Increasing the accuracy of analytics technologies drives modern business, and getting more realistic results applicable to on-the-ground business operational conditions means orgs can make better decisions faster.
Serverless ML, ML on AWS Lambda, ML on Google Cloud Functions, Scalable Serverless ML, Classify your dog for less than a penny!
Netflix open-sourced Metaflow for performing data science and machine learning on cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud (GCP) - although optimized for AWS. What features does it provide?
Data products are productized versions of data science and machine learning initiatives that deliver value to end-users.
Graph machine learning (graphML) is a subset of deep learning with much higher accuracy because big data records are linked together by their relationships.
#NLP #MachineLearning #algorithm learns to tell #stories by summarizing #commercial #RealEstate #data, #earning #profits and spurring #CustomerRetention. #BINGO!
Deep-learning convolutional generative adversarial neural network crushes web design.
Graphs and graph datasets are rich data structures that can be used uniquely to improve the accuracy and effectiveness of machine learning workflows. Some of the key interactions are graph analytics as features, semi supervised learning, graph based deep learning, and machine learning approaches to hard graph problems.
Do you remember the first time you saw a commercial about “the Cloud?” That was one of the pivotal moments for technology buzzwords going mainstream. It’s been a nonstop thrill ride since then: Web 2.0. Internet of Things. Big Data. Machine Learning. Like “the Cloud,” the term “machine learning” is thrown around a lot, but it’s not entirely clear who it is useful to. People who follow it are aware that machine learning techniques were used by Google to create an unstoppable Go playing machine, and that it allows computers to drive with abilities getting closer to human drivers by the day.
“Expero was fundamental to the success on this project, effectively communicating their progress throughout and helping our scientists develop an understanding of deep learning techniques.”
Knowing who has the flu is hard. Doctors assess the small minority of sick people who make it into a clinic and send the test results to the CDC every few weeks. The CDC models this information and retroactively corrects their national influenza like illness (ILI) report every two weeks.