Gitlab meets Kubernetes

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Datum: 20.10.2016 Speaker: Benjamin Stein, Arnold Bechtoldt, inovex GmbH mehr Vorträge: https://www.inovex.de/de/content-pool/vortraege/

Gitlab meets Kubernetes
Benjamin Stein
Arnold Bechtoldt
r2

Assets & Links
2
gitlab.com/inovex/pa-todo-app-web

Scenario: ToDo Webapp

4
Introduction
● Use Case: Managing Tasks
● Golang, Redis (Master/Slave)
● Gitlab (without CI), Jenkins CI

Architecture
5
Redis (Master) Redis Slave
ToDo Webapp

Motivation

Team Ops (+ QA)
Team Dev
Current Deployment Pipeline
7
Push Code Trigger Tests Build Artifact Release Artifact
Deploy Staging QAManual Testing Deploy Prod

★ Improve Tool Integration
★ Automate Everything
★ Make Deployments Great Again
Goals
8

Make Deployments Great Again
CC BY-SA 2.0 https://www.flickr.com/photos/gageskidmore/29273060912/in/album-72157673261299025/

Team DevOps (+ QA)
Improved Deployment Pipeline
10
Push Code Trigger Tests Build Artifact Release Artifact
Deploy Staging QAAutomated Testing Deploy Prod

Technologies
11
Gitlab KubernetesDocker

Demo Time
12
You know what happens with live demos?
Right, they always fail!

Demo Time: Video!
13
youtu.be/Ly_f221O6Fo

Conclusions

★ Improve Tool Integration
★ Automate Everything
★ Make Deployments Great Again
Goals (fixed)
15

TL;DR:
16
Improve the integration of your deployment toolstack
e.g. with Gitlab CI and Kubernetes and
get back the flexibility you ever wanted!

Benjamin Stein
Systems Engineer & Consultant @ inovex
〉 Software-Defined Datacenters
〉 Continuous Integration/Delivery
〉 DevOps
17
inovex.de/jobs

Arnold Bechtoldt
Systems Engineer & Consultant @ inovex
〉 Software-Defined Datacenters
〉 Continuous Integration/Delivery
〉 DevOps
18
inovex.de/jobs

Benjamin Stein
inovex GmbH
bstein@inovex.de
github.com/ben-st inovex.de youtube.com/inovexGmbH
CC BY-NC-ND diffus.org gitlab.com/inovex
abechtoldt@inovex.de
Arnold Bechtoldt
inovex GmbH
github.com/bechtoldt inovex.de youtube.com/inovexGmbH
CC BY-NC-ND arbe.io gitlab.com/inovex
Feedback & Questions:
sayat.me/benst
Feedback & Questions:
sayat.me/arbe

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Continuous delivery the french way Agile Cambridge 2014

Continuous delivery the french way Agile Cambridge 2014

CI/CD with Github Actions

CI/CD with Github Actions

CI/CD with Github Actions

Md. Minhazul Haque

gopaddle-meetup

gopaddle-meetup

gopaddle-meetup

Kandroid for nhn_deview_20131013_v5_final

Kandroid for nhn_deview_20131013_v5_final

Kandroid for nhn_deview_20131013_v5_final

Guides To Analyzing WebKit Performance

Guides To Analyzing WebKit Performance

Guides To Analyzing WebKit Performance

National Cheng Kung University

Creating a reasonable project boilerplate

Creating a reasonable project boilerplate

Creating a reasonable project boilerplate

Stanislav Petrov

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Apigee deploy grunt plugin.1.0

Apigee deploy grunt plugin.1.0

Apigee deploy grunt plugin.1.0

40 square's git workflow

40 square's git workflow

40 square's git workflow

Automating the Quality

Automating the Quality

Automating the Quality

Dejan Vukmirovic

Putting the Fun into Functioning CI/CD with JHipster

Putting the Fun into Functioning CI/CD with JHipster

Putting the Fun into Functioning CI/CD with JHipster

Gerard Gigliotti

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Deployment Patterns in the Ruby on Rails World

