How Our AI Chatbot Manages Data Versioning, Quality, and Pipelines
Our AI chatbot automates the full lifecycle of dataset management, from versioning and validation to preprocessing, feature store integration, and scheduled ingestion.
Instead of manually handling DVC/LakeFS, checking for corrupted files, or converting datasets, users simply instruct the agent — and it executes safely, efficiently, and reproducibly across hosts.
Below are examples of dataset management requests and how the agent handles them behind the scenes.
1. “Version my datasets.”
The agent enables dataset versioning by:
- Integrating with DVC, LakeFS, or other versioning systems
- Tracking dataset changes and lineage
- Creating snapshots and committing them to remote storage
- Validating version history and accessibility
Users get reproducible, trackable datasets without manually managing versions.
2. “Detect corrupted files.”
The chatbot ensures dataset integrity by:
- Scanning all files for missing shards, incorrect formats, or checksum mismatches
- Replacing or flagging corrupted files
- Logging affected files for auditing
- Validating the dataset post-repair
This prevents training on incomplete or corrupted data.
3. “Sample a balanced training subset.”
The agent prepares balanced datasets by:
- Detecting class distributions
- Performing stratified or weighted sampling
- Generating subsets suitable for training, validation, or testing
- Preserving reproducibility with fixed random seeds
Users get representative datasets without manual preprocessing.
4. “Compute dataset statistics.”
The chatbot computes detailed statistics, including:
- Mean, median, standard deviation per feature
- Label distributions and class imbalances
- Outlier detection
- Missing value analysis
This provides immediate insight into dataset quality and structure.
5. “Generate TFRecords.”
The agent converts raw data into optimized formats by:
- Transforming images, text, or structured data into TFRecords
- Sharding files for efficient parallel reading
- Validating data integrity and schema
- Supporting downstream TensorFlow or JAX pipelines
Users get high-performance data pipelines without manual conversion.
6. “Push data to feature store.”
The chatbot integrates with feature stores by:
- Connecting to Feast or custom APIs
- Registering datasets and metadata
- Ensuring consistent versioning and access control
- Validating feature availability for training or inference
This enables seamless feature reuse across projects.
7. “Automate daily data ingestion job.”
The agent schedules recurring ingestion pipelines by:
- Creating Kubernetes CronJobs or cloud-native schedulers
- Pulling, preprocessing, and storing new data automatically
- Monitoring pipeline execution and logging results
- Sending alerts on failures or anomalies
Users maintain fresh datasets without manual intervention.
Security and Safety Guarantees
✔ Safe and validated operations
All dataset changes, conversions, and ingestion pipelines are tested before execution.
✔ Permission-aware
Agent respects storage and feature store access policies.
✔ Auditable logs
Every data operation is fully logged for traceability.
✔ Isolation
Data transformations and ingestion jobs run in sandboxed environments to prevent accidental corruption.
Why This Matters
Dataset management is time-consuming and error-prone, from versioning and validation to preprocessing, feature store integration, and scheduled ingestion.
SkyPortal’s chatbot eliminates that friction.
Whether the user wants to:
- Version and track datasets
- Detect and fix corrupted files
- Prepare balanced training subsets
- Compute statistics
- Convert to optimized formats
- Push data to feature stores
- Automate daily ingestion
…they can do it instantly, safely, and without manually managing data pipelines.