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Machine Learning Models: 4 Ways to Test them in Production

Data Science Dojo

Modern businesses are embracing machine learning (ML) models to gain a competitive edge. Deploying ML models in their day-to-day processes allows businesses to adopt and integrate AI-powered solutions into their businesses. This reiterates the increasing role of AI in modern businesses and consequently the need for ML models.

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Maximizing Your Model Potential: Custom Dataset vs. Cross-Validation

Towards AI

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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Reinforcement Learning-Driven Adaptive Model Selection and Blending for Supervised Learning

Towards AI

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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Meet the winners of the Forecast and Final Prize Stages of the Water Supply Forecast Rodeo

DrivenData Labs

A separate blog post describes the results and winners of the Hindcast Stage , all of whom won prizes in subsequent phases. This blog post presents the winners of all remaining stages: Forecast Stage where models made near-real-time forecasts for the 2024 forecast season. Lower is better.

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Understanding Machine Learning Challenges: Insights for Professionals

Pickl AI

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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Mastering ML Model Performance: Best Practices for Optimal Results

Iguazio

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.

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

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.