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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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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. One of the keys for our success was really focusing that effort on what our key business initiatives were and what sorts of metrics mattered most to our customers.

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Six keys to achieving advanced container monitoring

IBM Big Data Hub

Containers have increased in popularity and adoption ever since the release of Docker in 2013, an open-source platform for building, deploying and managing containerized applications. Containerization helps DevOps teams avoid the complications that arise when moving software from testing to production.

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Overcoming Common Challenges in Natural Language Processing

Sisense

While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Using the semantic meaning of words it already knows as a base, the model can understand the meanings of words it doesn’t know that appear in test data.

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How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. A 2013 survey conducted by the IBM’s Institute of Business Value and the University of Oxford showed that 71% of the financial service firms had already adopted analytics and big data.

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

AWS Big Data

Each service implements k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) algorithms and distance metrics to calculate similarity. 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. Waiting for connections.