Deployment Patterns in the Ruby on Rails World

Deployment Patterns in the Ruby on Rails World

drone continuous Integration

drone continuous Integration

drone continuous Integration

Semelhante a Gitlab meets Kubernetes (20)

CI/CD Pipeline mit Gitlab CI und Kubernetes

CI/CD Pipeline mit Gitlab CI und Kubernetes

CI/CD Pipeline mit Gitlab CI und Kubernetes

Webinar - Unbox GitLab CI/CD

Webinar - Unbox GitLab CI/CD

Webinar - Unbox GitLab CI/CD

Skip Staging! Test Docker, Helm, and Kubernetes Apps like a Pro

Skip Staging! Test Docker, Helm, and Kubernetes Apps like a Pro

Skip Staging! Test Docker, Helm, and Kubernetes Apps like a Pro

PittsburgJUG_Cloud-Native Dev Tools: Bringing the cloud back to earth

PittsburgJUG_Cloud-Native Dev Tools: Bringing the cloud back to earth

PittsburgJUG_Cloud-Native Dev Tools: Bringing the cloud back to earth

Test driven development_continuous_integration

Test driven development_continuous_integration

Test driven development_continuous_integration

Build Automation in Android

Build Automation in Android

Build Automation in Android

Continuous delivery the french way Agile Cambridge 2014

Continuous delivery the french way Agile Cambridge 2014

Continuous delivery the french way Agile Cambridge 2014

CI/CD with Github Actions

CI/CD with Github Actions

CI/CD with Github Actions

gopaddle-meetup

gopaddle-meetup

gopaddle-meetup

Kandroid for nhn_deview_20131013_v5_final

Kandroid for nhn_deview_20131013_v5_final

Kandroid for nhn_deview_20131013_v5_final

Guides To Analyzing WebKit Performance

Guides To Analyzing WebKit Performance

Guides To Analyzing WebKit Performance

Creating a reasonable project boilerplate

Creating a reasonable project boilerplate

Creating a reasonable project boilerplate

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Gitlab ci e kubernetes, build test and deploy your projects like a pro

Apigee deploy grunt plugin.1.0

Apigee deploy grunt plugin.1.0

Apigee deploy grunt plugin.1.0

40 square's git workflow

40 square's git workflow

40 square's git workflow

Automating the Quality

Automating the Quality

Automating the Quality

Putting the Fun into Functioning CI/CD with JHipster

Putting the Fun into Functioning CI/CD with JHipster

Putting the Fun into Functioning CI/CD with JHipster

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Continuous integration / continuous delivery of web applications, Eugen Kuzmi...

Deployment Patterns in the Ruby on Rails World

Deployment Patterns in the Ruby on Rails World

Deployment Patterns in the Ruby on Rails World

drone continuous Integration

drone continuous Integration

drone continuous Integration

Mais de inovex GmbH

lldb kann mehr als nur einfache Breakpoints oder po. In dem Vortrag zeigt Oliver Bayer, wie sich mit Hilfe von lldb Programmcode zur Ausführungszeit manipulieren lässt, ohne das hierfür der Sourcecode anzupassen ist. Sei es, damit Test- oder Debugcode nicht in die produktiv App gelangt, oder weil der Sourcecode für einen Teil der App nicht vorliegt. Event: macoun, 04.10.2019 Speaker: Oliver Bayer, inovex Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

lldb – Debugger auf Abwegen

lldb – Debugger auf Abwegen

lldb – Debugger auf Abwegen

With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure. In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art. Speaker: Dr. Florian Wilhelm, Simon Bachstein, inovex Event: PyCon/PyData Berlin 2019 Datum: 10.10.2019 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Are you sure about that?! Uncertainty Quantification in AI

