Bachelor-Level

• B.Sc.: Big Data and Cloud Computing for AI

  • **GitOps** – Cloud-native Continuous Deployment - community.GitHub.io/kit/)** (Weaveworks)
  • Umsetzungshilfe für Lehrkräfte - Berufe/HR_technische_it_ausbildungsberufe_2020.pdf)\] des ISB (= Staatsinstitut für Schulqualität und Bildungsforschung München)
  • Illustrierende Aufgaben für technische IT-Ausbildungsberufe - Dialog** ([10. Jgst](https://www.isb.bayern.de/schularten/berufliche-schulen/berufsschule/materialien/technische-und-kaufmaennische-it-ausbildungsberufe/it-digital-dialog/) und [11. Jgst](https://www.isb.bayern.de/schularten/berufliche-schulen/berufsschule/materialien/technische-und-kaufmaennische-it-ausbildungsberufe/it-digital-dialog-11-jgst/))
  • **Rahmenlehrplan** für die Ausbildungsberufe Fachinformatiker/in in der Fachrichtung Systemintegration
  • Fachinformatiker/in - Systemintegration - Digitale Vernetzung](https://web.arbeitsagentur.de/berufenet/beruf/133560))
  • **Windows Server** und Active Directory
  • 2017 Linux Kernel Report - Computing-Dienste auf Linux laufen**, 82 Prozent der Smartphones, 62 Prozent der eingebetteten Systeme und 99 Prozent der Supercomputer.
  • What are the **Best SysOps Tools**?
  • **Ansible Tutorial** for Beginners: Playbook, Commands & Example
  • **Ops School** Curriculum
  • Monorepos - vs-polyrepo/):
  • Monorepo: please do!
  • Monorepos: please don't!
  • SBOM - sboms-strengthen-software-supply-chain)
  • Awesome SBOM
  • cpnatwork/alphaflow_dev - buildhub-Modul für die Build-Komposition und mit \*configbase-Modulen für Abhängigkeitskonsolidierung mittels Maven-POM-Vererbung)
  • Developing **With Project Dependencies - When to Package, When Not To**
  • Managing output in the .NET SDK projects
  • .NET Core in Action
  • ProjectReference - does-project-item-metadata-preservenewest-actually-work) sowie „Copy Local“ = Private:false) vs. **[PackageReference](https://learn.microsoft.com/en-us/nuget/consume-packages/package-references-in-project-files)** (u.a. „NOT Copy Local“ = [ExcludeAssets:runtime](https://learn.microsoft.com/en-US/nuget/consume-packages/package-references-in-project-files#controlling-dependency-assets) sowie [PrivateAssets:all](https://learn.microsoft.com/en-us/answers/questions/702182/how-to-hide-dependent-nuget-dll39s-from-consuming.html) und im Legacy-Build-Kontext ggf. [CopyLocalLockFileAssemblies:true](https://stackoverflow.com/questions/43837638/how-do-i-get-net-core-projects-to-copy-nuget-references-to-the-build-output/52824190#52824190))
  • **Quickstart** to create a NuGet package - klaus.com/better-nuget/)** von Alex Klaus | [Best Practices for **Versioning** NuGet **Packages** in the Enterprise](https://blog.inedo.com/nuget/package-versioning) (2021) by Eric Seng | **[NuGet in the Enterprise](https://blog.inedo.com/nuget/in-the-enterprise-2021)** (2021) by Eric Seng | [Prerelease Packages & **Repackaging**](https://docs.inedo.com/docs/proget-packages-repackaging) (2021) | [Managing the global packages, cache, and temp folders](https://learn.microsoft.com/en-us/nuget/consume-packages/managing-the-global-packages-and-cache-folders)
  • Adding both Project and Package references
  • Continuous Integration
  • CI/CD on Google Cloud - started/hands-on/set-up-ci-cd-pipeline/) | Microsoft [CI/CD-Baseline-Architektur mit Azure Pipelines](https://learn.microsoft.com/en-us/azure/devops/pipelines/architectures/devops-pipelines-baseline-architecture)
  • Awesome CI/CD
  • Release It!
