O'Reilly on Data

DECEMBER 10, 2019

Roughly a year ago, we wrote “ What machine learning means for software development.” Karpathy suggests something radically different: with machine learning, we can stop thinking of programming as writing a step of instructions in a programming language like C or Java or Python. Instead, we can program by example.

Software 335

Software Machine Learning Risk Data-driven 335

KDnuggets

NOVEMBER 4, 2021

Toloka is a crowdsourced data labeling platform that handles data collection and annotation projects for machine learning at any scale. In this Nov 11 Live Demo, Learn how to get reliable training data for machine learning.

Machine Learning 127

Machine Learning Data Collection 127

Smart Data Collective

FEBRUARY 28, 2023

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. Machine learning technology has already had a huge impact on our lives in many ways. There are numerous ways that machine learning technology is changing the financial industry. What is risk parity?

Machine Learning 117

Machine Learning Risk Cost-Benefit Marketing 117

Domino Data Lab

MAY 15, 2019

Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Product Management for Machine Learning.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

Smart Data Collective

APRIL 10, 2020

One study found that 53% of marketers plan to use machine learning in some capacity. At Smart Data Collective, we have discussed many of the ways that AI and machine learning have changed the face of performance marketing. Machine learning is changing the way that companies position their brand image.

Machine Learning 109

Machine Learning Testing Marketing Strategy 109

O'Reilly on Data

NOVEMBER 13, 2019

Questions of ethics and what role it should play are increasingly arising in machine learning and AI research, especially in the area of science applications.

Machine Learning 277

Machine Learning Data Collection Modeling IT 277

CIO Business Intelligence

OCTOBER 4, 2022

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.

Machine Learning 118

Machine Learning Data Architecture Uncertainty Data-driven 118

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. 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.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Rocket-Powered Data Science

MARCH 19, 2021

The determination of winners and losers in the data analytics space is a much more dynamic proposition than it ever has been. One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. A lot has changed in those five years, and so has the data landscape.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager.

Management 364

Management Experimentation B2B Machine Learning 364

Cloudera

DECEMBER 8, 2020

Two years ago we wrote a research report about Federated Learning. You can read it online here: Federated Learning. Federated Learning is a paradigm in which machine learning models are trained on decentralized data. First, it makes data privacy easier.

Machine Learning 85

Machine Learning Data Lake Reporting Data Collection 85

KDnuggets

JANUARY 25, 2023

ChatGPT as a Python Programming Assistant • How to Use Python and Machine Learning to Predict Football Match Winners • 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project • 5 Free Data Science Books You Must Read in 2023

Machine Learning 112

Machine Learning Data Science Data Collection Modeling 112

Smart Data Collective

OCTOBER 30, 2019

Machine learning is changing the future of marketing in many beneficial ways. There are a number of tactics that marketers can pursue to optimize campaigns with machine learning algorithms. Use artificial intelligence tools that rely on machine learning to optimize content for different visitors.

Machine Learning 93

Machine Learning Advertising Big Data Marketing 93

Smart Data Collective

JUNE 20, 2019

. My colleagues and I at Smart Data Collective have written extensively about the benefits of big data in fields like marketing, hospitality and cybersecurity. We sometimes realize that we need to discuss the implications of big data for other fields as well. How does machine learning influence technical writing?

Machine Learning 88

Machine Learning Big Data Statistics Data Collection 88

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. Alex Ratner on “Creating large training data sets quickly”.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

CIO Business Intelligence

DECEMBER 20, 2024

Data architecture components A modern data architecture consists of the following components, according to IT consulting firm BMC : Data pipelines. A data pipeline is the process in which data is collected, moved, and refined. It includes data collection, refinement, storage, analysis, and delivery.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Rocket-Powered Data Science

JUNE 17, 2022

Here is a list of my top moments, learnings, and musings from this year’s Splunk.conf : Observability for Unified Security with AI (Artificial Intelligence) and Machine Learning on the Splunk platform empowers enterprises to operationalize data for use-case-specific functionality across shared datasets.

Machine Learning 238

Machine Learning Recreation/Entertainment Risk Business Objectives 238

Analytics Vidhya

MAY 29, 2023

The two pillars of data analytics include data mining and warehousing. They are essential for data collection, management, storage, and analysis. Both are associated with data usage but differ from each other.

