2013, Statistics and Testing - Data Leaders Brief

DataKitchen’s 2020 Honors & Awards

DataKitchen

DECEMBER 30, 2020

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

Testing Big Data Statistics Manufacturing

Why you should care about debugging machine learning models

O'Reilly on Data

DECEMBER 12, 2019

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]

Machine Learning

Machine Learning Modeling Testing Risk Management

Recap of Amazon Redshift key product announcements in 2024

AWS Big Data

DECEMBER 17, 2024

Amazon Redshift , launched in 2013, has undergone significant evolution since its inception, allowing customers to expand the horizons of data warehousing and SQL analytics. In internal tests, AI-driven scaling and optimizations showcased up to 10 times price-performance improvements for variable workloads.

Data Lake

Data Lake Data Warehouse Data-driven Optimization

Webinars

Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Using DataOps to Drive Agility and Business Value

DataKitchen

JUNE 24, 2021

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.

Metrics

Metrics ROI Measurement Cost-Benefit

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

AWS Big Data

NOVEMBER 29, 2023

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.

Data Warehouse

Data Warehouse Analytics Data Lake Machine Learning

Data Drift Detection for Image Classifiers

Domino Data Lab

DECEMBER 1, 2019

In such cases, methods from statistical process control and operations research that rely primarily on numerical data are hard to adopt and necessitates a new approach to monitoring models in production. Step 4: Generate the test, train and noisy MNIST data sets. x_test = x_test.astype('float32') / 255.

Machine Learning

Machine Learning Modeling Deep Learning Testing

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

Domino Data Lab

APRIL 21, 2021

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.

Statistics

Statistics Machine Learning Modeling Metrics

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

AWS Big Data

MARCH 13, 2024

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.

Data Processing

Data Processing Dashboards Machine Learning Metrics

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

APRIL 23, 2024

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.

Experimentation

Experimentation Optimization Uncertainty Metrics

Manipulating Data with dplyr

Domino Data Lab

MARCH 27, 2019

For example, you can calculate the average percentage of votes cast for Democratic Party candidates: # Compute summary statistics for the `presidentialElections` data frame average_votes <- summarize(. Using the summarize() function to calculate summary statistics for the presidentialElections data frame. Red notes are added.

Statistics

Statistics Data Science Visualization IT

Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

AUGUST 22, 2019

Although it’s not perfect, [Note: These are statistical approximations, of course!] Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. This test set is available at download.tensorflow.org/data/questions-words.txt.]. Example 11.6 Note: Mikolov, T.,

Deep Learning

Deep Learning Modeling Metrics Testing

Data Science at The New York Times

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. .”

Data Science

Data Science Machine Learning Advertising Modeling

The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

Insight

MARCH 12, 2020

In 2013, Robert Galbraith?—?an The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face. an aspiring author?—?finished

Modeling

Modeling Metadata Publishing Sales

What Is Embedded Analytics?

Jet Global

MAY 1, 2023

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. Advanced Analytics Some apps provide a unique value proposition through the development of advanced (and often proprietary) statistical models. Users’ varied needs require a shift in traditional BI thinking.

Analytics

Analytics Cost-Benefit Visualization Dashboards

Data Leaders Brief

DataKitchen’s 2020 Honors & Awards

Why you should care about debugging machine learning models

Webinars

Trending Sources

Recap of Amazon Redshift key product announcements in 2024

Webinars

Using DataOps to Drive Agility and Business Value

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

Data Drift Detection for Image Classifiers

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

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

Towards optimal experimentation in online systems

Manipulating Data with dplyr

Deep Learning Illustrated: Building Natural Language Processing Models

Data Science at The New York Times

The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

What Is Embedded Analytics?

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