Five Steps to Create a New AI Model
Summary
TLDRDeep learning has revolutionized AI model development with foundation models, streamlining the process from data preparation to deployment. These models, adaptable through fine-tuning, accelerate specialized AI creation. The workflow involves data preparation, model training, validation, tuning, and deployment. IBM's Watsonx platform facilitates this, encompassing data management, governance, and AI interaction, promoting efficient AI lifecycle management.
Takeaways
- š¤ **Deep Learning Specialization**: Deep learning allows for the creation of specialized AI models such as chatbots and fraud detection systems.
- š **Foundation Models**: Foundation models provide a base that can be fine-tuned for specific applications, streamlining the AI development process.
- š¾ **Data Preparation**: Stage 1 involves preparing large amounts of data from various domains, including categorization and filtering.
- šļø **Model Training**: Stage 2 is about training the model on the prepared data piles, which can involve various types of foundation models.
- š **Validation**: After training, models are benchmarked in Stage 3 to assess their performance and quality.
- š ļø **Fine-Tuning**: In Stage 4, non-AI experts can fine-tune the model with local data to improve its performance.
- š **Deployment**: Stage 5 covers deploying the model either as a service in the cloud or embedded in an application.
- š **IBM's Watsonx**: IBM's Watsonx platform supports all stages of the AI model development workflow.
- š§ **Watsonx.data**: Watsonx.data is a data lakehouse that connects to data repositories for Stage 1.
- š **Watsonx.governance**: Watsonx.governance manages data and model cards for governance and lifecycle management.
- š¤ **Engagement**: Watsonx.ai enables application developers to engage with the model in Stage 4.
Q & A
What is the significance of deep learning in building specialized AI models?
-Deep learning allows for the creation of detailed and specialized AI models, such as customer service chatbots or fraud detection systems in banking, by training them with large amounts of labeled data.
What is the traditional process of building a new AI model for a specific specialization?
-The traditional process involves starting from scratch with data selection and curation, labeling, model development, training, and validation for each new specialization.
How do foundation models change the traditional AI model development paradigm?
-Foundation models provide a centralized base model that can be fine-tuned and adapted to specialized models, speeding up the development process.
What is the purpose of fine-tuning a foundation model?
-Fine-tuning adjusts a foundation model to a specific use case by training it with relevant data, which can significantly reduce the time and computational resources required.
What are the five stages of the workflow to create an AI model as described in the script?
-The five stages are: 1) Prepare the data, 2) Train the model, 3) Validate the model, 4) Tune the model, and 5) Deploy the model.
What types of data are used in Stage 1 of the AI model creation workflow?
-Stage 1 uses a combination of open source data and proprietary data across various domains, which may include petabytes of data.
What data processing tasks are performed during the preparation of data in Stage 1?
-Data processing tasks include categorization, filtering for inappropriate content, and removal of duplicates, resulting in a base data pile.
How does the selection of a foundational model affect the training process in Stage 2?
-The choice of foundational model influences the training process by determining the type of data it will work with and the complexity of the model, which can affect training duration and resource requirements.
What is the role of the application developer in Stage 4 of the AI model workflow?
-In Stage 4, the application developer engages with the model to generate prompts and may provide additional local data to fine-tune the model for better performance.
How does the IBM Watsonx platform support the AI model creation workflow?
-IBM Watsonx supports the workflow with three elements: Watsonx.data for data management, Watsonx.governance for data and model governance, and Watsonx.ai for application developer engagement.
What benefits do foundation models offer in terms of AI model development?
-Foundation models enable the creation of sophisticated AI applications more quickly by providing a base model that can be adapted to various specializations through fine-tuning.
Outlines
š¤ Deep Learning and Foundation Models
This paragraph discusses the evolution of AI model development with deep learning, emphasizing the importance of data gathering, labeling, and training. It introduces the concept of foundation models as a base for creating specialized AI models through fine-tuning. The paragraph outlines the five stages of AI model development: data preparation, model training, validation, tuning, and deployment. Data preparation involves categorization, filtering out unwanted content, and creating a base data pile. Model training involves selecting a foundational model, tokenizing data, and training the model, which can be computationally intensive. Validation assesses the model's performance against benchmarks. Tuning allows developers to improve model performance with local data and prompts. Deployment can be either as a cloud service or embedded in an application.
š Streamlining AI Model Development with IBM's Watsonx
The second paragraph focuses on the practical application of the five-stage workflow for AI model development, as facilitated by IBM's Watsonx platform. Watsonx streamlines the process by providing tools for each stage: Watsonx.data for data management, Watsonx.governance for overseeing data and model cards, and Watsonx.ai for application developers to engage with the model. The paragraph highlights how foundation models are revolutionizing AI model development, allowing for more sophisticated and rapid creation of AI applications. The platform is built on IBM's hybrid cloud platform, Red Hat OpenShift, indicating a robust infrastructure for AI development.
Mindmap
Keywords
š”Deep learning
š”Data labeling
š”Foundation model
š”Fine-tuning
š”Data processing tasks
š”Model validation
š”Application developer
š”Deployment
š”Watsonx
š”Hybrid cloud platform
š”Data governance
Highlights
Deep learning enables building detailed specialized AI models with sufficient data.