Data mining 308

Data mining Data Collection Strategy Data Analytics 308

CIO Business Intelligence

FEBRUARY 20, 2025

The data retention issue is a big challenge because internally collected data drives many AI initiatives, Klingbeil says. With updated data collection capabilities, companies could find a treasure trove of data that their AI projects could feed on. of their IT budgets on tech debt at that time.

Insurance 108

Insurance Cost-Benefit Unstructured Data Data Lake 108

Data Science and Beyond

JANUARY 23, 2016

Many modern data scientists don’t get to experience data collection in the offline world. Recently, I spent a month sailing down the northern Great Barrier Reef, collecting data for the Reef Life Survey project.

Data Collection 52

Data Collection Machine Learning 52

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Data Collection 167

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Examples: (1-3) All those applications shown in the definition of Machine Learning. (4)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Some machine learning approaches (and many software engineering practices) are simply not appropriate for near-real time applications. Agreeing on metrics. arbitrary stemming, stop word removal.).

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

JANUARY 28, 2019

The problems with consent to data collection are much deeper. It comes from medicine and the social sciences, in which consenting to data collection and to being a research subject has a substantial history. We really don't know how that data is used, or might be used, or could be used in the future.

Insurance 247

Insurance Metadata Data Collection Marketing 247

Smart Data Collective

APRIL 5, 2022

They are using tools like Amazon SageMaker to take advantage of more powerful machine learning capabilities. Amazon SageMaker is a hardware accelerator platform that uses cloud-based machine learning technology. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Machine Learning 145

Machine Learning Cost-Benefit Data Science Unstructured Data 145

Smart Data Collective

AUGUST 11, 2022

Sustaining the responsible use of machines. Human labeling and data labeling are however important aspects of the AI function as they help to identify and convert raw data into a more meaningful form for AI and machine learning to learn. However, Ai uses algorithms that can screen and handle large data sets.

Data Quality 139

Data Quality Machine Learning Digital Transformation Big Data 139

TDAN

AUGUST 17, 2021

If you are planning on using predictive algorithms, such as machine learning or data mining, in your business, then you should be aware that the amount of data collected can grow exponentially over time.

Predictive Modeling 115

Predictive Modeling Modeling Data mining Big Data 115

Occam's Razor

MARCH 30, 2017

Machine Learning | Marketing. Machine Learning | Analytics. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Smart Data Collective

APRIL 8, 2020

Data warehouse, also known as a decision support database, refers to a central repository, which holds information derived from one or more data sources, such as transactional systems and relational databases. The data collected in the system may in the form of unstructured, semi-structured, or structured data.

Technology 144

Technology Data Warehouse Big Data Machine Learning 144

CIO Business Intelligence

APRIL 12, 2024

These roles include data scientist, machine learning engineer, software engineer, research scientist, full-stack developer, deep learning engineer, software architect, and field programmable gate array (FPGA) engineer.

Deep Learning 124

Deep Learning IT Machine Learning IoT 124

Smart Data Collective

MARCH 29, 2021

Asset data collection. Data has become a crucial organizational asset. Companies need to make the most out of their data resources, which includes collecting and processing them correctly. Data collection and processing methods are predicted to optimize the allocation of various resources for MRO functions.

Big Data 140

Big Data Management Cost-Benefit Internet of Things 140

Cloudera

FEBRUARY 8, 2021

In addition, moving outside the vehicle, existing fragmented approaches for data management associated with the machine learning lifecycle are limiting the ability to deploy new use cases at scale. The vehicle-to-cloud solution driving advanced use cases.

Machine Learning 108

Machine Learning Manufacturing Unstructured Data Data Collection 108

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. Top 15 data science bootcamps. Data Science Dojo. Data Science Dojo.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

CIO Business Intelligence

MARCH 30, 2022

For organizations looking to move beyond stale reports, decision intelligence holds promise, giving them the ability to process large amounts of data with a sophisticated mix of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision support platforms.

Machine Learning 144

Machine Learning Testing Risk Data Collection 144

Smart Data Collective

APRIL 22, 2020

This moment in history is unlike any other — and the value of data in ending it resembles nothing we’ve yet seen. Combining the coronavirus and big data may prove just how valuable artificial intelligence and other major technologies can be. To do this, scientists train neural networks using vast databases of existing DTI data.

Big Data 140

Big Data Machine Learning Risk Interactive 140