We are excited to introduce to our community a long awaiting new feature - job scheduling. This feature enables you to automate repetitive tasks, set up regular model re-training, and easily manage machine learning workflows. Whether you're a seasoned developer or data scientist, or just getting started with AI, job scheduling will make your life easier. So, let's dive in and explore this exciting new addition to the MindsDB platform!
Here is how to create a job in MindsDB:
Let’s briefly analyze this syntax:
Check out our docs here to learn more about jobs.
In this chapter, we’ll explore the various use cases of the CREATE JOB statement in MindsDB. This powerful feature allows users to automate their ML workflows and streamline the process of creating predictive models. With the ability to schedule and manage jobs, users can save time and resources while improving the accuracy and efficiency of their data analysis. Let's see how this feature can benefit your work.
You may need to retrain your model when there are new training data available or when the MindsDB version is updated. Here is how you can create a job that retrains your model regularly and saves predictions into your database table:
In the previous example, we saved predictions into the already existing table. It is also possible to create a table on the fly.
We name the job save_predictions. Next, we define the SQL code to be executed by this job:
Here, the START and END clauses are omitted. Therefore, the job starts its execution right away and executes every hour until manually disabled.
You can create a one-time job to drop your model at a defined date.
We name the job drop_model. Next, we define the SQL code to be executed by this job - the DROP MODEL statement removes the home_rentals_model model from the mindsdb project.
After providing valuable insight into the CREATE JOB statement in MindsDB, we encourage you to give this feature a try and see how it can benefit your work.
You can use the job scheduling feature to create a chatbot that replies to messages using the underlying OpenAI model. Have a look at our Twitter chatbot implementation here.
Go ahead and create a demo account at MindsDB Cloud to get started. Don't hesitate to reach out via the Slack community or GitHub if you have any questions or need assistance. We're here to support you every step of the way!