Foundation models change the paradigm by providing a base model adaptable to specializations.
Foundation models can be fine-tuned with specialized data for rapid AI model development.
Stage 1 of AI model creation involves preparing data, which may include petabytes of data across domains.
Data processing in Stage 1 includes categorization, filtering for inappropriate content, and removing duplicates.
The output of Stage 1 is a base data pile, which is versioned and tagged for governance.
Stage 2 involves training the model on the base data piles using various types of foundation models.
Tokenization is a key step in preparing data for training foundation models.
Training foundation models can be computationally intensive, taking months and thousands of GPUs.
Stage 3 is validation, where the model's performance is benchmarked and a model card is created.
Stage 4 introduces the application developer who fine-tunes the model with local data and prompts.
Fine-tuning can be done quickly, in hours or days, compared to building a model from scratch.
Stage 5 is deployment, where the model can be offered as a service or embedded into applications.
IBM's Watsonx platform supports all five stages of the AI model creation workflow.
Watsonx.data connects with data repositories, Watsonx.governance manages data and model cards, and Watsonx.ai engages developers.
The 5-stage workflow allows for the creation of sophisticated AI applications more rapidly.
Transcripts
Deep learning has enabled us toĀ build detailed specialized AI models,Ā Ā
and we can do that provided we gather enough data,
label it, and useĀ that to train and deploy those models.
Models like customer service chatbots or fraud detection inĀ banking.
Now, in the past if you wanted to build a new model for your specialization -
so, say a model forĀ predictive maintenance in manufacturing -
well, youād need to start again with data selectionĀ and curation,
labeling, model development,Ā training, and validation.
But foundation modelsĀ are changing that paradigm.
So what is a foundation model?
A foundation model is a more focused, centralized effort to createĀ a base model.
And, through fine tuning, that base foundation model can be adapted to a specializedĀ model.
Need an AI model for programming language translation?
Well, start with a foundational model
andĀ then fine tune it with programming language data.
Fine tuning and adapting base foundationĀ models rapidly speeds up AI model development.
So, how do we do that?
Letās look at the fiveĀ stages of the workflow to create an AI model.
Stage 1 is to prepare the data.
Now in this stage we need to trainĀ 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 acrossĀ dozens of domains.
The data can combine both available open source data and proprietaryĀ data.
Now this stage performs a series of data processing tasks.
Those includeĀ categorization which describes what the data is.
So which data is English, which is German?
WhichĀ is Ansible which is Java? That sort of thing.
Then the data is also applied with a filtere.
So filtering allows us to, for example, apply filters for hate speech,
and profanity and abuse, and that sort of thing.
Stuff we want to filter out of the system that we don't train the model on it.
Other filters may flag copyrighted material, 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.
AndĀ that allows us to say, "This is what Iām training the AI model on, and here are theĀ filters I used".
It's perfect for governance.
Now, Stage 2 is to train the model.
And we're going to train the model on those base data piles.
So we startĀ this stage by picking the foundational model we want to use.
So we will select our model.
Now, there are many types of foundation models.
There are generative foundationĀ models, encoder-only models, lightweight models, high parameter models.
Are you looking to build anĀ AI model to use as a chatbot, or as a classifier?
So pick the foundational model that matches your useĀ 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 resultĀ in potentially trillions of tokens.Ā Ā
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 foundationĀ models can take months with many thousands of GPUs.
But, once itās done, the longest andĀ highest computational costs are behind us.
Stage 3 is "validate".
When training isĀ finished we benchmark the model.
And this involves running the model
and assessing itsĀ performance against a set of benchmarks
that help define the quality of the model.
And then from here we can create a model card
that says this is the model Iāve trained
andĀ these are the benchmark scores it has achieved.
Now up until this point the main persona thatĀ has performed these tasks
is the data scientist.
Now Stage 4 is "tune",
and this is where we bring in theĀ persona of the application developer.
This persona does not need to be an AI expert.
They engage withĀ the model, generating - for example - prompts that elicit good performance from the model.
TheyĀ 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 quickerĀ than building a model from scratch.
And now weāre ready for Stage 5, which is to deployment the model.
Now thisĀ model could run as as service offering deployed to a public cloud.
Or we could, alternatively, embed the model into anĀ application that runs much closer to the edgeĀ of the network.
Either way we can continueĀ to iterate and improve the model over time.
Now here at IBM weāve announced a platformĀ that enables all 5 of the stages of this workflow.
And Itās called watsonx and itās composed ofĀ three elements.
So we have: watsonx.data, watsonx.governance, and watsonx.ai.,
and this all built on IBMāsĀ hybrid cloud platform which is Red Hat OpenShift.
Now Watsonx.data is a modern data lakehouse
and establishes connections with the dataĀ repositories that make up the data inĀ Stage 1.
Watsonx.governance manages the data cards from Stage 1 and model cards fromĀ Stage 3
enabling a collection of fact sheets that ensure a well-governed AI process andĀ lifecycle.
And watsonx.ai provides a means for the application developer personaĀ to engage with the model in Stage 4.
Overall foundation models are changingĀ the way we build specialized AI modelsĀ Ā
and this 5-stage workflow allows teams toĀ create AI and AI-derived applications
with greater sophistication while rapidlyĀ speeding up AI model development.
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