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 187

Machine Learning Modeling Testing Risk Management 187

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.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

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 241

Testing Big Data Statistics Manufacturing 241

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 59

Modeling Metadata Publishing Sales 59

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 52

Experimentation Optimization Uncertainty Metrics 52

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 69

Statistics Machine Learning Modeling Metrics 69

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 115

Data Processing Dashboards Machine Learning Management 115