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DataKitchen’s 2020 Honors & Awards

DataKitchen

In June of 2020, Database Trends & Applications featured DataKitchen’s end-to-end DataOps platform for its ability to coordinate data teams, tools, and environments in the entire data analytics organization with features such as meta-orchestration , automated testing and monitoring , and continuous deployment : DataKitchen [link].

Testing 241
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. e-handbook of statistical methods: Summary tables of useful fractional factorial designs , 2018 [3] Ulrike Groemping.

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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Because ML models can react in very surprising ways to data they’ve never seen before, it’s safest to test all of your ML models with sensitivity analysis. [9] If so, have fun debugging! [1]

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

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Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. from imblearn.over_sampling import SMOTE.

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Using DataOps to Drive Agility and Business Value

DataKitchen

Chapin shared that even though GE had embraced agile practices since 2013, the company still struggled with massive amounts of legacy systems. Don’t just run out and just buy a fancy new tool or hire that genius person who’s going to do everything.”. Success Requires Focus on Business Outcomes, Benchmarking.

ROI 211
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Build a RAG data ingestion pipeline for large-scale ML workloads

AWS Big Data

Ray cluster for ingestion and creating vector embeddings In our testing, we found that the GPUs make the biggest impact to performance when creating the embeddings. You will see the Ray dashboard and statistics of the jobs and cluster running. He entered the big data space in 2013 and continues to explore that area.