Are you sure about that?! Uncertainty Quantification in AI

Are you sure about that?! Uncertainty Quantification in AI

In 2010 ImageNet finally ended the AI winter and gave machines the sense of sight. Within the following years dramatic improvements in tasks such as image classification and object detection lead to innovations like face ID and autonomous driving. Recently, similar developments happened in the field of natural language. Using Attention mechanism and transformers tasks such as question answering and text summarization reached new benchmarks. This talk will not only explain those, but point out how Transfer Learning and open source models such as Google Bert will open the field to new innovations in AI. Speaker: Nico Kreiling, inovex Event: AIxIA, 01.10.2019 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Why natural language is next step in the AI evolution

Why natural language is next step in the AI evolution

Why natural language is next step in the AI evolution

Die Worldwide Developers Conference (WWDC) ist eine von Apple jährlich durchgeführte Konferenz für Software-Entwickler (MacOS, iOS und WatchOS). Um die WWDC 2019 nochmal Revue passieren zu lassen, wurde beim Mobile Development Karlsruhe Meetup zu einer offenen Diskussionsrunde eingeladen. Die Slides fassen die für inovexler Philipp interessantesten Neuigkeiten der WWDC2019 zusammen und dienten beim Meetup als Diskussionsgrundlage. Event: 9. Mobile Development Meetup (WWDC Edition) Speaker: Philipp Wallrich, inovex Datum: 17.06.2019 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

WWDC 2019 Recap

WWDC 2019 Recap

WWDC 2019 Recap

Trust is good, control is better – A short story about Network Policies. Abstract: Probably everybody who uses Kubernetes in a productive environment with multiple users possibly has looked at policies. Often the operators of the cluster(s) just trust the policies but in some cases it might be useful to control if the policies actually have taken action and often there are just to many Policies in the cluster setup to manually test them all (and obviously you don’t want to do this). Testing the effectiveness of the Network Policies can be done in different approaches. In this talk we will show you the benefits and drawbacks of different approaches and what solution we finally chose. Also we will show you some other tools and how they complement our solution. As a takeaway you will get an overview of different testing strategies for policies, as well as understanding challenges in testing policies in general and the Kubernetes ecosystem. Event: ContainerDays 2019 Datum: 26.06.2019 Speaker: Johannes M. Scheuermann, Maximilian Bischoff (beide inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Network Policies

Network Policies

Network Policies

Interpretierbarkeit von ML-Modellen hat die Zielsetzung, die Ursachen einer Prognose offenzulegen und eine daraus abgeleitete Entscheidung für einen Menschen nachvollziehbar zu erklären. Durch die Nachvollziehbarkeit von Prognosen lässt sich beispielsweise sicherstellen, dass deren Herleitung konsistent zum Domänenwissen eines Experten ist. Auch ein unfairer Bias lässt sich durch die Erklärung aussagekräftiger Beispiele identifizieren. Prognosemodelle lassen sich grob in intrinsisch interpretierbare Modelle und nicht-interpretierbare (auch Blackbox-) Modelle unterscheiden. Intrinsisch interpretierbare Modelle sind dafür bekannt, dass sie für einen Menschen leicht nachvollziehbar sind. Ein typisches Beispiel für ein solches Modell ist der Entscheidungsbaum, dessen regelbasierter Entscheidungsprozess intuitiv und leicht zugänglich ist. Im Gegensatz dazu gelten Neuronale Netze als Blackbox-Modelle, deren Prognosen durch die komplexe Netzstruktur schwer nachvollziehbar sind. In diesem Talk erläuterte Marcel Spitzer das Konzept von Interpretierbarkeit im Kontext von Machine Learning und stellte gängige Verfahren zur Interpretation von Modellen vor. Besonderen Fokus legte er dabei auf modellunabhängige Verfahren, die sich auch auf prognosestarke Blackbox-Modelle anwenden lassen. Event: M3 Minds Mastering Machines Speaker: Marcel Spitzer Blog-Artikel: https://www.inovex.de/blog/machine-learning-interpretability/ Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Interpretable Machine Learning

