IBM Big Data Hub

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. appeared first on IBM Blog.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

DataKitchen

APRIL 13, 2021

Testing and Data Observability. Process Analytics. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability.

Testing 307

Testing Machine Learning Consulting Data Quality 307

datapine

NOVEMBER 19, 2019

Visual analytics: Around three million images are uploaded to social media every single day. In business intelligence, we are evolving from static reports on what has already happened to proactive analytics with a live dashboard assisting businesses with more accurate reporting.

Technology 279

Technology Internet of Things IT IoT 279

Domino Data Lab

JULY 2, 2019

Scale the problem to handle complex data structures. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. That speaks to the remarkable learning curve aspects of SQL, how oh-so-much data munging can be performed without having to sweat the details.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105