  • Magic Quadrant für ITSM
  • guru99 **What is ITIL?** - tutorial)**
  • Google **SRE Books** - im-engsten-Sinn = erste brauchbare Implementierung von DevOps für Hochgeschwindigkeit-IT)
  • DevOps Engineer, SRE Learning Path
  • SRE implements DevOps
  • Engineering for Reliability
  • Google SRE - Seite
  • Awesome SRE
  • Semantic Versioning
  • A Beginner's Guide to DevOps
  • Awesome DevOps » Books
  • Awesome Monorepo
  • **Release Pipelines** – Terminology und Basiskonzepte am Bsp. TFS/Azure
  • DevOps auf hackr.io - Selektion)
  • Configuration Management - management/), [Dependency Hell](https://en.wikipedia.org/wiki/Dependency_hell) ([DLL \[Heck\]](https://en.wikipedia.org/wiki/DLL_Hell), [NuGet hell](https://www.google.com/search?q=nuget+hell), …), [Dependency Manager](https://devopedia.org/dependency-manager), [Build-Artifact Management](https://www.leanix.net/en/wiki/vsm/software-artifacts), [Release Management](https://en.wikipedia.org/wiki/Release_management), [Release Automation](https://en.wikipedia.org/wiki/Application-release_automation), [Software-Artifact Repository](https://en.wikipedia.org/wiki/Software_repository), [Continuous Integration](https://en.wikipedia.org/wiki/Continuous_integration), [Infrastructure Automation / GitOps](https://about.gitlab.com/topics/gitops/), [Infrastructure as Code](https://en.wikipedia.org/wiki/Infrastructure_as_code), [Configuration as a Code](https://www.perforce.com/blog/vcs/configuration-as-code), [Virtualization](https://en.wikipedia.org/wiki/Hardware_virtualization), [Container](https://en.wikipedia.org/wiki/OS-level_virtualization) & [Containerization](https://aws.amazon.com/de/what-is/containerization/), [Consistent Environments](http://engineering-principles.onejl.uk/practices/Consistent_Environments.html), [Image Repositories](https://www.aquasec.com/cloud-native-academy/container-security/image-repository/), [Continuous Delivery](https://en.wikipedia.org/wiki/Continuous_delivery), [Deployability](https://en.wikipedia.org/wiki/Deployment), [Modifiability](https://iso25000.com/index.php/en/iso-25000-standards/iso-25010/57-maintainability), [Testability](https://en.wikipedia.org/wiki/Software_testability), [Monitorability/SLO-Überwachung](https://cloud.google.com/monitoring), [Maintainability](https://en.wikipedia.org/wiki/Software_maintenance), [Twelve Factor App Methodology](https://12factor.net/), [Site Reliability Engineering](https://en.wikipedia.org/wiki/Site_reliability_engineering)
  • Best **DevOps** Blogs and **Resources for Learning** - DevOps/awesome-learning) | roadmap.sh [DevOps Roadmap](https://roadmap.sh/devops) | [Awesome **Terraform**](https://github.com/cloud-architecture/awesome-terraform)
  • DevSecOps
  • Availability and Uptime
  • Already requires careful considerations
  • 5 nines - On Infrastructure](https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/s-99.999-or-higher-scenario-with-a-recovery-time-under-1-minute.html) = Continuous Availability**
  • some ATM switches running on Erlang
  • Künstliche Intelligenz und Wir
  • **Artificial General Intelligence** (AGI)
  • Stanford ML
  • 10 Types of Machine learning Algorithms and Their Use Cases
  • Top 15 Machine Learning Algorithms Every Data Scientist Should Know
  • A Beginner’s Guide to the Top 10 Machine Learning Algorithms
  • Learn the Foundations of Machine Learning and Artificial Intelligence