Interpretable Machine Learning

Interpretable Machine Learning

Das Ökosystem rund um Kubernetes wächst täglich. Insbesondere cloud-native Continuous-Deployment-Strategien stehen Hoch im Kurs und werden in diversen Open-Source-Projekten vorangetrieben. In einer Reihe von Evalutionen nimmt inovex diese Tools genauer unter die Lupe - den Anfang macht Jenkins X. Jenkins X wurde im März 2018 veröffentlicht. Das Konzept hinter dem Tool ist primär, bestehende Teillösungen (Helm, Skaffold, Prow, Tekton) einzusetzen, um sie abstrahiert in ein Kommandozeilen-Interface zu packen. Der Vortrag beschreibt sowohl die klassische Architektur als auch den "Severless"-Ansatz. Des weiteren werden das Kommandozeilen-Tool "jx", der allgemeine Entwicklungs-Workflow sowie diverse Features vorgestellt. Bei unseren Tests im Rahmen der Evaluation sind uns einige Stolpersteine aufgefallen. Es sind vor allem die vielen eingesetzten Dritt-Tools, die den Betrieb und den Upkeep eines mit Jenkins X erstellten Clusters verkomplizieren. Als Fazit stellen wir Jenkins X im Mai 2019 ein "befriedigend" aus und beobachten gespannt, wie sich das Tool in den kommmenden Monaten und Jahren weiterentwickeln wird. Event: Talk4Nerds, 29.04.2019 Speaker: Simon Kienzler, Johannes M. Scheuermann (beide inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

Neben dem großen Machine-Learning-Trend in der Cloud zeichnet sich zunehmend die Tendenz ab, bestimmte Aufgaben direkt auf Edge-Geräten auszuführen. Wir erkunden die Vorteile von Auswertungen direkt an der Quelle der Daten und die damit verbundenen Herausforderungen. Denn die Rechenleistung der Cloud steht uns hier leider nicht zur Verfügung. Zur Lösung stehen uns verschiedene Hardwareoptionen wie CPUs, GPUs, FPGAs oder spezielle ASICs und Frameworks zur Verfügung, die wir am Beispiel von einem Convolutional Neural Network evaluieren. Dabei gibt es praktische Tipps und Erfahrungen aus realen Projekten sowie anschauliche Demos auf verschiedenen Hardwareplattformen. Vorkenntnisse: Vorkenntnisse über tiefe neuronale Netze sind von Vorteil. Lernziele: - Verständnis über die Vorteile von AI auf Edge-Geräten und den damit verbundenen Herausforderungen. - Wissen über die verschiedenen Hard- und Softwarelösungen erlangen, um diese in eigenen Projekten einzusetzen. Event: building IoT, 03.04.2019 Speaker: Dominik Helleberg, inovex Mehr Tech-Vorträge: inovex.de/vortraege Mehr Blog-Artikel: inovex.de/blog

AI auf Edge-Geraeten

AI auf Edge-Geraeten

AI auf Edge-Geraeten

Der Talk auf der Konferenz „Talk4Nerds“ der R+V Versicherung bot eine Einführung in Prometheus als Monitoring-Lösung. Dabei ging inovexler Christoph auf die Anforderungen an ein modernes Monitoring Tool ein, wie Prometheus diesen Anforderungen entspricht und warum es zum defacto Standard im Kubernetes-Umfeld geworden ist. Abschließen beleuchtete Christoph die Non-Goals und wie man diese mit zusätzlichen Tools dennoch erreichen kann. Speaker: Christoph Petrausch (inovex) Event: Talk4Nerds Datum: 29.04.2019 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Prometheus on Kubernetes

