Web Services vs. Streaming for real-time machine learning endpoints
How Playtika determined the best architecture for delivering real-time ML streaming endpoints at scale By Avi Gabay, Director of Architecture at Playtika Machine learning (ML)
How Playtika determined the best architecture for delivering real-time ML streaming endpoints at scale By Avi Gabay, Director of Architecture at Playtika Machine learning (ML)
What is MLOps? Machine Learning Operations – commonly abbreviated as MLOps – is a methodological framework for collaboration and communication between data scientists and operations
CI/CD (Continuous Integration/Continuous Deployment) has long been a successful process for most software applications. The same can be done with Machine Learning applications, offering automated
The machine learning pipeline is the process data scientists follow to build machine learning models. Oftentimes, an inefficient machine learning pipeline can hurt the data
Continual learning is the ability of a model to learn continually from a stream of data. In practice, this means supporting the ability of a
Today’s business world is driven by data. Extracting meaning and insights from the vast amounts of data available. Enterprises rely on data to remain competitive
Machine Learning (ML) and Artificial Intelligence (AI) are spreading across various industries. With the rapid increase of volume as well as the complexity of data,
Use case to predict health conditions from Chest X-rays using deep learning.
5 simple Vim tips to save time on data science tasks. Learn more about this under rated text editor.
In the following tutorial, we will go over the process required to setup TensorFlow environment to deploy models.
In-house or not In-house? That is the question. Here are a few things to consider before making a decision.
What actions can you take today to become a more valuable data scientist? We’ve consolidated the great advice of our data science leaders into 10 actionable steps
