> ## Documentation Index > Fetch the complete documentation index at: https://docs.acusight.io/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart > Train and deploy your first model with AcuSight — the whole loop, end to end. This quickstart walks the entire [AcuSight MLOps loop](/what-is-acusight#the-acusight-mlops-loop) — from images on the line to a model running live on a device — right in your browser. There's nothing to install. Prefer to watch first? [What is AcuSight](/what-is-acusight#watch-the-mlops-loop-in-action) has a short video of this exact flow. And if a term doesn't ring a bell — batches, dataset versions, champion — the [Glossary](/reference/glossary) defines them all in one place. ## Before you start You'll need: * **An AcuSight account.** Sign in at [acusight.io](https://acusight.io). (If your organization runs AcuSight on-prem, use its URL instead.) * **Some images to work with** — either an edge device that's streaming images, or a folder of images on your computer to upload. ## Build your first model Go to [acusight.io](https://acusight.io) and sign in. You'll land on the **Home** dashboard, which shows your devices and their live status. Open **Projects** and create one (say, *Cable glands*). Then add images: * **From a device** — images your devices capture arrive automatically as [**batches**](/guides/data/projects-batches). Open a batch and choose **Move Batch to Project**. * **Upload** — use **Upload Data** to add images straight from your computer. Open **Annotate**. Your batch starts under **Unassigned** — open it to begin [labeling](/guides/data/annotation). Draw a box around each object and assign it a **class** (for example, `pass` or `fail`); you manage classes and their colors on **Classes & Tags**. Mark images as reviewed as you work through the batch. Once enough images are labeled, **add them to the dataset** and pick a train / validation / test split. Then open **Snapshots → Create Snapshot** to freeze an immutable [**dataset version**](/guides/data/dataset-versioning). You can optionally add preprocessing (such as dropping un-annotated images) and augmentation (such as horizontal flips) to the training split. Choose **Train Model** on your dataset version. Pick an architecture — **YOLOv8** is a solid default — and a model size (**Nano** is fast and ideal for edge devices). Accuracy and loss metrics (mAP, precision, recall) stream in live as training runs. Open **Models**, select your trained model, and choose **Deploy**. Pick the **group** to deploy to and confirm — every device in it picks up the model on its next check-in. Open **Video** to watch live inference — detections appear in real time, with **Good** / **Defect** events listed alongside. That's the full loop. Adapting a model later — as parts change or new defects appear — is the same path: collect more images, re-annotate, snapshot, retrain, redeploy. ## Next steps Where you go next depends on what you're here to do: Go deeper on annotation, dataset versioning, training, and deployment. Provision edge devices and operate them in production. REST API, live detection data, and Co-Pilot/MCP. Follow a tutorial and see the platform end to end. Want this same flow with a worked example and screenshots? [Build a defect detector](/tutorials/defect-detector) walks the whole loop as a guided tutorial.