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add deploy & dagger to README #593

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15 changes: 8 additions & 7 deletions README.md
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### What is KitOps?

KitOps is a packaging, versioning, and sharing system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using, and can be stored in your enterprise container registry.
KitOps is a packaging, versioning, and sharing system for AI/ML projects that uses open standards so it works with the AI/ML, development, and DevOps tools you are already using, and can be stored in your enterprise container registry. It's AI/ML platform engineering teams' preferred solution for securely packaging and versioning assets.

KitOps creates a ModelKit for your AI/ML project which includes everything you need to reproduce it locally or deploy it into production. You can even **selectively unpack a ModelKit** so different team members can save time and storage space by only grabbing what they need for a task. Because ModelKits are immutable, signable, and live in your existing container registry they're easy for organizations to track, control, and audit.

ModelKits simplify the handoffs between data scientists, application developers, and SREs working with LLMs and other AI/ML models. Teams and enterprises use KitOps as a secure storage throughout the AI/ML project lifecycle.
ModelKits [simplify the handoffs between data scientists, application developers, and SREs](https://www.youtube.com/watch?v=j2qjHf2HzSQ) working with LLMs and other AI/ML models. Teams and enterprises use KitOps as a secure storage throughout the AI/ML project lifecycle.

Use KitOps to speed up and de-risk all types of AI/ML projects:
* Predictive models
Expand All @@ -36,9 +36,10 @@ For our friends in the EU - ModelKits are the perfect way to create a library of

### 😍 What's New? ✨

* 🚢 First Look: Create a **[runnable container from a ModelKit](https://tinyurl.com/5b76p5u3)** with one command!
* 🚢 Create a **[runnable container from a ModelKit](https://tinyurl.com/5b76p5u3)** with one command! Read [KitOps deploy docs](https://kitops.ml/docs/deploy.html) for details.
* 🥂 Get the most out of KitOps' ModelKits by using them with the **[Jozu Hub](https://jozu.ml/)** repository. Or, continue using ModelKits with your existing OCI registry (even on-premises and air-gapped).
* ⛑️ [KitOps works great with Red Hat](https://developers.redhat.com/articles/2024/09/16/enhance-llms-instructlab-kitops) InstructLab and Quay.io products
* 🛠️ Use KitOps with Dagger pipelines using our modules from the [Daggerverse](https://daggerverse.dev/mod/github.com/jozu-ai/daggerverse/kit).
* ⛑️ [KitOps works great with Red Hat](https://developers.redhat.com/articles/2024/09/16/enhance-llms-instructlab-kitops) InstructLab and Quay.io products.


### Features
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* 🏭 **[Versioning](https://kitops.ml/docs/cli/cli-reference.html#kit-tag):** Each ModelKit is tagged so everyone knows which dataset and model work together.
* 🔒 **[Tamper-proofing](https://kitops.ml/docs/modelkit/spec.html):** Each ModelKit package includes an SHA digest for itself, and every artifact it holds.
* 🤩 **[Selective-unpacking](https://kitops.ml/docs/cli/cli-reference.html#kit-unpack):** Unpack only what you need from a ModelKit with the `kit unpack --filter` command - just the model, just the dataset and code, or any other combination.
* 🤖 **[Automation](https://github.com/marketplace/actions/setup-kit-cli):** Pack or unpack a ModelKit locally or as part of your CI/CD workflow for testing, integration, or deployment.
* 🤖 **[Automation](https://github.com/marketplace/actions/setup-kit-cli):** Pack or unpack a ModelKit locally or as part of your CI/CD workflow for testing, integration, or deployment (e.g. [GitHub Actions](https://github.com/marketplace/actions/setup-kit-cli) or [Dagger](https://daggerverse.dev/mod/github.com/jozu-ai/daggerverse/kit).
* 🐳 **[Deploy containers](https://kitops.ml/docs/deploy.html):** Generate a basic or custom docker container from any ModelKit.
* 🚢 **[Kubernetes-ready](https://kitops.ml/docs/deploy.html):** Generate a Kubernetes / KServe deployment config from any ModelKit.
* 🪛 **[LLM fine-tuning](https://dev.to/kitops/fine-tune-your-first-large-language-model-llm-with-lora-llamacpp-and-kitops-in-5-easy-steps-1g7f):** Use KitOps to fine-tune a large language model using LoRA.
* 🎯 **[RAG pipelines](https://www.codeproject.com/Articles/5384392/A-Step-by-Step-Guide-to-Building-and-Distributing):** Create a RAG pipeline for tailoring an LLM with KitOps.
* 📝 **[Artifact signing](https://kitops.ml/docs/next-steps.html):** ModelKits and their assets can be signed so you can be confident of their provenance.
Expand All @@ -56,8 +59,6 @@ For our friends in the EU - ModelKits are the perfect way to create a library of
* 🩰 **[Flexible](https://kitops.ml/docs/kitfile/format.html#model):** Reference base models using `model parts`, or store key-value pairs (or any YAML-compatible JSON data) in your Kitfile - use it to keep features, hyperparameters, links to MLOps tool experiments, or validation output.
* 🏃‍♂️‍➡️ **[Run locally](./docs/src/docs/dev-mode.md):** Kit's Dev Mode lets you run an LLM locally, configure it, and prompt/chat with it instantly.
* 🤗 **Universal:** ModelKits can be used with any AI, ML, or LLM project - even multi-modal models.
* 🐳 **Deploy containers:** Generate a Docker container as part of your `kit unpack` (coming soon).
* 🚢 **Kubernetes-ready:** Generate a Kubernetes / KServe deployment config as part of your `kit unpack` (coming soon).

### See KitOps in Action

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