So in this lecture, we're going to summarize everything we learned in this section, this section was
all about how to use Adewusi forecast, which is an industrial strength time series, forecasting products.
In this section, you learned about the data model that is used by US forecast.
In some ways, it's much more complicated than what we'd been working with in this course prior.
Instead of just a data frame or an umpire, we had to build covid files with a specific schema to fit
what AWI expects.
In other ways, this makes HWC forecasts very flexible.
We know that adding more features or more items is merely just a matter of adding more CSV files.
So there's a tradeoff between flexibility and complexity.
We learn that in order to train the model, it wasn't just a matter of passing our data and do some
function.
Instead, there were several steps that took a few minutes of just waiting.
We had to create data set groups which then held data sets.
There were different kinds of data sets like Target Time series and related time series and metadata.
There were also different data set domains so that the model could be more tailored to your particular
application.
For example, retail forecasting, supply chain forecasting and web traffic forecasting.
We learned about the six different model types available, including a rhema profit CNN, QR and Depay
Plus, which was the one we used.
Finally, we learned how to create a predictor and how to use modern features like Auto Amelle and hyper
parameter optimization, once our predictor was trained, we saw how to use the predictor to make a
forecast.
We saw that the forecast was actually pretty good, although we did only try a single time series.
To close this section, I want to leave you with a few thoughts about how this solution compares to
the other models we've studied.
One thing we didn't explore was whether or not the predictor can make forecasts outside of the specified
time window.
I encourage you to try this on your own, to verify what I'm saying.
But in fact, this is not possible.
As you recall, at one point we defined our forecast horizon and then pass this into our predictor.
So if you want to forecast beyond that window, you want to be able to use the same model.
In fact, if you want to make enough sample predictions, that's also not possible.
This brings me to my next point, which is something worth considering, you actually have no access
to the model at all.
This is really a solution that seems like it's meant for the enterprise crowd rather than practicing
data scientists.
In fact, Amazon advertises that this service can be used without any machine learning experience.
So it's not surprising that the only interface between you and the model is uploading your data to S3
and launching a few jobs using Boso three.
If you did have access to the model, it would be easy to do things like in sample predictions or forecasting
an arbitrary number of time steps.
This is a real disadvantage because it makes it more difficult to debug your model if you want to apply
explainable A.I. techniques.
You can't do that without access to the model.
One practical thing you have to consider is this if you're using this in production and you need to
generate forecasts pretty frequently, what does this entail?
Well, if you did have access to the model, you just call model predict or some other analogous python
function.
But with US forecasts, we've seen that this is not possible.
Instead, you just have to go through the entire process again.
So imagine that you need to generate a new forecast every week.
Then you would have to upload all the data for the most recent week.
Then you have to add your new data set to the data set group.
Then you have to create your new predictor, which might take a few hours.
And only after you've done all that work can you generate a new forecast.
And this model you trained is only good for this week.
Next week you have to go through the same process again.
And remember, this all costs money.
I encourage you to check out the AWB forecast pricing page to see how much this really costs in terms
of dollars and cents.
So if you want to use AWB forecast, it's not just a one time cost, but rather an ongoing cost.
That is, if you want to make forecasts on an ongoing basis.
Then again, this applies to essentially all of Amazon's services.
At the same time, it is a powerful solution and it's really excellent for those who have programming
experience, but no machine learning expertise.