Deep learning has enabled us to build detailed specialized models,
and we can do thatprovided we gather enough data, label it, and use that to train and deploy those models.
Models like customer service chatbots or fraud detection in banking.
In the past if you wanted to build a new model for your specialization - say a model for predictive maintenance in manufacturing -
well you’d need to start again with data selection and curation, labeling, model development, training, and validation.
But foundation models are changing that paradigm. So what is a foundation model?
A foundation model is a more focused, centralized effort to create a base model.
And through fine tuning that base foundation model can be adapted to a specialized model. Need an AI model for programming language translation?
Well Start with a foundational model and then fine tune it with programming language data.
Fine tuning and adapting base foundation models rapidly speeds up AI model development.
So, how do we do that? Let’s look at the five stages of the workflow to create an AI model.
Stage one is to prepare the data. Now in this stage we need to train our AI model with the data we're going to use,
and we're going to need a lot of data. Potentially petabytes of data across dozens of domains.
The data can combine both available open source data and proprietary data.
Now this stage performs a series of data processing tasks. Those include categorization which describes what the data is.
So which data is English, which is German? Which is Ansible which is Java? That sort of thing.
Then the data is also applied with a filtered.
So filtering allows for us, for example applying filters for hate, abuse, profanity, and that sort of thing.
Stuff we want to filter out of the system that you don't want the model to train on.
Other filters may flag copyrighted material or private or sensitive information. Something else we're going to take out is duplicate data as well.
So we're going to remove that from there. And then that leaves us with something called a base data pile.
So that's really the output of stage one.
And this base data pile can be versioned and tagged. And that allows us to say this is what I’m training the AI model on
and here are the filters I used. Perfect for governance.
Now Stage two is to train the model. And we're going to train the model on those data piles.
So we start this stage by picking the foundational model we want to use. So we will select our model.
There are generative foundation models, encoder only models, lightweight models, high parameter models.
Are you looking to build an AI model to use as a chatbot, or as a classifier?
So pick the foundational model that matches your use case, then match the data pile with that model.
Next we take the data pile and we tokenize it.
Foundation models work with tokens rather than words, and a data pile could result in potentially trillions of tokens.
And now we can engage the process of training using all of those tokens. This process can take a long time, depending on the size of the model.
Large scale foundation models can take months with many thousands of GPUs. But once it’s done the longest and highest computational costs are behind us.
Stage three is validate. When training is finished we benchmark the model.
And this involves running the model and assessing its performance against a set of benchmarks that help define the quality of the model.
From here we can create a model card
that says this is the model I’ve training and these are the benchmark scores it has achieved.
Now up until this point the main persona that has performed these tasks is the data scientist.
Now stage four is tune and this is where we bring in the persona of the application developer.
This persona does not need to be an AI expert. They engage with the model, generating - for example - prompts that elicit good performance from the model.
They can provide additional local data to fine tune the model to improve its performance.
And this stage is something that you can do in hours or days - much quicker than building a model from scratch.
And now we’re ready for Stage five, which is to deployment the model.
Now this The model could run as as service offering deployed to a public cloud.
Or we could alternatively it may be embedded in an application that runs much closer to the edge of the network.
Either way we can continue to iterate and improve the model over time.
Now here at IBM we’ve announced a platform that enables all five of the stages of this workflow.
And It’s called watsonx and it’s composed of three elements: watsonx.data, watsonx.governance, and watsonx.ai.,
and this all built on IBM’s hybrid cloud platform which is Red Hat OpenShift.
Watsonx.data is a modern data lakehouse and establishes connections with the data repositories that make up the data in stage one.
Watsonx.governance manages the data cards from stage one and model cards from stage three
enabling a collection of fact sheets that ensure a well-governed AI process and lifecycle.
And watsonx.ai provides a means for the application developer persona to engage with the model in stage four.
Overall foundation models are changing the way we build specialized AI models
and this five stage workflow allows teams to create AI and derived AI applications
with greater sophistication while rapidly speeding up AI model development.