- Predictive Analytics
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Predictive analysis is the future outcome forecasting to measure
businesses success rate by historical data analysis. It is an
application of Statistical and ML models that covers the gap between
what happened before and what will happen next.
- Predictive Modeling
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It is a process including model designing, training, and validating
the solution accuracy upon quality assurance. All combined add
features that determine the possible measured outcomes of certain
data-driven decision making. It covers classification, regression,
time series, and event history modeling.
- Classification Model
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This is a separate model that determines whether churn occurs or not,
fraud or not, low or high risk categorization. Common statistical
algorithms used in classification models are logistic regression,
XGBoost, LightGBM, and artificial neural networks (ANNs).
- Regression Model
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Regression analysis is used to find the relationship between changes
occurring in some data. It produces continuous values such as next
month’s sales, average claim cost, and customer LTV. Common
processes include linear regression, gradient boosting regression,
and GLMs.
- Time Series Analysis
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It is a sequential data analysis technique used for personalized
forecasting, machine part failure, and financial time series. The
technology tools used for time series analysis include ARIMA, LSTM,
and Temporal Fusion Transformers.
- Golden Set
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Golden datasets are predefined 500 to 5,000 examples of input and
output data that vary across all industry use cases. Each tells the
correct option for the respective prompt. No model is deployed until
it meets the golden dataset of the eval harness gatepass.
- Eval Gate
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Trango Tech’s eval harness includes 7 checkpoints that a model
must pass to reach production. The control points are accuracy,
calibration, drift sensitivity, bias, cost per prediction, latency,
and robustness. Shipments are made upon successful eval harness
gatepass.
- Data Drift
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Data drift is the change in data behavior in production over time. It
includes the transfer in distribution that is called input drift, the
connection between data input and output that is called concept
drift, and the shifted result data that is called label drift.
- MLOps
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Machine learning operations are a set of engineering practices that
monitor, retrain, and deploy models in production. The operations
include, but are not limited to, versioning, lineage, eval gate, drift
detection, retraining, and governance.
- Feature Engineering
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The process of transforming the raw data into a clean and structured
form so that models can learn from it. Usually, the activities with
higher return on time in predictive analytics solutions development
depend more on the data quality than the maths used for analysis.
- Model Monitoring
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Model monitoring is a practice that involves continuous watch over
the model performance in production. Such as prediction quality,
latency, charges, drift signs, and organizational outcomes. If one is
not mentioning a point, that’s probably where they are lacking.
- Model Card
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Model cards are standardized documentation artifacts that outline the
aim of model building, its training data, keep records of performance,
restrictions, and the use of the model. A model card is a mandatory
requirement of the EU AI Act and is advised to use for the rest of the
AI value chain.