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Wednesday 01 July 2026 17:37:20 GMT

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Moctar Mohamed :

super maman

2026-07-02 09:56:35

14

Coulibaly🇨🇮🇰🇵🇨🇮 :

o choooo

2026-07-02 17:47:50

0

Awa_Geuye :

2026-07-01 22:48:05

5

Seydou Diarra :

2026-07-02 15:23:12

1

ALASTOR :

je vois son aura a travers le phone

2026-07-02 18:49:51

0

bby :

eeeeeeeh😂😂😂

2026-07-01 17:47:40

4

King-olivier1th :

IA est trop fort qu’on ne pige plus le vrai du faux

2026-07-02 18:13:52

0

بلال الغامض 🖤⏳ 🎭 :

Prime mum

2026-07-02 17:24:36

0

Joël woollard :

2026-07-02 06:48:26

1

King Derrick☠️☠️💯💯 :

2026-07-02 02:44:55

0

cendresse11🇲🇬 :

2026-07-02 13:26:42

0

ismo :

2026-07-02 19:03:38

0

Chief Umaru D Billionaire :

as the looking of things this woman is going through a lot but just can't prove it

2026-07-01 19:00:12

2

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تقدر تعترف بالحُب..؟♥️@ناصر الوبير #ناصر_الوبير #شعر #اكسبلور #explore

تقدر تعترف بالحُب..؟♥️@ناصر الوبير #ناصر_الوبير #شعر #اكسبلور #explore

Cagrilintide is a novel, long-acting peptide designed to target multiple receptors involved in appetite regulation and energy balance. Unlike traditional weight loss medications that often target a single pathway, cagrilintide acts as a dual agonist, primarily affecting both the amylin receptor and, in some cases, the calcitonin receptor. By engaging these pathways simultaneously, cagrilintide works to reduce food intake, increase satiety, and promote weight loss. #healthiswealth #956valley #beauty #therapy

Cagrilintide is a novel, long-acting peptide designed to target multiple receptors involved in appetite regulation and energy balance. Unlike traditional weight loss medications that often target a single pathway, cagrilintide acts as a dual agonist, primarily affecting both the amylin receptor and, in some cases, the calcitonin receptor. By engaging these pathways simultaneously, cagrilintide works to reduce food intake, increase satiety, and promote weight loss. #healthiswealth #956valley #beauty #therapy

#musicreactions #music #reaction @Watsebha

#musicreactions #music #reaction @Watsebha

Little run at the end though 😭🤣

Little run at the end though 😭🤣

Machine Learning Operations (MLOps) is a field that combines software engineering, machine learning, and cloud infrastructure to develop, deploy, and monitor ml systems in production. Some common tools, areas of study, and practices used in MLOps are: - Data and model versioning to track datasets, experiments and configurations over time. - Automated training and evaluation pipelines to ensure reproducible and scalable model development. - Continuous Integration and Continuous Deployment (CI/CD) workflows to test, validate, and release models safely. Infrastructure management to scale manage ml systems through: - Cloud computing - Containerization - Orchestration - Infrastructure as Code (IaC) Model serving and deployment using methods such as: - Batch/Real-time inference - Edge deployment - Distributed serving - A/B testing and shadow deployment Observability/Explainability and monitoring through: - Performance monitoring - Data quality checks - Drift detection - Logging and tracing Feature management systems are used to create, store, and serve consistent features across training and inference environments. Resource optimization and scheduling improves efficiency, reliability, and scalability. Together, these methods form a framework for actually integrating ML models into maintainable production systems. Learn AI concepts, Visually. Join 8000+ Others in our Visually Explained Deep Learning Newsletter. Get your weekly AI breakdown (link in bio). #machinelearning #computerscience #github #devops #gitops

Machine Learning Operations (MLOps) is a field that combines software engineering, machine learning, and cloud infrastructure to develop, deploy, and monitor ml systems in production. Some common tools, areas of study, and practices used in MLOps are: - Data and model versioning to track datasets, experiments and configurations over time. - Automated training and evaluation pipelines to ensure reproducible and scalable model development. - Continuous Integration and Continuous Deployment (CI/CD) workflows to test, validate, and release models safely. Infrastructure management to scale manage ml systems through: - Cloud computing - Containerization - Orchestration - Infrastructure as Code (IaC) Model serving and deployment using methods such as: - Batch/Real-time inference - Edge deployment - Distributed serving - A/B testing and shadow deployment Observability/Explainability and monitoring through: - Performance monitoring - Data quality checks - Drift detection - Logging and tracing Feature management systems are used to create, store, and serve consistent features across training and inference environments. Resource optimization and scheduling improves efficiency, reliability, and scalability. Together, these methods form a framework for actually integrating ML models into maintainable production systems. Learn AI concepts, Visually. Join 8000+ Others in our Visually Explained Deep Learning Newsletter. Get your weekly AI breakdown (link in bio). #machinelearning #computerscience #github #devops #gitops

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