Prometheus on Kubernetes

Prometheus on Kubernetes

Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds. Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019 Speaker: Marcel Kurovski, inovex GmbH Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

In seinem Meetup Talk berichtete Maximilian von den aktuellen Problemen von Cloud Computing – insbesondere im Internet of Things – und wie diese durch Edge Computing mitigiert werden können. Er erklärte, wie eine generische Edge-Computing-Architektur aussehen kann und zeigte Anwendungsfälle, von denen manche auch schon in existierenden Produkten umgesetzt sind. Im Anschluss stellte er Azure IoT Edge vor und erläuterte, wie es das bestehende IoT Framework von Microsoft erweitert sowie die Grundkonzepte, die IoT Edge bereitstellt. Auch die Probleme in dem noch jungen Produkt wurden angesprochen, aber auch die Vorteile und Features, die es liefert. In der gemeinsamen Demo mit Eli haben dann beide Speaker die technischen Details von Azure IoT Edge gezeigt und demonstriert, beispielsweise wie Code automatisiert von einer CI/CD-Pipeline in Azure DevOps auf ein IoT-Gerät deployed werden kann. Event: inovex Meetup, 12.03.& 19.03.2019 Speaker: Maximilian Bischoff, inovex Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Azure IoT Edge

Es liegt in der Natur des Menschen das Unvorhersehbare vorherzusagen: Wetter, Aktienkurse, Krankheitsverläufe, die Reaktion eines Menschen. Neueste Deep Learning Ansätze sind in der Lage solche sequentielle Sachverhalte immer genauer zu prognostizieren, setzen aber auch immer größere Datenmengen und Rechenleistungen voraus, die sowohl in Forschung als auch in der Praxis häufig nicht vorliegen. Wie kann man gute Ergebnisse erreichen, wenn nur wenig Daten vorliegen? Marisa Mohr stellte in ihrem Vortrag einen neuen und vielversprechenden informationstheoretischen Ansatz zum Feature Learning von sequentiellen Daten vor, der potenziell auch mit wenigen Daten auskommt. Dabei ging es speziell um ordinale Muster in Zeitreihen, wie sie beispielsweise als Veränderung von Emotionen im Gesprächsverlauf zu finden sind. Eine solche Entwicklung ist für Menschen in der Regel leicht zu erkennen. Chatbots hingegen können nicht intuitiv auf solche Emotionsverläufe reagieren, sondern müssen entsprechend programmiert werden. Details: Deep-Learning-Ansätze wie LSTMs, RNNs oder TCNs haben sich im Umgang mit sequentiellen Daten bewährt. Neuronale Netzwerke sind tief im technischen Sinn, weil sie mehrere (verborgene) Schichten besitzen, aber nicht weil sie ein tiefes Verständnis von Problemen entwickeln. In diesem Vortrag stellte Marisa einen symbolischen informationstheoretischen Ansatz des Representation Learnings von Zeitreihen vor und damit eine Möglichkeit, konzeptionelle Schichten zu konstruieren. Die Idee hinter der sogenannten Permutationsentropie besteht darin, anstelle der Werte einer Zeitreihe die Ordnungsrelation zwischen den Werten zu betrachten, und so auf das natürliche Auf und Ab des zugrundeliegenden dynamischen Systems zurückzugreifen. Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019 Speakerin: Marisa Mohr (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen. In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen. Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019 Speaker: Anna Weißhaar (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Nicht zuletzt durch die medienwirksame Erfolge des maschinellen Lernens durch DeepMind, OpenAI und Kollegen ist Künstliche Intelligenz im Moment wieder in aller Munde. Einerseits locken zahlreiche neue, vorher undenkbare Anwendungen wie die automatische Diagnose von Krankheiten, autonome Fahrzeuge und Drohnen, oder die automatische Übersetzung gesprochener Wörter. Andererseits warnen mahnenden Stimmen wird vor dem zunehmendem Einflussnahme der „Algorithmen“ auf fast alle Bereiche unseres Lebens sowie vor unerwünschten Folgen von sich verselbstständigenden Computern gewarnt. Einige träumen von – oder fürchten sich vor – der vermeintlich unausweichlichen Singularität, an der sich nichts weniger als das Schicksal der gesamten Menschheit entscheiden wird. Doch was verbirgt sich hinter dem Begriff Künstliche Intelligenz? Je nachdem, wen man fragt, erhält man unterschiedliche, bisweilen gegensätzliche Antworten. Dieser Vortrag stellt einige dieser Antworten vor und versucht sie (nicht nur) anhand von Beispielen aus Forschung und Anwendung einzuordnen. Event: Business Analytics Day, 07.03.2019 Speaker: Dr. Matthias Richter, Dr. Stefan Igel (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Künstlich intelligent?

