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    7 Categories for Evaluating MLOps Vendors [Free Worksheet]

    Evaluating machine learning operations (MLOps) vendors can be daunting. With so many providers claiming similar capabilities, it’s hard to figure out...
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    Model Remediation: The Solution to The Day 3 Problem of Model Drift

    Model failure is a huge concern for any AI-driven organization. The phenomenon that causes models to stop working in production—known as model drift,...
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    What Is AI Model Drift?

    In our recent post on model retraining, we touch on an unfortunate but unavoidable fact of machine learning (ML) models: They often have remarkably...
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    How to Choose an MLOps Vendor

    Once you’ve settled the “build vs. buy” debate, your research enters a new phase: how to choose a machine learning operations (MLOps) vendor. That...
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    Eric Korman Explains Valor and Its Step Change for Model Evaluation

    Eric Korman is the Chief Science Officer at Striveworks. He leads our Research and Development Team, which recently released Valor—our...
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    What Is Data Lineage?

    Data lineage refers to the full history of data points and actions taken on them throughout a machine learning (ML) workflow. By inspecting a...
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    Understanding Performance Bias With the Valor Model Evaluation Service

    Machine learning benchmarks like ImageNet, COCO, and LLM Leaderboard usually target a single metric, such as accuracy for classification tasks or...
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    To Build or Buy an MLOps Platform?

    It’s the oldest debate in business (or at least the oldest since Silicon Valley invented software-as-a-service). “If we need software, should we buy...
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    Striveworks President Paul Brinkley: MLOps and Catching the Right Wave

    Paul Brinkley has had a diverse career. From doctoral studies on neural networks in the ’90s to senior roles with communications companies,...
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    Striveworks secures patent for innovative data lineage process

    Striveworks Secures Patent for Innovative Data Lineage Process

    The new process grants greater transparency into machine learning workflows and allows for better data governance. Originally published on January 4,...
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    Navigating AI’s ‘Messy Middle’ in 2024

    With all due apologies to our future president, Taylor Swift, 2023 was the year of AI. The excitement around generative AI crescendoed, seeing vastly...
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    Demystifying CV: The Power of Convolution in Neural Networks

    Computer vision is an innovative field of research that aims to provide computers with an understanding of digital imagery, often through artificial...
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    Understanding Neural Networks and the Training Process

    Training a neural network involves a lot of mathematics, including linear algebra and multivariate calculus, and a lot of computation. The purpose of...
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    Two classifiers being combined into a single classifier. In general, the decision boundaries of the two classifiers cannot merely be superimposed; instead new boundaries must be learned that respect both tasks.

    Sequential Learning through Knowledge Distillation

    In the last blog post, we saw that neural networks can be simplified (or pruned) to alleviate their complexity and cost for computation. Often small...
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    Pruning ResNets for Fun and Profit

    What are Neural Networks? Neural networks are highly parameterized functions which can be trained to fit/represent/model data. Parameterized models...
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