Upload basics.txt with huggingface_hub

basics.txt

@@ -1,5 +1,5 @@
 This file is a merged representation of the entire codebase, combining all repository files into a single document.
-Generated by Repomix on: 2025-01-30T10:25:45.743Z
+Generated by Repomix on: 2025-02-06T16:56:09.144Z
 
 ================================================================
 File Summary
@@ -117,6 +117,11 @@ File: docs/book/user-guide/cloud-guide/cloud-guide.md
 description: Taking your ZenML workflow to the next level.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # ☁️ Cloud guide
 
 This section of the guide consists of easy to follow guides on how to connect the major public clouds to your ZenML deployment. We achieve this by configuring a [stack](../production-guide/understand-stacks.md).
@@ -138,6 +143,11 @@ File: docs/book/user-guide/llmops-guide/evaluation/evaluation-in-65-loc.md
 description: Learn how to implement evaluation for RAG in just 65 lines of code.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Evaluation in 65 lines of code
 
 Our RAG guide included [a short example](../rag-with-zenml/rag-85-loc.md) for how to implement a basic RAG pipeline in just 85 lines of code. In this section, we'll build on that example to show how you can evaluate the performance of your RAG pipeline in just 65 lines. For the full code, please visit the project repository [here](https://github.com/zenml-io/zenml-projects/blob/main/llm-complete-guide/most\_basic\_eval.py). The code that follows requires the functions from the earlier RAG pipeline code to work.
@@ -230,6 +240,11 @@ File: docs/book/user-guide/llmops-guide/evaluation/evaluation-in-practice.md
 description: Learn how to evaluate the performance of your RAG system in practice.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Evaluation in practice
 
 Now that we've seen individually how to evaluate the retrieval and generation components of our pipeline, it's worth taking a step back to think through how all of this works in practice.
@@ -279,6 +294,11 @@ File: docs/book/user-guide/llmops-guide/evaluation/generation.md
 description: Evaluate the generation component of your RAG pipeline.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Generation evaluation
 
 Now that we have a sense of how to evaluate the retrieval component of our RAG
@@ -677,6 +697,11 @@ File: docs/book/user-guide/llmops-guide/evaluation/README.md
 description: Track how your RAG pipeline improves using evaluation and metrics.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Evaluation and metrics
 
 In this section, we'll explore how to evaluate the performance of your RAG pipeline using metrics and visualizations. Evaluating your RAG pipeline is crucial to understanding how well it performs and identifying areas for improvement. With language models in particular, it's hard to evaluate their performance using traditional metrics like accuracy, precision, and recall. This is because language models generate text, which is inherently subjective and difficult to evaluate quantitatively.
@@ -715,6 +740,11 @@ File: docs/book/user-guide/llmops-guide/evaluation/retrieval.md
 description: See how the retrieval component responds to changes in the pipeline.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Retrieval evaluation
 
 The retrieval component of our RAG pipeline is responsible for finding relevant
@@ -1064,6 +1094,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-embeddings/evaluating-finetun
 description: Evaluate finetuned embeddings and compare to original base embeddings.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 Now that we've finetuned our embeddings, we can evaluate them and compare to the
 base embeddings. We have all the data saved and versioned already, and we will
 reuse the same MatryoshkaLoss function for evaluation.
@@ -1204,6 +1239,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-embeddings/finetuning-embeddi
 description: Finetune embeddings with Sentence Transformers.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 We now have a dataset that we can use to finetune our embeddings. You can
 [inspect the positive and negative examples](https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0_distilabel) on the Hugging Face [datasets page](https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0_distilabel) since
 our previous pipeline pushed the data there.
@@ -1308,6 +1348,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-embeddings/finetuning-embeddi
 description: Finetune embeddings on custom synthetic data to improve retrieval performance.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 We previously learned [how to use RAG with ZenML](../rag-with-zenml/README.md) to
 build a production-ready RAG pipeline. In this section, we will explore how to
 optimize and maintain your embedding models through synthetic data generation and
@@ -1355,6 +1400,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-embeddings/synthetic-data-gen
 description: Generate synthetic data with distilabel to finetune embeddings.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 We already have [a dataset of technical documentation](https://huggingface.co/datasets/zenml/rag_qa_embedding_questions_0_60_0) that was generated
 previously while we were working on the RAG pipeline. We'll use this dataset
 to generate synthetic data with `distilabel`. You can inspect the data directly
@@ -1900,6 +1950,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-llms/finetuning-100-loc.md
 description: Learn how to implement an LLM fine-tuning pipeline in just 100 lines of code.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Quick Start: Fine-tuning an LLM
 