Künstlich intelligent?

Künstlich intelligent?

In den letzten drei Jahren haben wir die Infrastruktur der Fernseh-Plattform waipu.tv gebaut. Dabei haben wir angefangen Tools für den Betrieb in Golang zu schreiben. Aus einigen der Tools wurden Core-Services, die auch die Last einer Fußball-WM-Übertragung locker wegstecken. Wir wollen euch zeigen, wie wir mit der selben Tool-Chain (Golang & Co) Betriebs-Probleme lösen und kritische Business-Applikationen entwickeln. Klassisch DevOps oder Golden Hammer? Speaker: Christoph Petrausch, Igor Lankin (beide inovex) Event: DevOpsConference, 04.12.2018 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Dev + Ops = Go

Das Android Open Source Project, kurz AOSP, ist das Betriebssystem, das auf den meisten heutigen und wahrscheinlich auch auf deinem Smartphone läuft. Es ist die Basis für das Android-App-Universum und wird von Millionen Nutzern und Entwicklern auf der Welt verwendet. Wegen der offenen Verfügbarkeit des Source Codes ist es auch die Basis für bekannte Custom ROMs wie LineageOS. Der erste Teil des Talks gab eine Übersicht über die Architektur des Betriebssystems, das App-Ökosystem, den Hardware Abstraction Layer (HAL), die Sicherheitskonzepte und einige neue Betriebssystementwicklungen wie Project Treble in Android 8.0. Der zweite Teil des Talks gab einen Einblick in den Quellcode und die Struktur des AOSP: Wie lädt man sich den Source Code herunter, wie baut man das AOSP für unterstützte Geräte und wie kann man die eigenen ROMs auf ein Smartphone flashen? Zum Spaß wurde auch noch in einige Implementierungsdetails von Android-App-API-Funktionen geblickt, die man als App Developer schon aufgerufen hat. Speaker: Stefan Lengfeld, inovex Event: inovex Meetup Köln, 23.10.2018 Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Das Android Open Source Project

Das Android Open Source Project

Das Android Open Source Project

Interpretable Machine Learning describes the process of revealing causes of predictions and explaining a derived decision in a way that is understandable to humans. The ability to understand the causes that lead to a certain prediction enables data scientists to ensure that the model is consistent to the domain knowledge of an expert. Furthermore, interpretability is critical to obtain trust in a model and to be able to tackle problems like unfair biases or discrimination against particular subgroups. This talk covers an introduction to the concept of interpretability and an overview of popular interpretability techniques. Speaker: Marcel Spitzer, inovex Event: Kaggle Munich Meetup, 20.11.2018 Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Machine Learning Interpretability

Machine Learning Interpretability

Machine Learning Interpretability

Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically. Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data. What you'll learn: Understand how semisupervised learning with GANs works Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data Gain insight into an interesting OCR use case of an online vehicle marketplace Event: O'Reilly Artificial Intelligence Conference, London, 11.10.2018 Speaker: Dr. Florian Wilhelm Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

