expertise needed
data engineering
Requires skills in building and maintaining data pipelines, storage, versioning, lineage and logging infrastructure.
7 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Datasheets for Datasets | Documentation | Architecture/model Agnostic Requirements/black Box | Any | A datasheet for a dataset is a short document that accompanies the dataset and answers a set of questions about it, in... | ||
| MLflow Experiment Tracking | Process | Architecture/model Agnostic Requirements/black Box | Any | MLflow Tracking is the part of the open-source MLflow platform that records each run of a machine learning training... | ||
| Data Version Control | Process | Architecture/model Agnostic Requirements/black Box | Any | Data Version Control (DVC) is an open-source tool that records which version of a dataset or model was used at each... | ||
| Data Poisoning Detection | Algorithmic | Architecture/model Agnostic Requirements/white Box +1 | Any | Data poisoning detection looks for training examples that an attacker has planted to change how a model behaves. The... | ||
| Training Data Extraction Testing | Testing | Architecture/neural Networks/transformer/llm Paradigm/generative +1 | Text | Training data extraction testing measures how much of its training text a generative language model reproduces word for... | ||
| PII Detection and Redaction in Training Text | Algorithmic | Architecture/model Agnostic Architecture/neural Networks/transformer/llm +1 | Text | PII detection and redaction finds personally identifiable information (PII), such as names, email addresses, phone... | ||
| Data Validation Checks | Testing | Architecture/model Agnostic Requirements/black Box | Tabular | Data validation checks apply a written set of rules about what valid data look like to every new batch of data before a... |
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