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

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Conversations suggest that AI is already transforming most major industries.

Advertising 102

Advertising Recreation/Entertainment Experimentation Enterprise 102

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

CIO Business Intelligence

MAY 31, 2022

These new, digitally enhanced worlds, realities, and business models are poised to revolutionize both life and enterprise in the next decade, as explored in Accenture’s recent Technology Vision 2022 report. Here are five implications these technologies will have on security and privacy as we build our collective future. .

Technology 116

Technology Experimentation Interactive IoT 116

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. finished his first novel, Cuckoo’s Calling. labels).

Modeling 59

Modeling Metadata Publishing Sales 59

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. See [link]. Industry 4.0 2) Connected cars. (3)

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Data Science and Beyond

APRIL 5, 2021

The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Instead, many companies have switched to a locally-remote model, hiring remotely within the same country or timezone region. Remote work. Only time will tell. Sustainability.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Dataiku

APRIL 22, 2022

It’s no secret that training AI models is an energy-intensive and large dataset-dependent endeavor and, to corroborate this, researchers at the University of Massachusetts Amherst performed a lifecycle assessment on large, widely accepted AI models trained on the vast datasets needed to achieve accuracy. It's All in a Name.

Experimentation 59

Experimentation Modeling Business Objectives Manufacturing 59

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Collecting additional data is not always possible and can be expensive.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

O'Reilly on Data

MAY 14, 2020

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

Management 363

Management Experimentation B2B Machine Learning 363

Occam's Razor

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. 2) MLOps became the expected norm in machine learning and data science projects.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure. That is the solution.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

DataRobot Blog

OCTOBER 4, 2022

This has prompted AI/ML model owners to retrain their legacy models using data from the post-COVID era, while adapting to continually fluctuating market trends and thinking creatively about forecasting. However, hand-coding, testing, evaluating and deploying highly accurate models is a tedious and time-consuming process.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. Amazon Simple Storage Service (Amazon S3) is a popular cloud-based object storage service that can be used as the foundation for building a data lake.

Snapshot 80

Snapshot Data Lake Testing Strategy 80

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. You'll be surprised, there's only one tool in that mix. It is not even an analytics tool. " That lead to this post.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Cloudera

JUNE 6, 2019

Over time, workloads start processing more data, tenants start onboarding more workloads, and administrators (admins) start onboarding more tenants. While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake.

Metadata 88

Metadata Data Lake Optimization Strategy 88

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Mobile devices (phones, tablets, wearables) are transforming how we behave, how we buy, how we consume content, and dare I say how we become happy or we become sad.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Occam's Razor

MARCH 23, 2010

Javascript tag driven click data processed in the cloud provided through a web based front end that allows you to segment and create meaningful views of the data unique to you. There are more mistruths and F U D about Web analytics out there than I think is reasonable. Part of it fueled by Vendors. What a competitive bunch!

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

MAY 27, 2015

In a recent set of keynotes and consulting engagements in the US, UK and Canada, I've had an overwhelming feeling that in very fundamental ways some companies make imprecise choices when it comes to their digital strategy. Not because they don't have enough money or opportunity or people. Let's go! Can you do RM+RP+RT using TV?

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

OCTOBER 19, 2010

An expansive view of what it means to use data online, both from the type of data perspective and the kind of desired impact perspective. They'll simply puke data faster and, if you implement them right, more efficiently. What is the first thing you want when you think about web analytics? Of course tools. spectrum.

Analytics 135

Analytics Testing Measurement Optimization 135

datapine

JANUARY 31, 2022

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. Let’s see it with a real-world example.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation. We ought not dredge our data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. The organization functions off a clearly defined Digital Marketing & Measurement Model. #1. A part of reason for this mis-match in value delivered is that there is a natural evolution that needs to occur.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. Many used some data, but they unfortunately used silly data strategies/metrics. Real-time data is life changing. It seems absolutely stupid to say, " No, I don't want real-time data. "

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Analytical thinking is a collection of skills. They never had to worry that they have to be in a persistent forward motion… sometimes just to stay current. This is normal.

Marketing 136

Marketing Analytics Machine Learning Strategy 136