 There's a lot to understand about LLM fine-tuning - from choosing the right base model to preparing your dataset and selecting training parameters. But let's start with a concrete implementation to see how it works in practice. The following 100 lines of code demonstrate:
@@ -2118,6 +2173,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-llms/finetuning-llms.md
 description: Finetune LLMs for specific tasks or to improve performance and cost.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 So far in our LLMOps journey we've learned [how to use RAG with
 ZenML](../rag-with-zenml/README.md), how to [evaluate our RAG
 systems](../evaluation/README.md), how to [use reranking to improve retrieval](../reranking/README.md), and how to
@@ -2167,6 +2227,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-llms/finetuning-with-accelera
 description: "Finetuning an LLM with Accelerate and PEFT"
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Finetuning an LLM with Accelerate and PEFT
 
 We're finally ready to get our hands on the code and see how it works. In this
@@ -2420,6 +2485,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-llms/starter-choices-for-fine
 description: Get started with finetuning LLMs by picking a use case and data.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Starter choices for finetuning LLMs
 
 Finetuning large language models can be a powerful way to tailor their
@@ -2590,6 +2660,11 @@ File: docs/book/user-guide/llmops-guide/finetuning-llms/why-and-when-to-finetune
 description: Deciding when is the right time to finetune LLMs.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Why and when to finetune LLMs
 
 This guide is intended to be a practical overview that gets you started with
@@ -2678,6 +2753,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/basic-rag-inference-pipel
 description: Use your RAG components to generate responses to prompts.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Simple RAG Inference
 
 Now that we have our index store, we can use it to make queries based on the
@@ -2842,6 +2922,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/data-ingestion.md
 description: Understand how to ingest and preprocess data for RAG pipelines with ZenML.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 The first step in setting up a RAG pipeline is to ingest the data that will be
 used to train and evaluate the retriever and generator models. This data can
 include a large corpus of documents, as well as any relevant metadata or
@@ -3018,6 +3103,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/embeddings-generation.md
 description: Generate embeddings to improve retrieval performance.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Generating Embeddings for Retrieval
 
 In this section, we'll explore how to generate embeddings for your data to
@@ -3233,6 +3323,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/rag-85-loc.md
 description: Learn how to implement a RAG pipeline in just 85 lines of code.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 There's a lot of theory and context to think about when it comes to RAG, but
 let's start with a quick implementation in code to motivate what follows. The
 following 85 lines do the following:
@@ -3374,6 +3469,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/README.md
 description: RAG is a sensible way to get started with LLMs.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # RAG Pipelines with ZenML
 
 Retrieval-Augmented Generation (RAG) is a powerful technique that combines the
@@ -3414,6 +3514,11 @@ File: docs/book/user-guide/llmops-guide/rag-with-zenml/storing-embeddings-in-a-v
 description: Store embeddings in a vector database for efficient retrieval.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Storing embeddings in a vector database
 
 The process of generating the embeddings doesn't take too long, especially if the machine on which the step is running has a GPU, but it's still not something we want to do every time we need to retrieve a document. Instead, we can store the embeddings in a vector database, which allows us to quickly retrieve the most relevant chunks based on their similarity to the query.
@@ -3550,6 +3655,11 @@ description: >-
   benefits.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Understanding Retrieval-Augmented Generation (RAG)
 
 LLMs are powerful but not without their limitations. They are prone to generating incorrect responses, especially when it's unclear what the input prompt is asking for. They are also limited in the amount of text they can understand and generate. While some LLMs can handle more than 1 million tokens of input, most open-source models can handle far less. Your use case also might not require all the complexity and cost associated with running a large LLM.
@@ -3603,6 +3713,11 @@ File: docs/book/user-guide/llmops-guide/reranking/evaluating-reranking-performan
 description: Evaluate the performance of your reranking model.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Evaluating reranking performance
 
 We've already set up an evaluation pipeline, so adding reranking evaluation is relatively straightforward. In this section, we'll explore how to evaluate the performance of your reranking model using ZenML.
@@ -3830,6 +3945,11 @@ File: docs/book/user-guide/llmops-guide/reranking/implementing-reranking.md
 description: Learn how to implement reranking in ZenML.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Implementing Reranking in ZenML
 
 We already have a working RAG pipeline, so inserting a reranker into the
@@ -3988,6 +4108,11 @@ File: docs/book/user-guide/llmops-guide/reranking/README.md
 description: Add reranking to your RAG inference for better retrieval performance.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 Rerankers are a crucial component of retrieval systems that use LLMs. They help
 improve the quality of the retrieved documents by reordering them based on
 additional features or scores. In this section, we'll explore how to add a
@@ -4017,6 +4142,11 @@ File: docs/book/user-guide/llmops-guide/reranking/reranking.md
 description: Add reranking to your RAG inference for better retrieval performance.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 Rerankers are a crucial component of retrieval systems that use LLMs. They help
 improve the quality of the retrieved documents by reordering them based on
 additional features or scores. In this section, we'll explore how to add a
@@ -4046,6 +4176,11 @@ File: docs/book/user-guide/llmops-guide/reranking/understanding-reranking.md
 description: Understand how reranking works.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 ## What is reranking?
 
