Scalability Archive - Rock the Prototype - Softwareentwicklung & Prototyping

AI language models

Sascha Block2025-02-05T13:55:57+01:00Tags: AGI, AI ethics, AI for companies, AI Governance, AI in everyday life, AI in research, AI language models, AI Research, AI safety, AI-supported creativity, Alexa, Algorithmic Bias, Artificial intelligence, Attention mechanism, Autoencoding, Autoregressive models, BERT, Bias in AI, bidirectional models, Chatbots, CLIP, Cloud computing, Computational linguistics, Computing power, Conversational AI, DALL-E, Data ethics, Deep learning, DeepMind, digital assistants, Embeddings, Energy-efficient AI, Explainability, Fairness in AI, Few-Shot Learning, Few-Shot Prompting, Fine tuning, GDPR, generative AI, generative language models, GitHub Copilot, GloVe, Google AI, Google Assistant, GPT, GPT-2, GPT-3, GPT-4, GPUs, Hallucinations in AI, Hugging Face, Information retrieval, Interpretability, Knowledge Graphs, Language processing, Large Language Models, LLM, LSTM, machine learning, machine translation, Masked Language Modeling, Meta AI, Model compression, multimodal AI, multimodal models, Named Entity Recognition, natural language processing, neural networks, NLP, Open source AI, OpenAI, OpenAI Codex, Perplexity, Privacy, Prompt Engineering, Prompt Tuning, Regulation, Reinforcement Learning, Responsible AI, Retrieval-Augmented Generation, RNN, Scalability, Self-Attention, Sentiment analysis, Siri, Supervised Learning, synthetic data, T5, Text classification, Text generation, Tokenizer, TPUs, Training data, Transfer Learning, Transformer, Unsupervised Learning, Voice assistants, Word2Vec, Zero-shot learning|

AI language models - what's behind them? Artificial intelligence is changing the way we interact with machines. Language models enable machines not only to understand language, but also to generate meaningful, human-like responses. Find out more now...

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Architecture Trade-off Analysis Method (ATAM) – Software Architecture Reviews

Sascha Block2024-09-17T17:46:45+02:00Tags: Architectural decisions, Architectural goals, Architectural pattern, Architecture analysis, Architecture Trade-off Analysis Method, ATAM, Complexity, Critical infrastructures, Design Reviews, Design styles, Evaluation processes, Maintainability, Performance, Project success, Quality attributes, Quality requirements, Quality scenarios, Risk analysis, Risk reduction, Scalability, Security, Software Architecture, Software Architecture Analysis Method, Software projects, Stakeholders, Strengthening communication, Trade-offs|

What is the Architecture Trade-off Analysis Method (ATAM)? If you have ever worked with complex systems as a software architect, you know how crucial these trade-offs are for the long-term success of a project. ATAM helps you to better understand these trade-offs and identify potential risks early on before they have a negative impact in later development phases.

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Deep learning frameworks

Sascha Block2025-02-01T17:01:23+01:00Tags: Activation Functions, AI benchmarking, AI deployment in the cloud, AI ethics, AI for autonomous systems, AI for companies, AI for image recognition, AI for language models, AI for recommendation systems, AI for robotics, AI for word processing, AI Frameworks, AI in medicine, AI in production, AI in the automotive industry, AI in the financial sector, AI infrastructure, AI optimization, AI Performance, AI Pipeline, AI Research, AI toolkits, AI training, Algorithmic fairness, Algorithmic optimization, Artificial intelligence, artificial neural networks, Autoencoder, AutoML, Backpropagation, Batch Normalization, BERT, Bias in AI, Big Data, Caffe, Cloud-based AI, Clustering, CNTK, Computer Vision, Convolutional Neural Networks, Cross Validation, Data Augmentation, Data Parallelism, Data Science, Deep learning, Deep reinforcement learning, DeepMind, Deployment of AI models, Deployment strategies, Dropout, Edge AI, Edge Computing, Explainable AI, Feature engineering, Federated Learning, Few-Shot Learning, FPGAs, Generative Adversarial Networks, GPT, GPU acceleration, Gradient Descent, Graph Neural Networks, Hardware acceleration for AI, High-Performance Computing, Hyperparameter optimization, Hyperparameter tuning, Image classification, Industrial applications for AI, Inference, Interpretability of models, JAX, Keras, Language processing, Loss function, LSTM, machine learning, Machine Learning Operations, MobileNet, Model compatibility, Model compression, Model management, Model Parallelism, Model Quantization, Model Serving, Model validation, MXNet, natural language processing, neural networks, neuronal architecture, NVIDIA CUDA, Object recognition, ONNX, Open Source, OpenAI, OpenAI Gym, Optimization, PyTorch, Recurrent Neural Networks, Regularization, Reinforcement Learning, ResNet, Scalability, TensorFlow, Theano, TPUs, Training of models, Training Pipelines, Transfer Learning, Transformer models, VGG, Zero-shot learning|

What are deep learning frameworks? Deep learning frameworks are

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GitOps – Efficient infrastructure management with Git & Kubernetes

Sascha Block2024-06-06T16:38:03+02:00Tags: Alerts, Ansible, ArgoCD, Audit logging, Automated tests, Automation, Boss, CD-Pipeline, CI-Pipeline, CI/CD-Pipelines, CloudFormation, CNCF, Code Reviews, Collaboration, Compliance, Consistency, Container orchestration, Continuous Deployment, Continuous Integration, Datadog, Declarative infrastructure, Deployment, DevOps practices, Flux, Git-centered administration, Git-Repositories, Git-Workflow, GitHub Actions, GitOps methods, GitOps-Tools, HashiCorp Vault, Helmet, Infrastructure, Infrastructure as Code, Infrastructure changes, Jenkins X, Kubernetes, Kubernetes-Cluster, Kustomize, Monitoring, nfrastructure management, Prometheus, Pull requests, Puppet, Repeatability, Rollbacks, Rollouts, Scalability, Security considerations, Self-healing, Software applications, Synchronization, Terraform, Traceability, Transparency, Versioning, Weaveworks|

What is GitOps? Which 4 basic GitOps principles you should definitely know... Overview and relevance of Git-based workflows in modern software development. Now understand the basics of GitOps and master the core components of GitOps.

