Rocket-Powered Data Science

JULY 6, 2021

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

CIO Business Intelligence

DECEMBER 19, 2024

Skills in Python, R, TensorFlow, and Apache Spark enable professionals to build predictive models for energy usage, optimize resource allocation, and analyze environmental impacts. This is where machine learning algorithms become indispensable for tasks such as predicting energy loads or modeling climate patterns.

IT 52

IT IoT Machine Learning Optimization 52

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. If anything, this focus has shifted to the ML or predictive model.

IoT 24

IoT Big Data Data Warehouse Uncertainty 24

AWS Big Data

NOVEMBER 14, 2023

With a combination of low-latency data streaming and analytics, they are able to understand and personalize the user experience via a seamlessly integrated, self-reliant system for experimentation and automated feedback. The probability results are also stored in Amazon S3 to continuously retrain the model in SageMaker.

IoT 59

IoT Data-driven Data Lake Data Strategy 59