If you have been following our blog or someone who is technical and knows how AI works, then you would know that current AI models use huge amounts of data for their training. They train on huge datasets. And every time you train an AI model, you need an additional amount of data, so the data keeps on growing that no team can manually keep a record of every dataset.
And if this information isn’t organized properly, the entire AI workflow can collapse. Models can get mixed up. And if by mistake old data gets used then the result cannot be produced up to the quality standards that need to be met.
So, to prevent all these problems from happening, AI companies use metadata stores. They are the control centers that store data details. They keep track of every detail about data: what data was used to train the AI model, which model version was trained on which data sets, etc.
Instead of storing the actual data, it stores details about that data.
To understand it clearly, relate it with daily life examples. Suppose you need a book and you go to the library to get it issued. But you don’t know where you will find that book in the library. For this purpose you use the library catalog. The catalog doesn’t keep the books inside it. It only stores the information like the title, author, shelf number, and category. that helps you find the right book quickly. So, a metadata store is like this library catalog that contains information about data but not the data itself.
Read More: Why Big Tech Is Betting on Application-Specific Integrated Circuits for AI Acceleration
Why AI Companies Can’t Work Without Metadata Stores
During the developmental and training phases of AI model development, AI teams try many different versions and use trial and error to figure out which model performs the best. For this purpose, they run hundreds of experiments using different datasets, parameters, specifications, etc.
Metadata stores help teams to keep track of every version, every change, and every result. Without these data control centers, teams would constantly lose track of what they tried, what worked for their model and what did not work. These stores keep everything organized and managed, so that teams can work smoothly at optimal speed without messing up data.
In finance, healthcare, or legal work, companies need to explain why an AI system made a certain decision. A metadata store keeps all the background details, so you can easily trace how a model was trained and what influenced its choices.
Our current large language models run thousands of automated tasks in various steps. For their perfect functionality and preventing any chaos, they need proper tracking systems in the form of metadata stores to keep pipelines running in an organized, step-by-step way so that not a single step gets mixed up and every task has a clear record behind it.
How Does a Metadata Store Work?
Metadata stores work by simply collecting the metadata, organizing it properly, and then using it in an organized manner to help the AI pipeline work smoothly.
It Collects Metadata From Different Sources
In this step, the metadata store collects the metadata about the data that is being used by AI models. This metadata can be in the following forms:
- What dataset was used
- Which version of the model ran
- What settings were chosen
- What results came out
- Which experiment was tried
- What errors appeared
A metadata store gathers all these small details in one place. This detail about the data can come from the data pipeline, the dataset being used, notebooks where experiments are written, feature stores, log, etc. The metadata store collects this information in the central inbox, so nothing gets lost.
It Organizes Everything in a Structured Way
Once the information is collected, the metadata store sorts the collected metadata. It creates different domains or groups to store different types of information. For example
It stores details about models in one category, may be named as model, performance metrics in another category, training settings under the training metadata category, and so on. It.
It Helps AI Pipelines Use That Metadata Automatically
Now, in the third and last step, metadata stores help AI pipelines use metadata automatically. For example, there is an AI model ABC that was trained initially using dataset V3. The metadata store will tell the trainer automatically to use the same data set to retrain the ABC model rather than using V2 or V1 mistakenly.
So, overall, it uses the stored information to:
- retrain models correctly
- monitor performance
- schedule jobs and tasks automatically
As a result, AI pipelines can run with fewer mistakes and much less manual work.
What Types of Metadata Does It Store?
A metadata store keeps different kinds of information to help AI systems stay organized. There are four main types of store and each store keeps different types of information about data.
Data Metadata
Data metadata is the basic information about the data that an AI model uses. It doesn’t include the actual data but only the important details about it.
It tells AI teams about:
- What columns or fields data has
- Where the data files are kept
- Which version of the data you are using
- Where the data originally came from e.g. pipelines, feature stores, log etc.
Data metadata stores make it organized and easy for AI teams to understand the features and parameters of the data they are working on.
The data metadata store keeps a record of every change made to the data. If the data was updated, cleaned, corrected, or replaced, the metadata store remembers each version. It makes sure everyone is working with the correct and most updated version of the dataset.
Model Metadata
Model metadata stores information about the AI model. It tells which version of the AI model you used, with what settings you trained the AI model with, how well the AI model performed, and what results it gave during testing.
This information makes it easy for teams to compare different models and see which one gives the best results and why it worked better than the others.
Pipeline Metadata
Pipeline metadata is the information about what happened while the AI was running. It saves things like:
- a special ID or number for each run
- notes about what the system did
- the files the AI created
- any mistakes or errors that happened
A pipeline metadata store helps AI teams see all the steps in a clear, step-by-step order. It shows how the AI performed its tasks and how the model was trained. If something goes wrong, teams can quickly find the problem and fix it.
Operational Metadata
Operational metadata is the information about how the AI system is being used.
It records metadata like:
- who used the system
- when each task or pipeline was run
- how much computer power was used
This helps teams see how the system is working, check if everything is running correctly, and make the AI system faster and more efficient.
Use Cases of Metadata Stores in AI
Metadata stores help AI systems stay organized by tracking model versions, prompts, results, system activity, and all steps across different AI tasks. Let’s discuss some of the few use cases of metadata stores.
LLM Fine-Tuning and Prompt Management
During the development and training of the Large Language Model, AI teams have to test it many times to see how it responds in different situations.
LLMs don’t always give the same quality of answer. Some prompts work better. Some don’t.
So teams try many prompts and different training steps to find out which prompt gives the most accurate answer, which version of the model performs better, what setting makes the model more helpful, what causes mistakes or wrong responses, etc.
A metadata store keeps track of all these versions, prompts, and results so teams know which one worked best and why.
Fraud Detection Systems
AI used in banks keeps track of all the unusual activities that occur in any of the users’ accounts, like strange transactions or unexpected large payments. In such suspicious cases, a metadata store saves the details about what suspicious element AI found in that activity, why it flagged it, and what pattern it noticed.
By keeping all this information, the system can learn from older cases. So next time, it can spot similar fraud faster and prevent it smartly.
Recommendation Engines
Apps like Netflix, YouTube, or Amazon use AI to recommend content or products. A metadata store keeps track of user behavior patterns and performance results. This helps these recommendation apps to improve their recommendations over time.
FAQs about Metadata Store
1. What is metadata?
Metadata is information about your data. It’s the minor details that describe the real data. For example, if a photo is a video then its dimensions, size, and location are metadata. It’s just the small details that describe the real data.
2. Is a metadata store the same as a database?
No. A database stores the actual data. A metadata store only stores information about that data but not the data itself.
3. Why do AI teams need a metadata store?
AI teams use a metadata store to stay organized. It helps them track which data, model, and settings were used so they don’t get confused or make mistakes. It also makes it easy to repeat or fix any experiment.
4. What metadata does an AI model generate?
An AI model generates details like which dataset was used, what version of the model ran, what training settings were chosen, how well the model performed etc. These details are metadata.