Towards AI

JUNE 6, 2023

Achieving Peak Performance: Mastering Control and Generalization Source: Image created by Jan Marcel Kezmann Today, we’re going to explore a crucial decision that researchers and practitioners face when training machine and deep learning models: Should we stick to a fixed custom dataset or embrace the power of cross-validation techniques?

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Mlearning.ai

FEBRUARY 2, 2023

Data scientists use a technique called cross validation to help estimate the performance of a model as well as prevent the model from… Continue reading on MLearning.ai »

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Flipboard

JUNE 1, 2025

Ground truth cross-validation was conducted using high-resolution satellite imagery from Google Earth, combined with an NDVI overlay derived from Landsat 8 data. The ensemble model achieved an overall accuracy (OA) of 92.2% and a kappa coefficient (KC) of 0.84. The models, RF had an OA of 91.4% and KC of 0.82, SVM had 88.3%

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Towards AI

AUGUST 23, 2024

Like regular ML, LLM hyperparameters (e.g., The evaluation process should mirror standard machine learning practices; using train-test-validation splits or k-fold cross-validation, finding an updated version and evaluating it on the keep aside population. temperature or model version) should be logged as well.

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Mlearning.ai

JANUARY 27, 2023

How we do this is the subject of the concept of cross-validation. With cross-validation methods, I will actually change this selection and division procedure dynamically and try to utilize all the data I have. Diagram of k-fold cross-validation. Cross-validation is not actually (just) a validation process.

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Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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Women in Big Data

JUNE 6, 2025

Rupa, an AI/ML Solution Architect and Senior Data Scientist at Siemens championed the program and served as the primary organizer and Stuti, Lead Data Scientist at Samsung provided technical guidance and coordination throughout the 8 week program. Data Bias Discussion: Somya asked about inherent bias in the dataset.

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AWS Machine Learning Blog

JANUARY 26, 2023

Amazon SageMaker is a fully managed machine learning (ML) service providing various tools to build, train, optimize, and deploy ML models. ML insights facilitate decision-making. To assess the risk of credit applications, ML uses various data sources, thereby predicting the risk that a customer will be delinquent.

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Towards AI

FEBRUARY 3, 2025

Inspired by Deepseeker: Dynamically Choosing and Combining ML Models for Optimal Performance This member-only story is on us. Traditionally, we rely on cross-validation to test multiple models XGBoost, LGBM, Random Forest, etc. and pick the best one based on validation performance. Upgrade to access all of Medium.

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Mlearning.ai

FEBRUARY 15, 2023

The accuracy of the ML model indicates how many times it was correct overall. Submission Suggestions Text Classification in NLP using Cross Validation and BERT was originally published in MLearning.ai Precision refers to how well the model predicts a certain category. Tanveer, M., & Suganthan, P.

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Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

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DrivenData Labs

JANUARY 22, 2025

Final Stage Overall Prizes where models were rigorously evaluated with cross-validation and model reports were judged by a panel of experts. The cross-validations for all winners were reproduced by the DrivenData team. Lower is better. Unsurprisingly, the 0.10 quantile was easier to predict than the 0.90

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Flipboard

MAY 21, 2025

Hyperparameter optimization and tenfold cross-validation were used to assist in choosing model parameters. Logistic regression was employed to identify the significant features linked to extubation outcomes from a pool of 31 variables. A total of eight independent models and a weighted posterior voting ensemble model were developed.

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Pickl AI

FEBRUARY 17, 2025

This scenario highlights a common reality in the Machine Learning landscape: despite the hype surrounding ML capabilities, many projects fail to deliver expected results due to various challenges. Machine Learning (ML) has emerged as a transformative force across various industries, revolutionising how businesses operate and make decisions.

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Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Towards AI

AUGUST 23, 2024

Simplifying LLM Development: Treat It Like Regular ML Photo by Daniel K Cheung on Unsplash Large Language Models (LLMs) are the latest buzz, often seen as both exciting and intimidating. Like regular ML, LLM hyperparameters (e.g., temperature or model version) should be logged as well.

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AWS Machine Learning Blog

DECEMBER 13, 2024

Amazon SageMaker Pipelines includes features that allow you to streamline and automate machine learning (ML) workflows. Ensemble models are becoming popular within the ML communities. Pipelines can quickly be used to create and end-to-end ML pipeline for ensemble models.

