The logistics of machine learning typically take waaay more effort than the machine learning itself. Moreover, machine learning systems aren't like normal software projects so continuous integration takes on new meaning.
Tensor Abuse - how to reuse machine learning frameworksTed Dunning
Tensors are a very useful tool for mathematical programming. Moreover, the optimization frameworks that are part of most machine learning frameworks have some very cool uses outside of the normal machine learning kinds of tasks.
You know that a single number isn't a good summary of a measurement. T-digest and other non-uniform histograms can make it easy to keep track of an entire distribution and can be combined in OLAP queries.
The latest t-digest is faster, more accurate and has hard bounds on size.
Ellen Friedman and I spoke at the ACM meetup about how stream-first architecture can have a big impact and how the logistics of machine learning is a great example of that impact.
This is my half of the presentation.
This talk shows practical methods for find changes in a variety of kinds of data as well as giving real-world examples from finance, telecom, systems monitoring and natural language processing.
Surprising Advantages of Streaming - ACM March 2018Ellen Friedman
Shift to a new idea: stream instead of database as heart of your big data architecture. With the right capabilities for event-by-event streaming data transport (not processing) you get the flexibility of streaming microservices & much more. Includes real world use case examples.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
Big data technologies are being applied to a wide variety of use cases. We will review tangible examples of machine learning, discuss an autonomous driving project and illustrate the role of MapR in next generation initiatives. More: http://info.mapr.com/WB_Machine-Learning-for-Chickens_Global_DG_17.11.02_RegistrationPage.html
Tensor Abuse - how to reuse machine learning frameworksTed Dunning
Tensors are a very useful tool for mathematical programming. Moreover, the optimization frameworks that are part of most machine learning frameworks have some very cool uses outside of the normal machine learning kinds of tasks.
You know that a single number isn't a good summary of a measurement. T-digest and other non-uniform histograms can make it easy to keep track of an entire distribution and can be combined in OLAP queries.
The latest t-digest is faster, more accurate and has hard bounds on size.
Ellen Friedman and I spoke at the ACM meetup about how stream-first architecture can have a big impact and how the logistics of machine learning is a great example of that impact.
This is my half of the presentation.
This talk shows practical methods for find changes in a variety of kinds of data as well as giving real-world examples from finance, telecom, systems monitoring and natural language processing.
Surprising Advantages of Streaming - ACM March 2018Ellen Friedman
Shift to a new idea: stream instead of database as heart of your big data architecture. With the right capabilities for event-by-event streaming data transport (not processing) you get the flexibility of streaming microservices & much more. Includes real world use case examples.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
Big data technologies are being applied to a wide variety of use cases. We will review tangible examples of machine learning, discuss an autonomous driving project and illustrate the role of MapR in next generation initiatives. More: http://info.mapr.com/WB_Machine-Learning-for-Chickens_Global_DG_17.11.02_RegistrationPage.html
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
This was one of the talks that I gave at the Strata San Jose conference. I migrated my topic a bit, but here is the original abstract:
Application developers and architects today are interested in making their applications as real-time as possible. To make an application respond to events as they happen, developers need a reliable way to move data as it is generated across different systems, one event at a time. In other words, these applications need messaging.
Messaging solutions have existed for a long time. However, when compared to legacy systems, newer solutions like Apache Kafka offer higher performance, more scalability, and better integration with the Hadoop ecosystem. Kafka and similar systems are based on drastically different assumptions than legacy systems and have vastly different architectures. But do these benefits outweigh any tradeoffs in functionality? Ted Dunning dives into the architectural details and tradeoffs of both legacy and new messaging solutions to find the ideal messaging system for Hadoop.
Topics include:
* Queues versus logs
* Security issues like authentication, authorization, and encryption
* Scalability and performance
* Handling applications that span multiple data centers
* Multitenancy considerations
* APIs, integration points, and more
Real-time Puppies and Ponies - Evolving Indicator Recommendations in Real-timeTed Dunning
This talk describes how indicator-based recommendations can be evolved in real time. Normally, indicator-based recommendations use a large off-line computation to understand the general structure of items to be recommended and then make recommendations in real-time to users based on a comparison of their recent history versus the large-scale product of the off-line computation.
In this talk, I show how the same components of the off-line computation that guarantee linear scalability in a batch setting also give strict real-time bounds on the cost of a practical real-time implementation of the indicator computation.
These are the slides from my talk at FAR Con in Minneapolis recently. The topics are the implications of buried treasure hoards on data security, horror stories and new, simpler and provably secure methods for public data disclosure.
