Smart Data Collective

MAY 8, 2022

Let’s not forget that big data and AI can also automate about 80% of the physical work required from human beings, 70% of the data processing, and more than 60% of the data collection tasks. From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace.

Big Data 122

Big Data Data Analytics Analytics Statistics 122

Cloudera

NOVEMBER 19, 2020

The Public Sector data challenge. Robust online systems have streamlined interactions and generated a wealth of new data to support mission success and enhanced citizen engagements. However, this rapid scaling up of data across government agencies brings with it new challenges. Modernization has been a boon to government.

Machine Learning 119

Machine Learning Data-driven Modeling Deep Learning 119

IBM Big Data Hub

FEBRUARY 23, 2024

The emergence of NLG has dramatically improved the quality of automated customer service tools, making interactions more pleasant for users, and reducing reliance on human agents for routine inquiries. Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. billion by 2030.

Enterprise 95

Enterprise Interactive Data-driven Key Performance Indicator 95

Decision Management Solutions

FEBRUARY 8, 2021

The goal is to establish the Mayflower 400 as an open platform for marine research that would reduce costs and ease the burden on scientists and sailors, who have to brave a dangerous and unpredictable environment in the course of their data-collecting missions. We’re finding that it doesn’t have to be completely autonomous.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

DataRobot

OCTOBER 20, 2021

Achieving that level of governance at scale requires a common understanding of AI and data concepts. Individuals interacting with AI systems should possess a baseline data literacy, especially in high-risk use cases that require human collaboration at the final decision-making stage.

Machine Learning 52

Machine Learning Metrics Deep Learning Risk 52

Ontotext

DECEMBER 15, 2023

The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu. By exploring the first topic machine-learning , we find 117k Github repos. The taxonomy integrates various data sources, offering a holistic view of AI innovation.

Machine Learning 69

Machine Learning Deep Learning Interactive Statistics 69

IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 78

Marketing Technology Advertising Machine Learning 78

DataRobot Blog

JULY 27, 2022

Here we will demonstrate an end-to-end unattended workflow that: trains a new model on the Fashion MNIST Dataset uploads it to an Algorithmia Data Collection creates a new algorithm on Algorithmia creates DataRobot deployment Links everything together via the MLOps Agent. The Demo: Autoscaling with MLOps.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

Domino Data Lab

JUNE 23, 2019

This talk will describe how you can navigate all these challenges that you’re going to face and build a business where every product interaction benefits from your investment in machine learning. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. Prototypes and Data Product MVPs.

Marketing 364

Marketing Experimentation Metrics Testing 364

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109