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Flipboard

MAY 1, 2025

For example, a single mortgage application might require manual review and cross-validation of hundreds of pages of tax returns, pay stubs, bank statements, and legal documents, consuming significant time and resources. Jady Liu is a Senior AI/ML Solutions Architect on the AWS GenAI Labs team based in Los Angeles, CA.

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DrivenData Labs

MAY 22, 2024

Also, I have 10 years of experience with C++ cross-platform development, especially in the medical imaging domain, and for embedded solutions. Vitaly Bondar: ML Team lead in theMind (formerly Neuromation) company with 6 years of experience in ML/AI and almost 20 years of experience in the industry.

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Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

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How to Learn Machine Learning

MAY 21, 2025

Here’s why the it deserves a special place in your ML toolkit: It punishes overconfidence Making bold predictions that turn out wrong? Other Metrics You might be wondering how Brier compares to other metrics like log loss (cross-entropy) or AUC-ROC. This is where our hero the Brier Score truly shines!

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Heartbeat

FEBRUARY 20, 2023

Comet ML has an intricate web of tools that combine simplicity and safety and allows one to not only track changes in their model but also deploy them as desired or shared in teams. Workflow Overview The typical iterative ML workflow involves preprocessing a dataset and then developing the model further. Big teams rely on big ideas.

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Mlearning.ai

JUNE 7, 2023

Machine learning is a rapidly evolving field that provides powerful tools for data analysis and prediction. Continue reading on MLearning.ai »

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. ML-based predictive models nowadays may consider time-dependent components — seasonality, trends, cycles, irregular components, etc. — to

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Ocean Protocol

FEBRUARY 2, 2023

Indeed, the most robust predictive trading algorithms use machine learning (ML) techniques. On the optimistic side, algorithmically trading assets with predictive ML models can yield enormous gains à la Renaissance Technologies… Yet algorithmic trading gone awry can yield enormous losses as in the latest FTX scandal.

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AWS Machine Learning Blog

MAY 31, 2024

Here, we use AWS HealthOmics storage as a convenient and cost-effective omic data store and Amazon Sagemaker as a fully managed machine learning (ML) service to train and deploy the model. With SageMaker Training, a managed batch ML compute service, users can efficiently train models without having to manage the underlying infrastructure.

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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

OCTOBER 3, 2024

For information about how you can manage and process your own unstructured data, see Unstructured data management and governance using AWS AI/ML and analytics services. Visier has written a full tutorial about how to use Visier Data in Amazon SageMaker and have also built a Python connector available on their GitHub repo.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

For the classfier, we employed a classic ML algorithm, k-NN, using the scikit-learn Python module. To implement the classifier, we employed a classic ML algorithm, SVM, using the scikit-learn Python module. The aim is to understand which approach is most suitable for addressing the presented challenge.

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Mlearning.ai

FEBRUARY 3, 2023

Example: Think of the ML model as a robot that you want to teach how to do a specific task, like recognizing animals. Parameters are values that are learned by an ML model during the training process, while Hyperparameters are set prior to training and remain constant during the training process.

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The MLOps Blog

DECEMBER 7, 2022

And we at deployr , worked alongside them to find the best possible answers for everyone involved and build their Data and ML Pipelines. Building data and ML pipelines: from the ground to the cloud It was the beginning of 2022, and things were looking bright after the lockdown’s end. With that out of the way, let’s dig in!

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Pickl AI

NOVEMBER 28, 2024

Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. As businesses increasingly rely on ML to gain insights and improve decision-making, the demand for skilled professionals surges. This growth signifies Python’s increasing role in ML and related fields.

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Mlearning.ai

MARCH 7, 2023

The goal of ML is to discover patterns and not simply memorize our training data, the fundamental problem is how to discover that pattern that generalizes. In real-life ML work, we fit models using a finite collection of data even with the most extreme scale, the number of available data points remains small.

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Ocean Protocol

SEPTEMBER 29, 2023

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. By leveraging cross-validation, we ensured the model’s assessment wasn’t reliant on a singular data split.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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Mlearning.ai

JULY 6, 2023

AI-generated image ( craiyon ) In machine learning (ML), a hyperparameter is a parameter whose value is given by the user and used to control the learning process. This is in contrast to other parameters, whose values are obtained algorithmically via training.

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