Subject archive for "model-production"

Model Management

High-standard ML validation with Deepchecks

We've blogged before about the importance of model validation, a process that ensures that the model is performing the way it was intended and that it solves the problem it was designed to solve. Validations and tests are key elements to building machine learning pipelines you can trust. We've also talked about incorporating tests in your pipeline, which many data scientists find problematic. The issues stem from the fact that not all data scientists feel confident about traditional code testing methods, but more importantly, data science is so much more than just code. When validating pipelines we need to think about verifying the data integrity, inspecting its distributions, validating data splits, model evaluation, model comparison etc. But how can we deal with such complexity and maintain consistency in our pipelines? Enter Deepchecks - an open-source Python package for testing and validating machine learning models and data.

By Noam Bressler14 min read


Celebrate Py Day!

Pi Day is upon us and, while we celebrate “Pi” with pie, we mustn’t forget that other great “Pi” in our lives, which is, of course, Python! That programming language which has emerged as the de facto standard for production-grade data science, used by the majority of production-grade data scientists.

By Kjell Carlsson3 min read

Machine Learning

A Guide to Machine Learning Model

Machine learning is a subset of artificial intelligence (AI) that uses algorithms to learn from trends, data sets and certain behaviors. This process involves the development of machine learning models that can answer questions, predict future outcomes and solve organizational problems.

By David Weedmark11 min read

Data Science

Bringing Machine Learning to Agriculture

At The Climate Corporation, we aim to help farmers better understand their operations and make better decisions to increase their crop yields in a sustainable way. We’ve developed a model-driven software platform, called Climate FieldView™, that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. FieldView™ can incorporate grower-specific data, such as historical harvest data and operational data streaming in from special devices, including (our FieldView Drive) that are installed in tractors, combines, and other farming equipment. It incorporates public and third-party data sets, such as weather, soil, satellite, elevation data and proprietary data, such as genetic information of seed hybrids that we acquire from our parent company, Bayer.

By Jeff Melching10 min read

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