  • Dive into Deep Learning (**D2L**)
  • Deep Learning
  • How a stubborn computer scientist accidentally launched the deep learning boom - computers-got-shockingly-good-at-recognizing-images/)
  • Reinforcement Learning
  • **Reinforcement Learning**: An Introduction - Version) von Sutton & Barto
  • **Deep RL** Bootcamp
  • Making Friends with machine learning (**MFML**)
  • Big Book of **Data Science** Use Cases - book-of-machine-learning-use-cases)
  • YOLO-NAS: SoTA Foundation Model for Object Detection
  • Mops vs. Muffins - learning AIs are so easy to fool](https://www.nature.com/articles/d41586-019-03013-5) (2019) by Douglas Heaven
  • Brillengestelle
  • GLAZE
  • Tools-Seite BDCC, Abschnitt Data Science
  • **Generative Adversarial Networks**: Build Your First Models - learning/gan)
  • What are Large Language Models
  • A jargon-free explanation of how AI large language models work
  • State of Process Automation
  • Learn Generative AI for Developers
  • ChatGPT: Everything You **Need to Know** - python-programming-assistant.html)
  • GPT-3's family tree - Paper von Google](https://arxiv.org/abs/1706.03762) bekannt)
  • LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide
  • Top Open Source (Free) Text to Code Generator models on the market
  • Awesome OpenAI
  • Awesome Text-to-Image
  • LEAM
  • Awesome Generative AI - generative-deep-art), [Awesome MVS](https://github.com/walsvid/Awesome-MVS)
  • Google Prompting Essentials
  • Prompt Engineering 101 - Introduction and resources - engineering-101) | roadmap.sh [Prompt Engineering Roadmap](https://roadmap.sh/prompt-engineering)
  • ChatGPT-**for-Data-Science**-Cheatsheet - **Cheatsheet**](https://www.kdnuggets.com/publications/sheets/ChatGPT_Cheatsheet_Costa.pdf)
  • ChatGPT4 – Potential Scenarios For Accelerated Cybercrime
  • The Ultimate **Stable Diffusion Prompt Guide** - adobe-firefly/) (2023) von Tory Barber
  • How to Use ChatGPT to Write Prompts & Prompt Templates for Adobe Firefly & Midjourney
  • Chain of Command
  • 20 ChatGPT Prompts - in-one AI Cheat Sheet](https://images.app.goo.gl/uvfr5XsuUdHD2gRYA), [Prompt Engineering Mistakes](https://images.app.goo.gl/ANUTJHrRc6QzM1ak6)
  • Effektives Prompting in der Pädagogik
  • prompts.chat - prompts), Alexandria [Propmt Library](https://aiexandria.com/), Temaniaga [Prompt Hub for Business](https://www.temaniaga.com/apps/ai-prompt-hub/), [Awesome ChatGPT Prompts](https://github.com/f/awesome-chatgpt-prompts)
  • GodOfPrompt
  • Cookbook - prompts)
  • PromptHero
  • PromptHero
  • loveable
  • Awesome Prompt Engineering - Liste [Useful Prompt Engineering tools and resources](https://www.reddit.com/r/StableDiffusion/comments/xcrm4d/useful_prompt_engineering_tools_and_resources/) | lablab [AI Tutorials](https://lablab.ai/t) | [Awesome Text-to-Image](https://github.com/Yutong-Zhou-cv/Awesome-Text-to-Image)
  • Handbook of Robotics
  • Bib/OPAC - 3-319-32552-1)\]
  • Handbuch Robotik
  • Mobile Robotik
  • Awesome Robotics - robotics) #2 | [Robotics Coursework](https://github.com/mithi/robotics-coursework) | [Learning Optimal Control](https://github.com/martinseilair/learningoptimalcontrol)
  • Introduction to **Operations Research**
  • Operations Research: Applications and Algorithms
  • Übersichtsgrafik **OR**
  • What types of optimization problems does SCIP solve?