 Reranking is the process of refining the initial ranking of documents retrieved
@@ -4176,6 +4311,11 @@ description: >-
   Delivery
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Set up CI/CD
 
 Until now, we have been executing ZenML pipelines locally. While this is a good mode of operating pipelines, in
@@ -4327,6 +4467,11 @@ File: docs/book/user-guide/production-guide/cloud-orchestration.md
 description: Orchestrate using cloud resources.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Orchestrate on the cloud
 
 Until now, we've only run pipelines locally. The next step is to get free from our local machines and transition our pipelines to execute on the cloud. This will enable you to run your MLOps pipelines in a cloud environment, leveraging the scalability and robustness that cloud platforms offer.
@@ -4515,6 +4660,11 @@ File: docs/book/user-guide/production-guide/configure-pipeline.md
 description: Add more resources to your pipeline configuration.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Configure your pipeline to add compute
 
 Now that we have our pipeline up and running in the cloud, you might be wondering how ZenML figured out what sort of dependencies to install in the Docker image that we just ran on the VM. The answer lies in the [runner script we executed (i.e. run.py)](https://github.com/zenml-io/zenml/blob/main/examples/quickstart/run.py#L215), in particular, these lines:
@@ -4685,6 +4835,11 @@ description: >-
   MLOps projects.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Configure a code repository
 
 Throughout the lifecycle of a MLOps pipeline, it can get quite tiresome to always wait for a Docker build every time after running a pipeline (even if the local Docker cache is used). However, there is a way to just have one pipeline build and keep reusing it until a change to the pipeline environment is made: by connecting a code repository.
@@ -4793,6 +4948,11 @@ File: docs/book/user-guide/production-guide/deploying-zenml.md
 description: Deploying ZenML is the first step to production.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Deploying ZenML
 
 When you first get started with ZenML, it is based on the following architecture on your machine:
@@ -4867,6 +5027,11 @@ File: docs/book/user-guide/production-guide/end-to-end.md
 description: Put your new knowledge in action with an end-to-end project
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # An end-to-end project
 
 That was awesome! We learned so many advanced MLOps production concepts:
@@ -4965,6 +5130,11 @@ File: docs/book/user-guide/production-guide/remote-storage.md
 description: Transitioning to remote artifact storage.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Connecting remote storage
 
 In the previous chapters, we've been working with artifacts stored locally on our machines. This setup is fine for individual experiments, but as we move towards a collaborative and production-ready environment, we need a solution that is more robust, shareable, and scalable. Enter remote storage!
@@ -5187,6 +5357,11 @@ File: docs/book/user-guide/production-guide/understand-stacks.md
 description: Learning how to switch the infrastructure backend of your code.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Understanding stacks
 
 Now that we have ZenML deployed, we can take the next steps in making sure that our machine learning workflows are production-ready. As you were running [your first pipelines](../starter-guide/create-an-ml-pipeline.md), you might have already noticed the term `stack` in the logs and on the dashboard.
@@ -5415,6 +5590,11 @@ File: docs/book/user-guide/starter-guide/cache-previous-executions.md
 description: Iterating quickly with ZenML through caching.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Cache previous executions
 
 Developing machine learning pipelines is iterative in nature. ZenML speeds up development in this work with step caching.
@@ -5599,6 +5779,11 @@ File: docs/book/user-guide/starter-guide/create-an-ml-pipeline.md
 description: Start with the basics of steps and pipelines.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Create an ML pipeline
 
 In the quest for production-ready ML models, workflows can quickly become complex. Decoupling and standardizing stages such as data ingestion, preprocessing, and model evaluation allows for more manageable, reusable, and scalable processes. ZenML pipelines facilitate this by enabling each stage—represented as **Steps**—to be modularly developed and then integrated smoothly into an end-to-end **Pipeline**.
@@ -5939,6 +6124,11 @@ File: docs/book/user-guide/starter-guide/manage-artifacts.md
 description: Understand and adjust how ZenML versions your data.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Manage artifacts
 
