AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.

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The MLOps Blog

DECEMBER 7, 2022

Given that the whole theory of machine learning assumes today will behave at least somewhat like yesterday, what can algorithms and models do for you in such a chaotic context ? 2 To teach them how to use the stack considered best for them (mostly focusing on fundamentals of MLOps and AWS Sagemaker / Sagemaker Studio).

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Heartbeat

MAY 29, 2023

BERT model architecture; image from TDS Hyperparameter tuning Hyperparameter tuning is the process of selecting the optimal hyperparameters for a machine learning algorithm. Use a representative and diverse validation dataset to ensure that the model is not overfitting to the training data.

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DagsHub

APRIL 21, 2024

Autonomous Vehicles: Automotive companies are using ML models for autonomous driving systems including object detection, path planning, and decision-making algorithms. MLOps ensures the reliability and safety of these models through rigorous testing, validation, and continuous monitoring in real-world driving conditions.

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Pickl AI

APRIL 26, 2024

Techniques such as cross-validation, regularisation , and feature selection can prevent overfitting. Then, I would explore forecasting models such as ARIMA, exponential smoothing, or machine learning algorithms like random forests or gradient boosting to predict future sales.

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Source: Equifax) Part 1 of this series discusses the most common challenges associated with the manual lending process.

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AWS Machine Learning Blog

JULY 20, 2023

Ensemble learning refers to the use of multiple learning models and algorithms to gain more accurate predictions than any single, individual learning algorithm. This final estimator’s training process often uses cross-validation. We also implement a k-fold cross validation function. Computer Communications.

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AWS Machine Learning Blog

JANUARY 26, 2023

Data scientists train multiple ML algorithms to examine millions of consumer data records, identify anomalies, and evaluate if a person is eligible for credit. Best Egg trains multiple credit models using classification and regression algorithms. Valerio Perrone is an Applied Science Manager at AWS. Solutions Architect at AWS.

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