The MLOps Blog

MARCH 15, 2023

ML model explainability: Make sure the ML model is interpretable and understandable by the developers as well as other stakeholders and that the value addition provided can be easily quantified. .” If you aren’t aware already, let’s introduce the concept of ETL. Redshift, S3, and so on.

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Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. The second is drift.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Snorkel AI

JUNE 30, 2023

You have to make sure that your ETLs are locked down. And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. The second is drift.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Mlearning.ai

FEBRUARY 9, 2023

In industrial applications of Data Science, model complexity, model explainability, efficiency, and ease of deployment play a large role, even if that means you’re settling for a slightly less accurate model. Model explainability is an important skill for a Data Scientist’s job. This is even more common for first-time baseline models.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

AWS Machine Learning Blog

JANUARY 5, 2024

Figure 11 – Model monitor dashboard with selection prompts Figure 12 – Model monitor drift analysis Conclusion The implementation explained in this post enabled Wipro to effectively migrate their on-premises models to AWS and build a scalable, automated model development framework.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. There, you can use infographics, custom visualizations, and broader ways to explain your ideas. Data on its own is not sufficient for a cohesive story.

Data Scientist 52

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