Data and training preparation
Prepare the data your models will learn from.
Organise examples around the task, review their quality and preserve the data used by each run. Give domain experts a clear role in preparing training material and assessing new cases.
How does Bibha help prepare training data?
Bibha supports datasets and training segments, data import, example preparation and validation. Teams can review duplicate or low-quality material, preserve dataset versions and separate training examples from test cases. Provenance, sensitive-data preparation, expert annotation, export and retention help manage how the material is used through the model-development lifecycle.
Start with the work the model must learn.
Useful training material connects an input with the result the business expects. For example, a request may be paired with an agreed classification or the fields an operational record should contain. Examples should reflect the task you intend to teach.
Datasets
Organise examples intended for model development and testing into datasets. Keep the material connected to the task the team is working on.
Training segments
Select training material for a particular learning purpose. Use a segment to focus model work on a task, topic or business context.
Example preparation
Prepare task instructions, inputs and expected outputs in a form the model-development process can use. Make the intended result clear enough for a domain reviewer to assess.
Bring in approved material and check its shape.
Before examples are useful for training, they need to arrive from supported sources and contain the information the task requires. Import and validation address that preparation step.
Data import
Bring approved examples into a dataset from supported sources. Confirm the source and input format for the model-development work.
Data validation
Check required fields, malformed examples and incomplete content. Resolve unusable inputs before treating them as training material.
Put domain knowledge into the review.
Business expertise matters when examples disagree, important cases are missing or the expected answer is unclear. Review gives the team a way to correct the material before relying on it.
Duplicate and quality review
Identify repeated, contradictory or low-quality examples for review. Check whether the examples represent the task as it is actually performed.
Expert annotation and review
Capture domain experts' labels, corrections and preferred examples. Use their judgement to make the intended behaviour explicit.
Keep a record of what each run used.
A result is easier to interpret when the examples behind it are stable and identifiable. Testing also needs unfamiliar cases, so the team can assess behaviour beyond the material used for learning.
Dataset versions
Preserve a stable record of the examples used for a particular run. Connect the model-development result to that data version.
Separate training and test examples
Keep learning material separate from evaluation cases. Use the reserved examples to review performance beyond the material included in that training run.
Define permitted use before the data is reused.
Data preparation includes decisions about origin, permission and unnecessary sensitive content. Those decisions belong with the examples, not in an assumption that any available material can be used for any purpose.
Data rights and provenance
Record where examples came from and their permitted uses. Make origin and reuse decisions reviewable as the material moves through development.
Sensitive-data preparation
Remove or minimise unnecessary personal and confidential fields. Keep the material focused on the information the task needs.
Data export and retention
Retrieve approved data assets and apply their retention rules. Define the access, export and removal responsibilities for the engagement.
Decide what belongs in training and what belongs in knowledge.
Training examples shape model behaviour. Reference knowledge supplies information while the system works. A changing policy may be better supplied as connected knowledge; repeated examples of a specialist classification may be useful for model adaptation.
The two approaches can work together. Your data plan should identify the task, the examples that teach it and the information that needs to stay current during operation.
Bring representative examples to the first discussion.
Describe the task, where its source material comes from, who can review it and what a correct result should contain. We can then work through the preparation, model and evaluation approach. Leave confidential or personal records out of the initial enquiry.
Questions and answers
A training segment is a selected set of material focused on a particular learning purpose, such as a task, topic or business context. It helps organise the examples used for model development.
Yes. Bibha supports checks for required fields, malformed examples and incomplete content, alongside duplicate and quality review. Domain experts can add labels, corrections and preferred examples.
Training material is used to teach or adapt the model. Separate test cases let the team assess how it handles unfamiliar work, rather than relying only on results from examples it has already seen.
No. Reference material can be connected as knowledge for the system to use at runtime. Training and fine-tuning are relevant when the task calls for changes to model behaviour using examples.
Data import works with supported sources and formats. Share the material types, access requirements and intended model task so the appropriate input configuration can be confirmed.