Cognitive computing with big data, high tech and low tech approachesTed Dunning
I explain some very approachable methods for analyzing big data via a detour through clipper ships and the 19th century open source scene.
Note that I mixed up the route of the Flying Cloud record in this talk. The Flying Cloud's record was actually from New York to San Francisco and was even more impressive than what I said. The usual time had been about 180 days. With Maury's charts, the time was reduced to about 135 days. The Flying Cloud's time was 89 days.
Thanks to Chen Kung for noticing my error.
Many statistics are impossible to compute precisely on streaming data. There are some very clever algorithms, however, which allow us to compute very good approximations of these values efficiently in terms of CPU and memory.
MapR is an ideal scalable platform for data science and specifically for operationalizing machine learning in the enterprise. This presentations gives specific reasons why.
Anomaly Detection - New York Machine LearningTed Dunning
Anomaly detection is the art of finding what you don't know how to ask for. In this talk, I walk through the why and how of building probabilistic models for a variety of problems including continuous signals and web traffic. This talk blends theory and practice in a highly approachable way.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
These are the slides that we used to ignite the conversation with the audience at Hadoop Summit EU. Come over to the Mahout dev list to be part of the ongoing conversation.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Apache Mahout is changing radically. Here is a report on what is coming, notably including an R like domain specific language that can use multiple computational engines such as Spark.
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
This talk focuses on how larger data sets are not only enabling advanced techniques, but also increasing the number of problems within reach of relatively simple techniques, that is "cheap learning".
This was one of the talks that I gave at the Strata San Jose conference. I migrated my topic a bit, but here is the original abstract:
Application developers and architects today are interested in making their applications as real-time as possible. To make an application respond to events as they happen, developers need a reliable way to move data as it is generated across different systems, one event at a time. In other words, these applications need messaging.
Messaging solutions have existed for a long time. However, when compared to legacy systems, newer solutions like Apache Kafka offer higher performance, more scalability, and better integration with the Hadoop ecosystem. Kafka and similar systems are based on drastically different assumptions than legacy systems and have vastly different architectures. But do these benefits outweigh any tradeoffs in functionality? Ted Dunning dives into the architectural details and tradeoffs of both legacy and new messaging solutions to find the ideal messaging system for Hadoop.
Topics include:
* Queues versus logs
* Security issues like authentication, authorization, and encryption
* Scalability and performance
* Handling applications that span multiple data centers
* Multitenancy considerations
* APIs, integration points, and more
Real-time Puppies and Ponies - Evolving Indicator Recommendations in Real-timeTed Dunning
This talk describes how indicator-based recommendations can be evolved in real time. Normally, indicator-based recommendations use a large off-line computation to understand the general structure of items to be recommended and then make recommendations in real-time to users based on a comparison of their recent history versus the large-scale product of the off-line computation.
In this talk, I show how the same components of the off-line computation that guarantee linear scalability in a batch setting also give strict real-time bounds on the cost of a practical real-time implementation of the indicator computation.
These are the slides from my talk at FAR Con in Minneapolis recently. The topics are the implications of buried treasure hoards on data security, horror stories and new, simpler and provably secure methods for public data disclosure.
Cognitive computing with big data, high tech and low tech approachesTed Dunning
I explain some very approachable methods for analyzing big data via a detour through clipper ships and the 19th century open source scene.
Note that I mixed up the route of the Flying Cloud record in this talk. The Flying Cloud's record was actually from New York to San Francisco and was even more impressive than what I said. The usual time had been about 180 days. With Maury's charts, the time was reduced to about 135 days. The Flying Cloud's time was 89 days.
Thanks to Chen Kung for noticing my error.
Many statistics are impossible to compute precisely on streaming data. There are some very clever algorithms, however, which allow us to compute very good approximations of these values efficiently in terms of CPU and memory.
MapR is an ideal scalable platform for data science and specifically for operationalizing machine learning in the enterprise. This presentations gives specific reasons why.
Anomaly Detection - New York Machine LearningTed Dunning
Anomaly detection is the art of finding what you don't know how to ask for. In this talk, I walk through the why and how of building probabilistic models for a variety of problems including continuous signals and web traffic. This talk blends theory and practice in a highly approachable way.
This is the position talk that I gave at CIKM. Included are 4 algorithms that I feel don't get much academic attention, but which are very important industrially. It isn't necessarily true that these algorithms *should* get academic attention, but I do feel that it is true that they are quite important pragmatically speaking.
These are the slides that we used to ignite the conversation with the audience at Hadoop Summit EU. Come over to the Mahout dev list to be part of the ongoing conversation.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Apache Mahout is changing radically. Here is a report on what is coming, notably including an R like domain specific language that can use multiple computational engines such as Spark.
