> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Lab Intro > Describes enrolling in course labs, using preconfigured workspaces with temporary API keys to run exercises, and best practices for obtaining and securing provider keys locally. Now that we've covered the theory, it's time to get hands-on with the labs for this lesson. The lab environment is preconfigured so you can focus on building and experimenting with models rather than spending time on setup. To begin, open the course's lab enrollment section on the learning platform. The labs for the first half of this course are free — create a free account and sign in. Once enrolled, you will see the available labs listed and can launch the workspace for this lesson directly. The lab workspaces include the necessary API keys and credentials already baked in for the duration of your session, so you can run the exercises immediately without creating keys elsewhere. A presentation slide titled "LAB ENVIRONMENT" with colorful UI-style boxes listing steps like "Create free account," "Access the labs," and "API keys baked in" on the left. On the right, a bearded man in a white T-shirt wearing a clip microphone stands and gestures while speaking. If you prefer to experiment on your own machine, obtain API keys directly from the model providers you plan to use (for example, Anthropic Claude, OpenAI, Google). Provider accounts typically include a monthly token allowance; paid tiers such as Claude Cloud offer larger quotas. Provider sites also include sample projects and quickstart guides you can use as templates: * [OpenAI](https://openai.com) * [Anthropic](https://www.anthropic.com) * [Google Cloud AI](https://cloud.google.com) The lab-provided API keys are temporary and scoped for the lab environment. If you plan to work outside the lab, generate your own provider keys and store them securely before running code locally. Never commit API keys, secrets, or credentials into public repositories. Use environment variables, secret managers, or local vault tools to keep keys safe when developing outside the lab. Quick start checklist | Step | Action | Notes / Example | | - | - | - | | 1 | Enroll in the course lab | Open the course lab enrollment page and sign in | | 2 | Create or sign in to a free account | No payment required for the first-half labs | | 3 | Launch the lab workspace | Lab workspace includes preconfigured keys and dependencies | | 4 | Run the exercises | Follow the lab steps — the API keys remain valid for the session | | 5 | Work locally (optional) | Obtain provider keys from their sites and follow their quickstarts | Steps to get started * Go to the course's lab enrollment page on the learning platform and sign in. * Create a free account (if you don't already have one). * Open the lab you want to run from the list of available labs. * Use the provided lab workspace — API keys and required dependencies are preconfigured. Best practices when moving from lab to local development * Rotate the temporary lab keys and generate your own provider keys before working locally. * Store keys in environment variables or a secret manager (e.g., AWS Secrets Manager, Google Secret Manager, or HashiCorp Vault). * Read provider documentation for rate limits, token usage, and billing to avoid unexpected charges. Further reading and references * [OpenAI API Documentation](https://platform.openai.com/docs) * [Anthropic Documentation](https://help.anthropic.com/) * [Google Cloud AI docs](https://cloud.google.com/docs) Go ahead and complete the lab exercises for this lesson. We'll resume with the next hands-on section after you finish. This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.