IT im 21. Jahrhundert – What a time to be alive! Es gibt einen (unüberschaubaren) Zoo an Methoden und Produkten die uns so viel Freude an der Arbeit bereiten! Sie sind modern, weil sie neu sind. Sie fordern unser Können heraus, weil sie komplex sind. Sie lösen einige Probleme, die wir vorher nicht hatten. Jeder will sie verwenden, weil Google, Netflix & Co. sie propagieren und Hand auf’s Herz: Will nicht jeder gerne so arbeiten wie Google, Netflix & Co.? Aber macht das wirklich Sinn? In diesem Vortrag blicken wir auf diverse Erkenntnisse aus dem Einsatz agiler Produktentwicklung, DevOps, Continuous Integration/Delivery, Infrastructure as Code, Immutable Infrastructure (bspw. Docker/Kubernetes), Application Logging und Service Monitoring. Learning Goals: - Wir müssen den Einsatz von Methoden und Tools an die Menschen ausrichten, die sie (weiter-)entwickeln und benutzen sollen. - Manchmal lösen wir mit neuen Tools Probleme, die wir vorher nicht hatten. - Die Suche nach einfachen Lösungen für komplexe Probleme ist essentiell, aber nicht immer einfach. Event: Continuous Lifecycle, 15.11.2018 Speaker: Arnold Bechtoldt Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

Pulumi (pulumi.io) offers an open source platform to create/manage and deploy your infrastructure in realy programming languages like JavaScript/TypeScript, Go and Python. As Cloud platforms the major 3 cloud providers are supported and additionally you can also use Pulumi with OpenStack and Kubernetes to deploy your applications in the cloud. In this talk we will take a look how Pulumi is different to traditional solutions like Terraform or the Cloud Provider specific solutions (e.g. CloudFormation). The main focus will be on deploying your services on top of Kubernetes. The talk will contain a little theory part about Pulumi, the rest of the talk is more focused on demos and practical parts. One focus of the talk is the difference of Pulumi to kubectl and helm (or to be precise how they complement each other. As a takeaway of this talk you should understand the basics of Pulumi and know what are the differences to the traditional deployment tools. Event: CNCF Meetup Hamburg & Stuttgart, 29.10.2018 & 07.11.2018 Speaker: Johannes M. Scheuermann, inovex Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/ Mehr Tech-Artikel: https://www.inovex.de/blog/

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Mais de inovex GmbH (20)

lldb – Debugger auf Abwegen

lldb – Debugger auf Abwegen

lldb – Debugger auf Abwegen

Are you sure about that?! Uncertainty Quantification in AI

Are you sure about that?! Uncertainty Quantification in AI

Are you sure about that?! Uncertainty Quantification in AI

Why natural language is next step in the AI evolution

Why natural language is next step in the AI evolution

Why natural language is next step in the AI evolution

WWDC 2019 Recap

WWDC 2019 Recap

WWDC 2019 Recap

Network Policies

Network Policies

Network Policies

Interpretable Machine Learning

Interpretable Machine Learning

Interpretable Machine Learning

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

AI auf Edge-Geraeten

AI auf Edge-Geraeten

AI auf Edge-Geraeten

Prometheus on Kubernetes

Prometheus on Kubernetes

Prometheus on Kubernetes

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

Azure IoT Edge

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Künstlich intelligent?

Künstlich intelligent?

Künstlich intelligent?

Dev + Ops = Go

Das Android Open Source Project

Das Android Open Source Project

Das Android Open Source Project

Machine Learning Interpretability

Machine Learning Interpretability

Machine Learning Interpretability

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

Último

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

The Digital Insurer

Abhishek Deb(1), Mr Abdul Kalam(2) M. Des (UX) , School of Design, DIT University , Dehradun. This paper explores the future potential of AI-enabled smartphone processors, aiming to investigate the advancements, capabilities, and implications of integrating artificial intelligence (AI) into smartphone technology. The research study goals consist of evaluating the development of AI in mobile phone processors, analyzing the existing state as well as abilities of AI-enabled cpus determining future patterns as well as chances together with reviewing obstacles as well as factors to consider for more growth.

