Browser-local AutoML
Learn how MLdeck keeps the normal CSV modeling loop in the browser while making validation and export responsibilities visible.
MLdeck resources for browser-local AutoML
Use this resource hub to explore MLdeck's public guides, example workflows, documentation, video channel, and company channels. The focus is practical CSV-based machine learning: browser-local AutoML, Data Quality review, validation evidence, ONNX-oriented export packages, and no raw CSV upload during normal browser-local workflows.
Browser-local AutoML
Browser-based AutoML
Compare the browser-based user experience with the browser-local training boundary for practical CSV workflows.
Train ML models in your browser
See the training workflow from CSV review to model comparison, validation evidence, and export artifact context.
AutoML without uploading raw CSV data
Review the normal browser-local workflow scope and the user-side responsibilities that still apply.
Data Quality review
CSV Data Quality Checker
Check missing values, schema signals, risky columns, and CSV readiness before reporting, sharing, cleanup, or AutoML.
Data Quality for Machine Learning
Understand why missing values, leakage risk, identifiers, high cardinality, imbalance, and sample limits matter before training.
Data quality checks example
Read a practical example of Data Quality review before interpreting AutoML metrics or export readiness.
Validation evidence
AutoML validation evidence
Learn how to interpret exploratory metrics, holdouts, baselines, leakage review, reports, and user-side verification.
Local AutoML vs cloud AutoML
Compare local-first exploration with managed cloud workflows, including privacy, setup, validation, and export responsibilities.
ONNX-oriented export packages
AutoML export artifacts
Review reports, manifest-driven package concepts, ONNX-oriented artifacts, and external verification responsibilities.
Export ONNX models from the browser
Understand how browser-local training can lead to ONNX-oriented export packages for external testing and verification.
ONNX export workflow example
Follow an example workflow from CSV training to schema review, parity checks, and export artifact testing.
Example workflows
CSV regression model example
See how numeric target selection, feature review, mean baseline comparison, and export context fit a regression workflow.
Music classification from CSV
Review a tabular classification example using audio-feature CSV data, leakage checks, baselines, and validation context.
Customer churn prediction from CSV
Explore a practical churn workflow with browser-local AutoML, feature review, Data Quality warnings, and exportable artifacts.
Public documentation
Public GitHub documentation repo
Use the public repository for MLdeck documentation, examples, privacy notes, validation notes, and public link maps.
Browser-local AutoML docs
Read the public documentation page that explains the browser-local AutoML positioning and boundaries.
Data Quality docs
Review public documentation for CSV Data Quality concepts and how they connect to MLdeck website guidance.
Export artifacts docs
Connect website export artifact guidance with the public documentation repo's export-artifact notes.
Video demos
MLdeck YouTube channel
Use the channel for planned demos covering browser-local AutoML, Data Quality review, validation evidence, and export artifacts.
End-to-end CSV training topic
This guide is the matching website page for a planned video showing how to train a machine learning model in the browser.
Company channels
MLdeck on LinkedIn
Follow product updates and educational posts about privacy-first CSV workflows, validation evidence, and export readiness.
MLdeck on GitHub
Use the public documentation repo as the safe public source for docs, examples, and link maps.
MLdeck on YouTube
Watch for public walkthroughs and demos that match the website's educational resource cluster.