diff --git a/docs/1. Initializing/1.0. System.md b/docs/1. Initializing/1.0. System.md index 11587a0..a75feb5 100644 --- a/docs/1. Initializing/1.0. System.md +++ b/docs/1. Initializing/1.0. System.md @@ -49,4 +49,4 @@ When using cloud services, be mindful of resource management and usage quotas, p - [GitHub Codespaces](https://github.com/features/codespaces) - [Google Cloud Workstations](https://cloud.google.com/workstations) -- [MLOps Landscape in 2024: Top Tools and Platforms](https://neptune.ai/blog/mlops-tools-platforms-landscape) +- [MLOps Landscape in 2024: Top Tools and Platforms](https://web.archive.org/web/20251006160541/https://neptune.ai/blog/mlops-tools-platforms-landscape) diff --git a/docs/5. Refining/5.5. AI-ML Experiments.md b/docs/5. Refining/5.5. AI-ML Experiments.md index 3a0a9ba..881349d 100644 --- a/docs/5. Refining/5.5. AI-ML Experiments.md +++ b/docs/5. Refining/5.5. AI-ML Experiments.md @@ -8,7 +8,7 @@ description: Master AI/ML experiment tracking with MLflow. Learn how to effectiv ## What is an AI/ML experiment? -[An AI/ML experiment](https://neptune.ai/blog/ml-experiment-tracking) is a systematic and iterative process for building robust machine learning models. It involves testing different algorithms, tuning hyperparameters, and using various datasets to discover the optimal configuration for a specific predictive task. Each experiment is a structured trial designed to measure the impact of changes on model performance, such as accuracy, efficiency, and reliability. +[An AI/ML experiment](https://www.bestaiweb.ai/glossary/experiment-tracking/) is a systematic and iterative process for building robust machine learning models. It involves testing different algorithms, tuning hyperparameters, and using various datasets to discover the optimal configuration for a specific predictive task. Each experiment is a structured trial designed to measure the impact of changes on model performance, such as accuracy, efficiency, and reliability. ## Why is experiment tracking essential in AI/ML? diff --git a/docs/5. Refining/5.6. Model Registries.md b/docs/5. Refining/5.6. Model Registries.md index fad910a..f713e77 100644 --- a/docs/5. Refining/5.6. Model Registries.md +++ b/docs/5. Refining/5.6. Model Registries.md @@ -8,7 +8,7 @@ description: Explore the use of model registries with MLflow for managing model ## What is a model registry? -[A model registry](https://neptune.ai/blog/ml-model-registry) is a centralized repository designed to manage the lifecycle of machine learning models. It acts as a version control system for models, tracking their journey from training and experimentation to staging and production deployment. This makes it an indispensable tool for collaborative and scalable MLOps. +[A model registry](https://www.bestaiweb.ai/glossary/model-registry/) is a centralized repository designed to manage the lifecycle of machine learning models. It acts as a version control system for models, tracking their journey from training and experimentation to staging and production deployment. This makes it an indispensable tool for collaborative and scalable MLOps. ## Why is a model registry essential? diff --git a/docs/7. Observability/7.0. Reproducibility.md b/docs/7. Observability/7.0. Reproducibility.md index 019c8fe..ed19000 100644 --- a/docs/7. Observability/7.0. Reproducibility.md +++ b/docs/7. Observability/7.0. Reproducibility.md @@ -8,7 +8,7 @@ description: Explore the crucial concept of reproducibility in MLOps and learn h ## What is reproducibility in MLOps? -[Reproducibility in MLOps](https://neptune.ai/blog/how-to-solve-reproducibility-in-ml) is the ability to re-create the exact same results of a machine learning experiment or model, given the same code, data, and environment. This is a fundamental requirement for validating findings, debugging models, and ensuring consistent behavior over time. Achieving reproducibility builds trust and transparency, enabling independent verification and accelerating development by providing a stable foundation. +[Reproducibility in MLOps](https://www.bestaiweb.ai/glossary/reproducibility/) is the ability to re-create the exact same results of a machine learning experiment or model, given the same code, data, and environment. This is a fundamental requirement for validating findings, debugging models, and ensuring consistent behavior over time. Achieving reproducibility builds trust and transparency, enabling independent verification and accelerating development by providing a stable foundation. ## What is the difference between reproducibility and replicability?