Machine Learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders or malicious insiders working towards a data breach, optical character recognition (OCR), learning to rank, and computer vision.
Source: https://en.wikipedia.org/wiki/Machine_learning
Position on the Adoption Curve
Presentations about Machine Learning
Deep Learning for Application Performance Optimization
Engineering Systems for Real-Time Predictions @DoorDash
Probabilistic Programming from Scratch
From Software Development to ML - A Team's Transformation
Machine Learning Open Space
How Machines Help Humans Root Cause Issues @Netflix
ML Data Pipelines for Real-Time Fraud Prevention @PayPal
Software Is Eating the World, ML Is Going to Eat Software
Python-Based AI Workflows - From Notebook to Production Scale
Interviews
Engineering Systems for Real-Time Predictions @DoorDash
QCon: Can you describe the machine learning platform you have leverage at DoorDash?
Raghav: We built our system around common machine learning open source libraries in Python like SciKit-Learn, LightGBM, and Keras. We have a microservices architecture also built in Python which includes a prediction service that handles all the predictions and a features service. All the services are hosted on AWS.
QCon: Can you briefly describe your real-time prediction system?
Raghav: Our Prediction system responds to HTTP/RPC requests, it accesses a model store to fetch the right model to use and obtains features from a features service.