  • LP
  • NLP
  • lm-AMZN-tfy
  • Convex Optimization
  • Convex Optimization
  • SO
  • Metaheuristiken
  • Genetische Algorithmen - Algorithmen](https://en.wikipedia.org/wiki/Swarm_intelligence)
  • VRP
  • **Vehicle Routing**: Problems, Methods, and Applications
  • Computational Optimal Transport
  • Awesome Operational Research - Operations-Research/blob/main/books.md) #2 | [Awesome OR-Tools (Sect. Research)](https://github.com/or-tools/awesome_or-tools?tab=readme-ov-file#research)
  • kostenlose Bücher über OR
  • OR: Models and Applications - research-algorithms-33430)
  • **AI for TSP** competition
  • AMARL: An Attention-Based Multiagent Reinforcement Learning Approach to the Min-Max Multiple Traveling Salesmen Problem
  • Learn to Solve the Min-max Multiple Traveling Salesmen Problem with Reinforcement Learning
  • Harvard CS109 Data Science - data-science)** | [**WolframAlpha** University Free Interactive Courses](https://www.wolfram.com/wolfram-u/courses/catalog/?f_format%5B%5D=interactive-course&q=&f_button=filters)
  • Statistical Learning with Python - university-statistical-learning)
  • Understanding Data
  • 5 **Statistical Paradoxes** Data Scientists Should Know
  • Data Science Cheatsheet 2.0
  • Mojo - und Multicore-Optimierungen, will Python als Data Science Programmiersprache #1 ablösen
  • HowTo **PostgresML**
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • The Data Science Design Manual
  • **5 Ways to Detect Outliers/Anomalies** That Every Data Scientist Should Know
  • Why **“1.5“ in IQR Method** of Outlier Detection?
  • Awesome **Outlier Detection** Resources - anomaly-detection)
  • Types of Maintenance - maintenance/) für Non-Technical-Readers
  • Buchwissen
  • State-of-the-Art **Predictive Maintenance Techniques**
  • An Introduction to Predictive Maintenance
  • **Decision Making in Predictive Maintenance**: Literature Review and Research Agenda for Industry 4.0
  • Computerized Maintenance Management Systems
  • Understanding the **World Through Data** - to-computational-thinking-and-data-4), [**Machine Learning** with Python: from Linear Models to Deep Learning](https://www.edx.org/course/machine-learning-with-python-from-linear-models-to) und [Computational Thinking for **Modeling and Simulation**](https://www.edx.org/course/computational-thinking-for-modeling-and-simulation) sowie als Anwendungsgebiet per **[Supply Chain Analytics](https://www.edx.org/course/supply-chain-analytics)**
  • Awesome MLOps
  • Liste per Awesome-LLM
  • Eric Hartford
  • Arditi et al. - to-Image ([am Bsp. Flux.1](https://medium.com/@aloshdenny/uncensoring-flux-1-dev-abliteration-bdeb41c68dff))
  • "eric hartford" - text?q=abliterate&type=model), ["unsensored"](https://huggingface.co/search/full-text?q=_uncensored&type=model)
  • Andrej Karpathy
  • The End of Software Engineering (as we know it) - end-of-system-architects/))
  • **Data Vault** 2.0
  • Awesome DevOps » Books
  • Künstliche Intelligenz und Wir
  • Künstliche Intelligenz und Wir
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • Künstliche Intelligenz und Wir
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • Formen der Heterogenität
  • Künstliche Intelligenz und Wir
  • Least squares quantization in PCM - Hotch-Potch](https://de.wikipedia.org/wiki/K-Means-Algorithmus#Historische_Entwicklung))
  • Visualizing Data using t-SNE
  • ELKI - studio)
  • scikit-learn
  • ML.NET - framework.net/)
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • Four Types of Analytics
  • Spurious Correlations
  • BI Dashboard Design Regeln für die Gestaltung aussagekräftiger Dashboards
  • The Data Visualisation Catalogue
  • Tableau Charts & Graphs Tutorial
  • 12 lesenswerte Bücher über Datenvisualisierung
  • Bücherliste zu Datenvisualisierung
