Learn Docker, Docker Compose, Multi-Container Projects, Deployment and all about Kubernetes from the ground up!
ML Ops (or MLOps) is a set of practices uniting Machine Learning, DevOps and Data Engineering, aiming to deploy and maintain ML systems reliably and efficiently.
MLOps is influential in the digital transformation world while handling Machine Learning Engineering projects in large scale implementations.
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MLOps will help you to understand how to build the Continuous Integration and Continuous Delivery pipeline for a ML/AI project
4+ Hours of Video Instruction With both machine learning and DevOps at the forefront these days, Milecia McGregor helps engineers understand how to apply key DevOps principles to their machine learning projects. When teams are working with machine learning models, changing features, different data sets, new algorithms, and unique computing resources all influence a machine learning models performance. Tracking all of these items can be complicated.
Understanding the Basics of MLOps (Machine Learning Operations).MLOps is a set of practices that combine Machine Learning, DevOps
Download this free how-to reference guide on machine learning contains everything you need — including code samples and notebooks — so you can get to work.
Machine learning is a powerful way to forecast Time Series. Feature Engineering is critical. A new innovation is coming in timetk - to help generate 200+ time-series features.
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Machine Learning (Theory)
Discover how AI and Machine Learning are revolutionizing database engineering, optimizing data management and transforming the field.
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Bayesian Reasoning and Machine Learning, by David Barber We like the bright pink cover!
A short summary on Machine Learning Model Convergence and Divergence
Today, we will have a look at How to get started with Deep Learning, we will discuss different tools for studying deep learning i.e. TensorFlow, Django, Numpy etc.
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Theoretical computer scientist Ilias Diakonikolas has received a prestigious two-year grant award from the Alfred P. Sloan Foundation
The single entry point in platforms discourages the Machine Learning engineer community from building modular and reusable components.
