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 105

Advertising Recreation/Entertainment Experimentation Enterprise 105

CIO Business Intelligence

MARCH 21, 2022

A data scientist’s chief responsibility is data analysis, which begins with data collection and ends with business decisions based on analytic results. The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

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. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

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

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]. 2) Connected cars. (3) 5) Industry 4.0.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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.

Experimentation 59

Experimentation Modeling Business Objectives Manufacturing 59

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. Only time will tell. Sustainability. Technical work.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Insight

MARCH 27, 2019

It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Not having enough labeled data often leads to overfitting, which means the model will not be able to generalize to unseen examples.

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. Another way to find the metric you want to change is to look at your business model. The business model also tells you what the metric should be.

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

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 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

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.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

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

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. 2018-06-21).

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

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

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

AWS Big Data

JULY 3, 2023

We use Iceberg tags to label market data snapshots to avoid look-ahead bias in the research data lake, which will enable us to test various trade entry and exit scenarios and assess the respective profitability of each. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15 Sell 2 (INVH, INVH) 2022-09-06 27419.797094 38.20

Snapshot 82

Snapshot Data Lake Testing Strategy 82

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! 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. Digital Marketing & Measurement Model. All that is great. That's it. Problematic, right? Management.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Cloudera

JUNE 6, 2019

This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. Furthermore, both Compute Cluster 1 and Compute Cluster 2 can operate on the same copy of the data with a single set of security policies. A Better Approach: Virtual Private Clusters.

Metadata 85

Metadata Data Lake Optimization Strategy 85

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. Media-Mix Modeling/Experimentation. I've covered the value of media-mix modeling (nee. I love media-mix modeling. Tag your mobile website.

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

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. If you don't have a robust experimentation program in your company you are going to die. Oh and when I say Experimentation I don't mean testing button sizes (BOO!). Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

MAY 27, 2015

It is an investment in numerous report writers or data (puking) automation or hiring a small army in India or Philippines to do that, before investing in any smart Analyst. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues.

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

OCTOBER 19, 2010

Move from a data collection obsession and develop a crush on data analysys. Experimentation and Testing Tools [The "Why" – Part 1]. Before you use any of these tools please please please read this blog post: The Definitive Guide To (8) Competitive Intelligence Data Sources ]. Three tools. Percent Mobile.

Analytics 135

Analytics Testing Measurement Optimization 135

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

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.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

The organization functions off a clearly defined Digital Marketing & Measurement Model. #1. Remember none of these jobs will do any data collection/IT work, even in medium-sized companies.) More on the Digital Marketing & Measurement Model, DMMM, in #2 below.). You know what your Return on Analytics is!

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

PS: The phrase "real-time data analysis" is an oxymoron. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. There is a lot of intent data on the web. Leverage the mental models of our day and age and you'll be immensely successful.

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. There is never a boring moment, there is never time when you can’t do something faster or smarter. This reality powers my impostor syndrome, and (yet?)

Marketing 136

Marketing Analytics Machine Learning Strategy 136