  • Awesome DataViz
  • **Delta Lake** - source storage layer that brings ACID transactions, data reliability, and performance to data lakes)
  • part 1 - vanlightly.com/analyses/2024/8/5/apache-icebergs-consistency-model-part-2), [part 3](https://jack-vanlightly.com/analyses/2024/8/6/apache-icebergs-consistency-model-part-3) (open-source; invented at Netflix)
  • part 1 - vanlightly.com/analyses/2024/4/24/understanding-apache-hudi-consistency-model-part-2), [part 3](https://jack-vanlightly.com/analyses/2024/4/25/understanding-apache-hudi-consistency-model-part-3) (open-source; invented at Uber)
  • part 1 - vanlightly.com/analyses/2024/7/3/understanding-apache-paimon-consistency-model-part-2), [part 3](https://jack-vanlightly.com/analyses/2024/7/3/understanding-apache-paimon-consistency-model-part-3) (born in the Apache Flink project where it was known as Flink Table Store)
  • Fluss
  • Understanding Kafka Replication Protocol
  • part1 - maas/apache-bookkeeper-insights-part-2-closing-ledgers-safely-386a399d0524?source=user_profile_page---------1-------------f7a88d0c88e5----------------------) ([more](https://medium.com/@jvanlightly))
  • Principles of **Data Integration**
  • 97 Things Every **Data Engineer** Should Know
  • **10+ Deploys per Day**: Dev and Ops Cooperation at Flickr
  • Künstliche Intelligenz und Wir
  • Handbook of Robotics
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • An Introduction to Predictive Maintenance
  • Künstliche Intelligenz und Wir
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • **Kubernetes Networking** Teil 1: Networking Essentials von Simon Kurth - networking-2-calico-cilium-weavenet/) | Webartikel [Container richtig vernetzen](https://www.linux-magazin.de/ausgaben/2017/08/netz-in-kubernetes/) von Thomas Fricke | Kubernetes [Networking Design Document](https://github.com/kubernetes/design-proposals-archive/blob/main/network/networking.md) | [Kubernetes **Security** Best Practices Part 2: **Network Policies**](https://engineering.dynatrace.com/blog/kubernetes-security-best-practices-part-2-network-policies/) von Renato Schosser
  • Künstliche Intelligenz und Wir
  • **Guide to Intelligent Data Science** – How to Intelligently Make Use of Real Data
  • Understanding the **World Through Data** - to-computational-thinking-and-data-4), [**Machine Learning** with Python: from Linear Models to Deep Learning](https://www.edx.org/course/machine-learning-with-python-from-linear-models-to) und [Computational Thinking for **Modeling and Simulation**](https://www.edx.org/course/computational-thinking-for-modeling-and-simulation) sowie als Anwendungsgebiet per **[Supply Chain Analytics](https://www.edx.org/course/supply-chain-analytics)**
  • **Data Ingestion** vs. Data Integration: How Are They Different?
  • Top 10 **SQL Projects** for Data Analysis - analysis-using-sql)
  • **Cloud Computing** nach der Datenschutz-Grundverordnung
  • **AWS** Educate - de/learn/browse/)
  • Prime Video: From distributed microservices to a monolith application
  • Umsetzungshilfe für Lehrkräfte - Berufe/HR_technische_it_ausbildungsberufe_2020.pdf)\] des ISB (= Staatsinstitut für Schulqualität und Bildungsforschung München)
  • Illustrierender Aufgabenpool für technische IT-Ausbildungsberufe - Dialog** ([10. Jgst](https://www.isb.bayern.de/schularten/berufliche-schulen/berufsschule/materialien/technische-und-kaufmaennische-it-ausbildungsberufe/it-digital-dialog/) und [11. Jgst](https://www.isb.bayern.de/schularten/berufliche-schulen/berufsschule/materialien/technische-und-kaufmaennische-it-ausbildungsberufe/it-digital-dialog-11-jgst/))
  • **Windows Server** und Active Directory
  • How to protect **GitHub** projects from non-reviewed code and **force code review culture**