fix(docs): repoint dead neptune.ai links and drop the wound-down product

docs/0. Overview/0.3. Platforms.md

@@ -18,7 +18,7 @@ Core capabilities of an MLOps platform typically include:
 - **Data and Artifact Storage**: Secure and scalable systems like [Amazon S3](https://aws.amazon.com/s3/) or [Google Cloud Storage](https://cloud.google.com/storage) for managing datasets, models, and other versioned artifacts.
 - **Compute Resources**: On-demand access to computational power, such as [Kubernetes](https://kubernetes.io/) clusters or managed services like [Databricks](https://databricks.com), for model training and inference.
 - **Workflow Orchestration**: Tools like [Apache Airflow](https://airflow.apache.org/), [Metaflow](https://metaflow.org/), or [Prefect](https://www.prefect.io/) that automate and manage the complex workflows and data pipelines involved in ML projects.
-- **Model Registries and Experiment Tracking**: Centralized platforms like [MLflow](https://mlflow.org/), [Neptune.ai](https://neptune.ai/), or [Weights & Biases](https://wandb.ai/site) for versioning models, tracking experiment parameters, and comparing results.
+- **Model Registries and Experiment Tracking**: Centralized platforms like [MLflow](https://mlflow.org/), [Weights & Biases](https://wandb.ai/site), or [Comet](https://www.comet.com/site/) for versioning models, tracking experiment parameters, and comparing results.
 
 The choice between open-source tools and managed enterprise solutions depends on trade-offs between flexibility, cost, and operational overhead. Smaller teams might combine tools like [MLflow](https://mlflow.org/) and [Airflow](https://airflow.apache.org/) for a custom, low-cost stack, while large organizations often prefer comprehensive platforms like [Databricks](https://databricks.com) or [AWS SageMaker](https://aws.amazon.com/sagemaker/) for their scalability and support.
 

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/20251207223323/https://neptune.ai/blog/mlops-tools-platforms-landscape)

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://web.archive.org/web/20251127141526/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.
 
 ## Why is experiment tracking essential in AI/ML?
 
@@ -22,7 +22,9 @@ In MLOps, the complexity and often non-deterministic nature of model development
 
 ## Which experiment tracking solution should you use?
 
-Numerous solutions are available for tracking AI/ML experiments. Major cloud platforms like [Google Cloud (Vertex AI)](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments), [Azure (Azure ML)](https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py), and [AWS (SageMaker)](https://aws.amazon.com/fr/sagemaker/experiments/) offer powerful, integrated MLOps capabilities. Specialized commercial tools like [Weights & Biases](https://wandb.ai/) and [Neptune AI](https://neptune.ai/) also provide excellent features.
+Numerous solutions are available for tracking AI/ML experiments. Major cloud platforms like [Google Cloud (Vertex AI)](https://cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments), [Azure (Azure ML)](https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment?view=azure-ml-py), and [AWS (SageMaker)](https://aws.amazon.com/fr/sagemaker/experiments/) offer powerful, integrated MLOps capabilities. Specialized commercial tools like [Weights & Biases](https://wandb.ai/), [Comet](https://www.comet.com/site/), and [ClearML](https://clear.ml/) also provide excellent features.
+
+> **A note on vendor risk**: this chapter used to recommend Neptune.ai here. OpenAI acquired it in December 2025 and it wound down its external service, taking its documentation with it. That is worth internalising before you build a workflow on a hosted tracker: an experiment store is where your history lives, so prefer one whose format you can export, and treat a vendor's continued existence as an assumption rather than a given. It is one of the reasons this course teaches MLflow, which you can run yourself.
 
 For those starting out or preferring an open-source, framework-agnostic solution, [MLflow](https://mlflow.org/) is an outstanding choice. It is versatile, robust, and integrates with a wide array of ML libraries.
 

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://web.archive.org/web/20251005125802/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.
 
 ## Why is a model registry essential?
 
@@ -24,7 +24,7 @@ A model registry provides critical governance and operational capabilities:
 The right choice depends on your existing ecosystem and requirements.
 
 - **Cloud-Based Platforms**: Major cloud providers offer tightly integrated solutions, such as [Google Vertex AI](https://cloud.google.com/vertex-ai/docs/model-registry/introduction), [AWS SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html), and [Azure ML](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-models).
-- **Third-Party Solutions**: Platforms like [Weights & Biases](https://wandb.ai/site) and [Neptune AI](https://neptune.ai/) provide comprehensive experiment tracking and model management features.
+- **Third-Party Solutions**: Platforms like [Weights & Biases](https://wandb.ai/site), [Comet](https://www.comet.com/site/), and [ClearML](https://clear.ml/) provide comprehensive experiment tracking and model management features.
 - **Open-Source**: [MLflow Model Registry](https://mlflow.org/docs/latest/model-registry.html) is a popular, framework-agnostic option that you can host yourself.
 
 To begin with MLflow, install it in your project:

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://web.archive.org/web/20251006163636/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.
 
 ## What is the difference between reproducibility and replicability?
 

mise.toml

@@ -54,7 +54,10 @@ depends = ["format:dprint"]
 
 [tasks."format:dprint"]
 description = "Format JSON, Markdown, TOML, YAML (dprint)"
-run = "dprint fmt"
+# `--allow-no-files` because the pre-commit hook passes staged paths and dprint.jsonc
+# excludes `docs/**`: without it, dprint exits 14 on a commit that touches only chapter
+# files, which is every content contribution this repository asks for.
+run = "dprint fmt --allow-no-files"
 
 # CHECK