Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. Design a photo-sharing platform similar to Instagram where users can upload their photos and share it with their followers. High Level Design. Component Design. API Design. Problem Statement.
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a Netflix member via Twitter This is an example of a question our on-call engineers need to answer to help resolve a member issue?—?which Now let’s look at how we designed the tracing infrastructure that powers Edgar. Now let’s look at how we designed the tracing infrastructure that powers Edgar.
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