And lo there was monitoring!
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For dust you are and to dust you will return. A wise saint said "Let there be a (little) light!" Monitoring in the past was reactive and infrastructure-centric, but new architectures and the rise of cloud computing increased complexity. Observability is now needed to be proactive, observe systems from multiple data sources to gain rich context and truly understand systems rather than just see symptoms like monitoring of the past. Observability concerns should be business concerns.
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As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and Grafana
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Designing Great Products: The Power of Design and Leadership by Chief Designe...
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💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with Parameters
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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And lo there was monitoring!
- 1. ... for dust you are and to dust you will return ... James Turnbull @kartar https://www.kartar.net
- 2. Let me tell you a story...
- 3. In the beginning there was darkness
- 4. Then the world grew
- 5. Then all at once there was a loud bang
- 6. But a wise saint said: "Let there be a (little) light!"
- 7. But Monitoring was a fragile beast
- 8. Limited resolution and scale Reactive Infrastructure-centric Single data source-centric Monitored widgets not outcomes
- 9. Virtualization, Cloud Containers, Serverless New architectures IT as revenue center not cost center
- 10. Complexity and impact writ large
- 11. So we didn't do so well last time...
- 12. Observability
- 13. It's not about the tools. It's never about the tools.
- 14. But it is about who the customer is...
- 15. Observability concerns are business concerns
- 17. Observe systems
- 18. Use more data sources
- 19. Rich data means deep context
- 20. Monitoring is about symptoms Observability is about understanding
Editor's Notes
- - This is a story of where we've been - And what you can learn from the past. - Where we're going
- - Humans as monitoring - single/small numbers of machines, ops teams. - Monitoring was limited: heavily checklist-based
- - move to client/server - more moving pieces - move to IT being mission critical
- Outages – more of them, more time to recover.
- Check-based monitoring. - periodic checks
- Limited granularity and doesn’t scale Built post-the-implementation Focused on host and availability metrics Checks are one source of data – generally ignores: traces, logs, metrics Focused on IT assets not business metrics
- Thankfully things have gotten simpler… Oh wait.
- Complexity plus IT becoming strategic.
- Despite all this - things have not evolved as rapidly in the monitoring world. 80/20 bubble. We need a new start. First, re-evaluate what data youre collecting, what you're watching, what you're alerting on Second, it’s about new requirements not about patching old systems. You need to build a new paradigm.
- New paradigm that we can grow into. A meta narrative. A superset of monitoring. Embraces more than monitoring, doesn’t mean giving monitoring up. Not a perfect definition. Aspirational
- You are not the (only) customer of your monitoring.
- Observe systems. Components are interchangeable and not worth the effort. Observe end-to-end – user experience matters.
- Checks Metrics Logs Traces Also add context – you know a lot about your systems, why not surface that so you don’t have to go fishing. Et al
- Good for debugging purposes. You can’t predict failure in complex systems but you know it’s going to happen arming yourself with evidence and diagnostic information leaves you best prepared. Tools and their data can tell you information. Data with context gives you answers.
- report the overall health of systems Highly granular insights into the behavior of systems along with rich context.