mindsdb/mindsdb:lightwood
Docker image. Learn more here.CREATE DATABASE
statement.
ma
column, which is a moving average of the historical median price for house sales. Looking at the data, you can see several entries for the same date, which depend on two factors: how many bedrooms the properties have, and whether properties are “houses” or “units”. This means that we can have up to ten different groupings here. Let’s look at the data for one of them.
CREATE MODEL
statement, where we specify what data to train FROM
and what we want to PREDICT
.
LATEST
keyword.
type
and bedrooms
columns and check how the forecast varies. This is because MindsDB recognizes each grouping as being its own different time series.
CREATE JOB
statement to create a Job.
Now, let’s use a Job to retrain the model every two days, just like we might in production. You can retrain the model to improve predictions every time when either new data or new MindsDB version is available. And, if you want to retrain your model considering only new data, then go for finetuning it.
house_sales
table. Learn more about the LAST
keyword here.
And there you have it! You created an end-to-end automated production ML system in a few short minutes.