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
ML Workshop 1: A New Architecture for Machine Learning LogisticsMapR Technologies
Having heard the high-level rationale for the rendezvous architecture in the introduction to this series, we will now dig in deeper to talk about how and why the pieces fit together. In terms of components, we will cover why streams work, why they need to be persistent, performant and pervasive in a microservices design and how they provide isolation between components. From there, we will talk about some of the details of the implementation of a rendezvous architecture including discussion of when the architecture is applicable, key components of message content and how failures and upgrades are handled. We will touch on the monitoring requirements for a rendezvous system but will save the analysis of the recorded data for later. Listen to the webinar on demand: https://mapr.com/resources/webinars/machine-learning-workshop-1/
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
How Rendezvous Architecture Improves Evaluation in the Real World
In this addition of our machine learning logistics webinar series we build on the ideas of the key requirements for effective management of machine learning logistics presented in the Overview webinar and in Part I Workshop. Here we focus on model-to-model comparison & evaluation, use of decoy models and more. Listen here: http://info.mapr.com/machine-learning-workshop2.html?_ga=2.35695522.324200644.1511891424-416597139.1465233415
DataOps: An Agile Method for Data-Driven OrganizationsEllen Friedman
DataOps expands DevOps philosophy to include data-heavy roles (data engineering & data science). DataOps uses better cross-functional collaboration for flexibility, fast time to value and an agile workflow for data-intensive applications including machine learning pipelines. (Strata Data San Jose March 2018)
State of the Art Robot Predictive Maintenance with Real-time Sensor DataMathieu Dumoulin
Our Strata Beijing 2017 presentation slides where we show how to use data from a movement sensor, in real-time, to do anomaly detection at scale using standard enterprise big data software.
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Predictive Maintenance Using Recurrent Neural NetworksJustin Brandenburg
My presentation from AnacondaCON 2018 where I discussed using Recurrent Neural Networks, Python, Tensorflow and the MapR Platform to develop deploy a predictive maintenance model for an IoT device in the manufacturing industry.
Converged and Containerized Distributed Deep Learning With TensorFlow and Kub...Mathieu Dumoulin
Docker containers running on Kubernetes combine with MapR Converged Data Platform allow any company to potentially enjoy the same sophisticated data infrastructure for enabling teams to engage in transformative machine learning and deep learning for production use at scale.
How to Leverage the Cloud for Business Solutions | Strata Data Conference Lon...MapR Technologies
IT budgets are shrinking, and the move to next-generation technologies is upon us. The cloud is an option for nearly every company, but just because it is an option doesn’t mean it is always the right solution for every problem.
Most cloud providers would prefer that every customer be tightly coupled with their proprietary services and APIs to create lock-in with that cloud provider. The savvy customer will leverage the cloud as infrastructure and stay loosely bound to a cloud provider. This creates an opportunity for the customer to execute a multicloud strategy or even a hybrid on-premises and cloud solution.
Jim Scott explores different use cases that may be best run in the cloud versus on-premises, points out opportunities to optimize cost and operational benefits, and explains how to get the data moved between locations. Along the way, Jim discusses security, backups, event streaming, databases, replication, and snapshots across a variety of use cases that run most businesses today.
Big Data LDN 2018: DATA OPERATIONS PROBLEMS CREATED BY DEEP LEARNING, AND HOW...Matt Stubbs
Date: 14th November 2018
Location: AI Lab Theatre
Time: 10:30 - 11:00
Speaker: Jim Scott
Organisation: MapR
About: There are numerous problems which have been exposed by deep learning models due to the sheer ability of the current generation of GPUs to create and run a large volume of models, and we are going to show people how to fix them. The exponential compute growth which has occurred in this area has opened the doors to creating and testing hundreds or thousands more models than the, one-by-one by hand which was performed in the past. These models use and generate data for both batch and real-time as well as training and scoring use cases. As data becomes enriched, and model parameters are explored, there is a real need for versioning everything, including the data. Many of these issues are similar to other software engineering problems, but new approaches must be taken to create solutions given the complexity of the problems. We will discuss what exactly these problems are, how they came to be and how to fix them.
Demystifying AI, Machine Learning and Deep LearningCarol McDonald
Deep learning, machine learning, artificial intelligence - all buzzwords and representative of the future of analytics. In this talk we will explain what is machine learning and deep learning at a high level with some real world examples. The goal of this is not to turn you into a data scientist, but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and Data scientists work with domain experts, architects, developers and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This and future posts are meant to provoke a review of your own data to identify new opportunities.