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Identity management and trust models in digital infrastructures

Sascha Block2024-02-01T09:43:03+01:00Categories: IT infrastructure, IT Security|Tags: APIs, Apps, Authentication, Authorization, Base wallet, BMI, BSI, Certifications, Compliance, Confidential Computing, Cyberattacks, digital identities, digital infrastructures, digital twin, Digitization, dynamic partnerships, e-prescription, eAT, eID, eIDAS Regulation, Electronic patient file, electronic prescription, Electronic residence permit, Encryption, ePA, EUdi Wallet, Federal ID card app, Federal Ministry of the Interior, Federal Office for Information Security, Federation, GDPR, General Data Protection Regulation, Identity card, Identity Management, Identity matching, Identity theft, Identity verification, Interoperability, ISO 27010, Multi-factor authentication, NFC, OAuth Framework, OpenID Connect, OpenID Federation, Penetration tests, Privacy, Quality control, Safety modules, Scalability, Security, Security checks, Single Sign-On, Smart eID Act, SmartPhone, Standards, Telematics ID, Telematics infrastructure, Tokenflow, Trust models, Trusted Execution Environment, User control, Vendor Lock-In, Wallet, Web applications|

Identity and access management and trust models in digital infrastructures are the invisible architectures that hold our digitally networked world together. In an era in which our lives increasingly take place online, complex networks interacting with digital identities, protocols and standards form the basic technical infrastructures that we use every day. And in the middle of it all: trust-based identities.

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LLAMA3 – AI model for natural language processing

Sascha Block2024-04-23T14:35:46+02:00Tags: AI model, Algorithm development, Chatbots, Efficiency, LLAMA3, machine translation, natural language processing, NLP, Open Source, Scalability, Training data, Transformer architecture|

What is LLAMA3? LLAMA3 is a state-of-the-art artificial intelligence (AI) model for natural language processing. As an AI model, it is known for its ability to understand and generate human-like speech. LLAMA3 differs from its predecessors by its open-source availability and its efficient structure, which makes it possible to achieve high-quality performance with a smaller number of parameters.

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Object-Relational Mapping (ORM) in software development!

Sascha Block2023-12-19T08:44:15+01:00Tags: .NET, ActiveRecord, Anemic Domain Model, Boilerplate code, Caching, Code reuse, CRUD operations, Data integrity, Data mapping, Data modeling, Database abstraction, Database independence, Database migrations, Database schema, Database tables, Database transactions, Design Pattern, Django ORM, Eager Loading, Entity classes, Entity Framework, Hibernate, HQL, Impedance mismatch, Java, Lazy Loading, LINQ, Migration management, N+1 problem, Node.js, Object-oriented programming, ORM, Performance, Python, Query languages, Repository pattern, Ruby on Rails, Scalability, Sequelize, Software Architecture, SQL, Transaction management, Unit-of-work pattern|

What is Object-Relational Mapping (ORM)? Object Relational Mapping (ORM)

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Refresh Token

Sascha Block2024-07-11T15:51:41+02:00Tags: Access Token, Administrative complexity, API, APIs, Apps, Authentication, blockchain-based solutions, Cloud security, Cloud services, Compliance, Cryptography, Data protection standards, Decentralized identities, DIDs, digital authentication systems, Flexibility, IT Security, Key areas, Refresh Tokens, Regulatory challenges, Scalability, Secure network protocols, Security, Security risks, Server infrastructure, Software Architecture, Technology improvements, Token, Tokens, User Experience|

What is a refresh token? Refresh tokens are essential components of modern authentication systems and an indispensable element in protocols such as OAuth 2.0 to enable the secure management of access rights to web resources. Find out more about how Refresh Token works and its life cycle...

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Reinforcement Learning

Sascha Block2025-01-26T18:51:25+01:00Tags: Actor-Critic Models, algorithmic trading, AlphaGo, autonomous vehicles, computational complexity, Deep Q-Networks, DQN, energy optimization, ethics in AI, Exploration vs. exploitation, Google Dopamine, Markov Decision Process, MDP, OpenAI Five, OpenAI Gym, overfitting, Policy-Based Methods, Portfolio Optimization, PyTorch, Q-Learning, Reinforcement Learning, Reward Design, reward maximization, RL, RLlib, Robotics, safety in AI, Scalability, Smart grids, sparse rewards, Stable baselines, Supervised Learning, TensorFlow, Unsupervised Learning|

Reinforcement learning is a branch of machine learning that trains agents to make optimal decisions by interacting with their environment. Reinforcement learning is used in autonomous vehicles, robotics, games such as AlphaGo and AlphaZero and the optimization of resources in energy systems.

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Scalability

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