Remove Artificial Inteligence Remove Comparison Remove Machine Learning Remove Metrics
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How Prompt-Based Development Revolutionizes Machine Learning Workflows

Mentormate

In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data.

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Identifying the Unknown With Clustering Metrics

Toptal

Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here's how to amplify your data insights with comparison metrics, including the F-measure.

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Streamlining ML Workflows: Integrating MLFlow Tracking with LangTest for Enhanced Model Evaluations

John Snow Labs

Machine Learning (ML) has seen exponential growth in recent years. With an increasing number of models being developed, there’s a growing need for transparent, systematic, and comprehensive tracking of these models. This synergy achieves the following: Transparency: Every run, metric, and insight is documented.

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What’s New and What’s Next in 2023 for HPC

CIO

Moving forward, we will see workflows that are more capable and widely adopted to facilitate edge-core-cloud needs like generating meshes, performing 3D simulations, performing post-simulation data analysis, and feeding data into machine learning models—which support, guide, and in some case replace the need for simulation.

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8 open source companies from YC Demo Day Winter ’22

TechCrunch

How it says it differs from rivals: Tuva uses machine learning to further develop its technology. They’re using that experience to help digital health companies get their data ready for analytics and machine learning. GrowthBook says it solves this by using a company’s existing data infrastructure and business metrics.

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Running Code and Failing Models

DataRobot

Machine learning is a glass cannon. Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

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Offshore Data Scientists

Mobilunity

Methods to Extract Data Insights Machine Learning Algorithms : Machine learning leverages algorithms to enable computers to learn from and make predictions or decisions based on data. Deep Learning : A subset of machine learning, deep learning uses artificial neural networks to process and understand complex data.

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