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Principled Technologies

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Maria Levchenko

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Enterprise Knowledge

2024: Domino Containers - The Next Step. News from the Domino Container commu...

2024: Domino Containers - The Next Step. News from the Domino Container commu...

2024: Domino Containers - The Next Step. News from the Domino Container commu...

Martijn de Jong

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Último (20)

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

[2024]Digital Global Overview Report 2024 Meltwater.pdf

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

Exploring the Future Potential of AI-Enabled Smartphone Processors

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Boost PC performance: How more available memory can improve productivity

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

Handwritten Text Recognition for manuscripts and early printed texts

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

How to Troubleshoot Apps for the Modern Connected Worker

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

Driving Behavioral Change for Information Management through Data-Driven Gree...

2024: Domino Containers - The Next Step. News from the Domino Container commu...

2024: Domino Containers - The Next Step. News from the Domino Container commu...

2024: Domino Containers - The Next Step. News from the Domino Container commu...

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Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

Artificial Intelligence: Facts and Myths

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

A Domino Admins Adventures (Engage 2024)

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

Real Time Object Detection Using Open CV

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Apidays New York 2024 - The value of a flexible API Management solution for O...

Gitlab meets Kubernetes

1. Gitlab meets Kubernetes Benjamin Stein Arnold Bechtoldt r2

2. Assets & Links 2 gitlab.com/inovex/pa-todo-app-web

3. Scenario: ToDo Webapp

4. 4 Introduction ● Use Case: Managing Tasks ● Golang, Redis (Master/Slave) ● Gitlab (without CI), Jenkins CI

5. Architecture 5 Redis (Master) Redis Slave ToDo Webapp

7. Team Ops (+ QA) Team Dev Current Deployment Pipeline 7 Push Code Trigger Tests Build Artifact Release Artifact Deploy Staging QAManual Testing Deploy Prod

8. ★ Improve Tool Integration ★ Automate Everything ★ Make Deployments Great Again Goals 8

9. Make Deployments Great Again CC BY-SA 2.0 https://www.flickr.com/photos/gageskidmore/29273060912/in/album-72157673261299025/

10. Team DevOps (+ QA) Improved Deployment Pipeline 10 Push Code Trigger Tests Build Artifact Release Artifact Deploy Staging QAAutomated Testing Deploy Prod

11. Technologies 11 Gitlab KubernetesDocker

12. Demo Time 12 You know what happens with live demos? Right, they always fail!

13. Demo Time: Video! 13 youtu.be/Ly_f221O6Fo

14. Conclusions

15. ★ Improve Tool Integration ★ Automate Everything ★ Make Deployments Great Again Goals (fixed) 15

16. TL;DR: 16 Improve the integration of your deployment toolstack e.g. with Gitlab CI and Kubernetes and get back the flexibility you ever wanted!

17. Benjamin Stein Systems Engineer & Consultant @ inovex 〉 Software-Defined Datacenters 〉 Continuous Integration/Delivery 〉 DevOps 17 inovex.de/jobs

18. Arnold Bechtoldt Systems Engineer & Consultant @ inovex 〉 Software-Defined Datacenters 〉 Continuous Integration/Delivery 〉 DevOps 18 inovex.de/jobs

19. Benjamin Stein inovex GmbH bstein@inovex.de github.com/ben-st inovex.de youtube.com/inovexGmbH CC BY-NC-ND diffus.org gitlab.com/inovex abechtoldt@inovex.de Arnold Bechtoldt inovex GmbH github.com/bechtoldt inovex.de youtube.com/inovexGmbH CC BY-NC-ND arbe.io gitlab.com/inovex Feedback & Questions: sayat.me/benst Feedback & Questions: sayat.me/arbe