Changes in how business is done combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries, including healthcare, manufacturing, automotive, telecommunications, and entertainment. Technical challenges arise with these disruptions, but the good news is there are now innovative solutions to address these problems. http://info.mapr.com/WB_Geo-distributed-Big-Data-and-Analytics_Global_DG_17.05.16_RegistrationPage.html
Real-Time Robot Predictive Maintenance in ActionDataWorks Summit
Industry 4.0 IoT applications promise vast gains in productivity from reduced downtime, higher product quality and higher efficiency. Modern industrial robots integrate hundreds of sensors of all kinds, generating tremendous volumes of data rich in valuable information. However, the reality is that some of the most advanced industrial makers in the world are barely getting started making use of this data, with relatively rudimentary, bespoke monitoring systems built at tremendous cost.
We believe that it is now possible, using a well-chosen selection of enterprise open source big data projects, to successfully deploy Industry 4.0 pilot use cases in a matter of months, at a small fraction of the cost of equivalent projects at leading high-tech makers. We propose to show a working prototype of just such a system, and explain in some detail how it was made.
Our presentation describes a working real-time ML-based anomaly detection system. We show a working industrial robot-analog installed with a wireless movement sensor. Our system scores the data in a cloud-based cluster. For added realism, the system we demonstrate live includes a working augmented-reality headset that can show the real-time status overlaid on the working robot.
This talk is about demonstrating a concrete example of a real-time predictive maintenance system, built as a series of microservices connected by Kafka streams and powered by the excellent H2O distributed Machine Learning tool. Our goal is for our attendees to get a feel for what can be realistically achieved by a few non-genius-level engineers in a few months of effort using the best in open source technology for real-time streams (Kafka) and Machine learning (H2O).
Where appropriate, we’ll mention how our choice of using the MapR Converged Data Platform made the development easier thanks to some of its unique features.
Speaker
Cao Yi, MapR
We introduce the idea that metadata, including project information, data labels, data characteristics and indications of valuable use, can be propagated through a data processing lineage graph. Further, finding examples of significant cooccurrence of propagated and original metadata gives us the basis of an interesting kind of search engine gives interesting recommendations of data given a problem statement even in a near cold-start situation.
The folk wisdom has always been that when running stateful applications inside containers, the only viable choice is to externalize the state so that the containers themselves are stateless or nearly so. Keeping large amounts of state inside containers is possible, but it’s considered a problem because stateful containers generally can’t preserve that state across restarts.
In practice, this complicates the management of large-scale Kubernetes-based infrastructure because these high-performance storage systems require separate management. In terms of overall system management, it would be ideal if we could run a software-defined storage system directly in containers managed by Kubernetes, but that has been hampered by lack of direct device access and difficult questions about what happens to the state on container restarts.
Ted Dunning describes recent developments that make it possible for Kubernetes to manage both compute and storage tiers in the same cluster. Container restarts can be handled gracefully without loss of data or a requirement to rebuild storage structures and access to storage from compute containers is extremely fast. In some environments, it’s even possible to implement elastic storage frameworks that can fold data onto just a few containers during quiescent periods or explode it in just a few seconds across a large number of machines when higher speed access is required.
The benefits of systems like this extend beyond management simplicity, because applications can be more Agile precisely because the storage layer is more stable and can be uniformly accessed from any container host. Even better, it makes it a snap to configure and deploy a full-scale compute and storage infrastructure.
How the Internet of Things is Turning the Internet Upside DownTed Dunning
This is a wide-ranging talk that goes into how the internet is architected, how that architecture is changing as a result of internet of things, how the internet of things worked in the 19th century big data, open-source community and how to build time-series databases to make this all possible.
Really.
Apache Kylin - OLAP Cubes for SQL on HadoopTed Dunning
Apache Kylin (incubating) is a new project to bring OLAP cubes to Hadoop. I walk through the project and describe how it works and how users see the project.
This talk describes the general architecture common to anomaly detections systems that are based on probabilistic models. By examining several realistic use cases, I illustrate the common themes and practical implementation methods.
Building multi-modal recommendation engines using search enginesTed Dunning
This is my strata NY talk about how to build recommendation engines using common items. In particular, I show how multi-modal recommendations can be built using the same framework.
Using Mahout and a Search Engine for RecommendationTed Dunning
I presented this talk at the Open World Forum in Paris in 2013. The ideas here are that you can do basic recommendations and extended forms of recommendation such as intelligent search or cross recommendation or multi-modal recommendation using Mahout's cooccurrence analysis together with a search engine.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
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
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.