Algorithm Administration - Revision history

BPeat: /* Automated Learning */

2026-02-11T12:52:36Z

‎Automated Learning

BPeat at 14:41, 28 May 2025

2025-05-28T14:41:27Z

BPeat at 20:54, 28 April 2024

2024-04-28T20:54:53Z

BPeat at 01:45, 27 April 2024

2024-04-27T01:45:20Z

BPeat: /* AIOps/MLOps */

2024-04-27T01:15:22Z

‎AIOps/MLOps

BPeat at 01:14, 20 March 2024

2024-03-20T01:14:01Z

BPeat: /* Automated Learning */

2024-03-08T10:34:51Z

‎Automated Learning

BPeat at 02:56, 6 March 2024

2024-03-06T02:56:13Z

BPeat: /* Large Language Model Operations (LLMOps) */

2023-10-10T10:48:01Z

‎Large Language Model Operations (LLMOps)

BPeat: /* LLMOps */

2023-10-05T10:57:48Z

‎LLMOps

@@ Line 307: / Line 307: @@
 * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]]
 * [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Communication Agents]] ... [[Smart Cities]] ...  [[Digital Twin]] ... [[Internet of Things (IoT)]]
 * [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
+|keywords=ChatGPT, artificial, intelligence, machine, learning, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 <!-- Google tag (gtag.js) -->
@@ Line 308: / Line 308: @@
 * [[Immersive Reality]] ... [[Metaverse]] ... [[Omniverse]] ... [[Transhumanism]] ... [[Religion]]
 * [[Telecommunications]] ... [[Computer Networks]] ... [[Telecommunications#5G|5G]] ... [[Satellite#Satellite Communications|Satellite Communications]] ... [[Quantum Communications]] ... [[Agents#Communication | Communication Agents]] ... [[Smart Cities]] ...  [[Digital Twin]] ... [[Internet of Things (IoT)]]
-* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[GPT-4]] ... [[GPT-5]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
+* [[Large Language Model (LLM)]] ... [[Large Language Model (LLM)#Multimodal|Multimodal]] ... [[Foundation Models (FM)]] ... [[Generative Pre-trained Transformer (GPT)|Generative Pre-trained]] ... [[Transformer]] ... [[Attention]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[AdaNet]]

@@ Line 734: / Line 734: @@
 <youtube>uClvvlfJxqo</youtube>
 <b>[[Python]] Lunch Break presents MLOps
-</b><br>Deploy your [[Assistants#Chatbot | Chatbot]] using CI/CD  by William Arias  Leverage DevOps practices in your next [[Assistants#Chatbot | Chatbot]] deployments, create pipelines for training and testing your [[Assistants#Chatbot | Chatbot]] before automating the deployment to staging or production. NLP developers can focus on the [[Assistants#Chatbot | Chatbot]] [[development]] and worry less about infrastructure and environments configuration  Bio:  Born and raised in Colombia, studied Electronic Engineering and specialized in Digital Electronics, former exchange student in Czech Technical University in the faculty of Data Science. I started my career working in Intel as Field Engineer, then moved to Oracle in Bogota. After 5 years in Oracle as Solution Architect I decided to get deeper in my knowledge of Machine Learning and Databases, relocated to Prague in Czech Republic, where I studied Data Science for a while from the academia perspective, later jumped to Solution Architect in CA technologies, later moved on to Developer of Automations and NLP in Adecco. Currently I work at Gitlab as Technical Marketing Engineer, teaching and evangelizing developers about DevOps
+</b><br>Deploy your [[Assistants#Chatbot | Chatbot]] using CI/CD  by William Arias  Leverage DevOps practices in your next [[Assistants#Chatbot | Chatbot]] deployments, create pipelines for training and testing your [[Assistants#Chatbot | Chatbot]] before automating the deployment to staging or production. NLP developers can focus on the [[Assistants#Chatbot | Chatbot]] [[development]] and worry less about infrastructure and environments configuration  Bio:  Born and raised in Colombia, studied Electronic Engineering and specialized in Digital Electronics, former exchange student in Czech Technical University in the faculty of Data Science. I started my career working in Intel as Field Engineer, then moved to Oracle in Bogota. After 5 years in Oracle as Solution Architect I decided to get deeper in my knowledge of Machine Learning and Databases, relocated to Prague in Czech Republic, where I studied Data Science for a while from the academia [[perspective]], later jumped to Solution Architect in CA technologies, later moved on to Developer of Automations and NLP in Adecco. Currently I work at Gitlab as Technical Marketing Engineer, teaching and evangelizing developers about DevOps
 |}
 |}<!-- B -->

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 * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]]
 * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]]

@@ Line 422: / Line 422: @@
 [https://www.google.com/search?q=~AIOps+~MLOps+~devops+~secdevops+~devsecops+pipeline+toolchain+CI+CD+machine+learning+ai ...Google search]
-* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
+* [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[MLflow]]
 * [[ChatGPT#DevSecOps| ChatGPT integration with DevSecOps]]

@@ Line 310: / Line 310: @@
 * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless, Generators, Drag n' Drop]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]]
 * [[AdaNet]]
 * [[Generative Pre-trained Transformer (GPT)#Generative Pre-trained Transformer 5 (GPT-5) | Generative Pre-trained Transformer 5 (GPT-5)]]
 * [https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/  AI Software Learns to Make AI Software]

@@ Line 738: / Line 738: @@
 == <span id="Large Language Model Operations (LLMOps)"></span>Large Language Model Operations (LLMOps) ==
 * [https://www.databricks.com/glossary/llmops What Is LLMOps? | Databricks]
 Large Language Model Operations (LLMOps) is a set of practices and tools for the operational management of large language models (LLMs) in production environments. LLMOps encompasses the entire lifecycle of LLMs, from development and deployment to monitoring and maintenance. LLMOps is important because LLMs are complex and resource-intensive models that require careful management to ensure that they are performing as expected and meeting the needs of their users. LLMOps tools and practices can help to streamline and automate the LLM lifecycle, making it easier to deploy and manage LLMs in production.