 Data sits at the heart of every machine learning workflow. Managing and versioning this data correctly is essential for reproducibility and traceability within your ML pipelines. ZenML takes a proactive approach to data versioning, ensuring that every artifact—be it data, models, or evaluations—is automatically tracked and versioned upon pipeline execution.
@@ -6575,6 +6765,11 @@ File: docs/book/user-guide/starter-guide/starter-project.md
 description: Put your new knowledge into action with a simple starter project
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # A starter project
 
 By now, you have understood some of the basic pillars of a MLOps system:
@@ -6645,6 +6840,11 @@ File: docs/book/user-guide/starter-guide/track-ml-models.md
 description: Creating a full picture of a ML model using the Model Control Plane
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Track ML models
 
 ![Walkthrough of ZenML Model Control Plane (Dashboard available only on ZenML Pro)](../../.gitbook/assets/mcp_walkthrough.gif)
@@ -6910,7 +7110,7 @@ ZenML Model and versions are some of the most powerful features in ZenML. To und
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>
 This file is a merged representation of the entire codebase, combining all repository files into a single document.
-Generated by Repomix on: 2025-01-30T10:25:46.732Z
+Generated by Repomix on: 2025-02-06T16:56:10.199Z
 
 ================================================================
 File Summary
@@ -6990,6 +7190,11 @@ File: docs/book/getting-started/deploying-zenml/custom-secret-stores.md
 description: Learning how to develop a custom secret store.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Custom secret stores
 
 The secrets store acts as the one-stop shop for all the secrets to which your pipeline or stack components might need access. It is responsible for storing, updating and deleting _only the secrets values_ for ZenML secrets, while the ZenML secret metadata is stored in the SQL database. The secrets store interface implemented by all available secrets store back-ends is defined in the `zenml.zen_stores.secrets_stores.secrets_store_interface` core module and looks more or less like this:
@@ -7096,6 +7301,11 @@ File: docs/book/getting-started/deploying-zenml/deploy-using-huggingface-spaces.
 description: Deploying ZenML to Huggingface Spaces.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Deploy using HuggingFace Spaces
 
 A quick way to deploy ZenML and get started is to use [HuggingFace Spaces](https://huggingface.co/spaces). HuggingFace Spaces is a platform for hosting and sharing ML projects and workflows, and it also works to deploy ZenML. You can be up and running in minutes (for free) with a hosted ZenML server, so it's a good option if you want to try out ZenML without any infrastructure overhead.
@@ -7175,6 +7385,11 @@ File: docs/book/getting-started/deploying-zenml/deploy-with-custom-image.md
 description: Deploying ZenML with custom Docker images.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Deploy with custom images
 
 In most cases, deploying ZenML with the default `zenmlhub/zenml-server` Docker image should work just fine. However, there are some scenarios when you might need to deploy ZenML with a custom Docker image:
@@ -7373,6 +7588,11 @@ File: docs/book/getting-started/deploying-zenml/secret-management.md
 description: Configuring the secrets store.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Secret store configuration and management
 
 ## Centralized secrets store
@@ -7508,6 +7728,11 @@ description: >
   Learn how to use the ZenML Pro API.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Using the ZenML Pro API
 
 ZenML Pro offers a powerful API that allows you to interact with your ZenML resources. Whether you're using the [SaaS version](https://cloud.zenml.io) or a self-hosted ZenML Pro instance, you can leverage this API to manage tenants, organizations, users, roles, and more.
@@ -7629,6 +7854,11 @@ description: >
   Learn about the different roles and permissions you can assign to your team members in ZenML Pro.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # ZenML Pro: Roles and Permissions
 
 ZenML Pro offers a robust role-based access control (RBAC) system to manage permissions across your organization and tenants. This guide will help you understand the different roles available, how to assign them, and how to create custom roles tailored to your team's needs.
@@ -7771,6 +8001,11 @@ description: >
   Learn about Teams in ZenML Pro and how they can be used to manage groups of users across your organization and tenants.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Organize users in Teams
 
 ZenML Pro introduces the concept of Teams to help you manage groups of users efficiently. A team is a collection of users that acts as a single entity within your organization and tenants. This guide will help you understand how teams work, how to create and manage them, and how to use them effectively in your MLOps workflows.
@@ -7850,6 +8085,11 @@ description: >
   Learn how to use tenants in ZenML Pro.
 ---
 
+{% hint style="warning" %}
+This is an older version of the ZenML documentation. To read and view the latest version please [visit this up-to-date URL](https://docs.zenml.io).
+{% endhint %}
+
+
 # Tenants
 
 Tenants are individual, isolated deployments of the ZenML server. Each tenant has its own set of users, roles, and resources. Essentially, everything you do in ZenML Pro revolves around a tenant: all of your pipelines, stacks, runs, connectors and so on are scoped to a tenant.