Domino Data Lab

JUNE 23, 2019

Pete Skomoroch presented “ Product Management for AI ” at Rev. Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Session Summary. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Figure 1 illustrates the framework for Scope 3 emission estimation employing a large language model.

Experimentation 81

Experimentation Modeling Deep Learning Data-driven 81

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

MARCH 21, 2022

A data scientist’s approach to data analysis depends on their industry and the specific needs of the business or department they are working for. Before a data scientist can find meaning in structured or unstructured data, business leaders and department managers must communicate what they’re looking for.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

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 95

Advertising Recreation/Entertainment Experimentation Enterprise 95

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

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

Insight

MARCH 12, 2020

Much like the challenge faced by hiring managers, literary agents can receive thousands of pitches every year, and there is no easy way to triage them. 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.

Modeling 59

Modeling Metadata Publishing Sales 59

O'Reilly on Data

MAY 14, 2020

In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager. In this article, we shift our focus to the AI Product Manager’s skill set, as it is applied to day to day work in the design, development, and maintenance of AI products. The AI Product Pipeline.

Management 363

Management Experimentation B2B Machine Learning 363

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

AWS Big Data

JULY 3, 2023

Index rebalancing arbitrage takes advantage of short-term price discrepancies resulting from ETF managers’ efforts to minimize index tracking error. 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. Create an EMR cluster.

Snapshot 88

Snapshot Data Lake Testing Strategy 88

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. Is it actionable?

Experimentation 61

Experimentation Testing Statistics Metrics 61

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

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

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. Managing Through Socio-Economic Disruption. In fact, 87% of organizations struggle with long deployment timelines.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

Cloudera

JUNE 6, 2019

As the user and workload count in the shared cluster increases, cluster admins tend to become increasingly sophisticated in leveraging various resource management & multi-tenancy features that we offer within a cluster to maintain order. Cloudera Manager (CM) 6.2 A Better Approach: Virtual Private Clusters.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

DECEMBER 12, 2016

But first, let me do something a bit more expansive and share all the key elements necessary to ensuring an analytics existence that can help create a high-impact data-influenced organization. Google Tag Manager. You live or die by the data you collect, and the quality of that data. Management. Qubit is one.

Analytics 141

Analytics Data-driven Strategy Consulting 141

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. If you are starting new, or going to touch the code on your website for any reason, please, in the name of Thor, Superman and Dilbert, use a tag management solution.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. When it comes to proving which campaigns are better and which numbers to report to the management what will you do? Likely not. Omniture cannot save you. Only you can save yourself.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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. Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data. The results?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

OCTOBER 19, 2010

But overinvest in people and everything that is required to make those people successful: invest in process, invest in their training, invest in large monitors for them, invest in backing them up against senior management, invest in involving them in key business strategy meetings, invest in… you catch my drift). Three tools.

Analytics 135

Analytics Testing Measurement Optimization 135

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

That is also a way to get people to sync the data analysis (not data puking, sorry I meant data reporting) with the speed at which the company actually makes decisions (data > analyst > manager > director > VP > question back to manager > yells at the analyst > back to director> VP = 6 days).

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. With that context, this post is all about career management in the digital space. This reality powers my impostor syndrome, and (yet?) It is now all on you.

Marketing 136

Marketing Analytics Machine Learning Strategy 136