Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. This integration is key in assuring that models evolve with the data – to avoid, for example, model drift. model packaging, deployment and serving. model monitoring.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers.

Experimentation 103

Experimentation Testing Optimization Measurement 103

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Impact Matrix.

Analytics 93

Analytics Metrics Strategy Measurement 93

CIO Business Intelligence

MARCH 21, 2022

As such, a data scientist must have enough business domain expertise to translate company or departmental goals into data-based deliverables such as prediction engines, pattern detection analysis, optimization algorithms, and the like. It doesn’t conform to a data model but does have associated metadata that can be used to group it.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

Occam's Razor

OCTOBER 21, 2013

As an analyst, I was upset that this change would hurt my ability to analyze the effectiveness of my beloved search engine optimization (SEO) efforts – which are really all about finding the right users using optimal content strategies. I've gone through the five stages in the Kubler-Ross model. silverlinings.

Optimization 154

Optimization Reporting Experimentation Metrics 154

Occam's Razor

MARCH 23, 2021

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). The benchmark for the beautiful metric AVOC is 15.3%. You have the start of a fabulous in-flight optimization engine.

Analytics 131

Analytics Scorecard Strategy Testing 131

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous DataOps metrics testing checks data’s validity, completeness and integrity at input and output. Just-in-Time” manufacturing increases production while optimizing resources.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. Capabilities Beyond Classic Jupyter for End-to-end Experimentation. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Occam's Razor

AUGUST 22, 2011

" ~ Web Metrics: "What is a KPI? " + Standard Metrics Revisited Series. Key To Your Digital Success: Web Analytics Measurement Model. Web Data Quality: A 6 Step Process To Evolve Your Mental Model. "Engagement" Is Not A Metric, It's An Excuse. Experimentation and Testing: A Primer.

KPI 124

KPI Metrics Measurement ROI 124

CIO Business Intelligence

JUNE 27, 2022

Tyson: You’re coming up to two years since you added Sentry’s ability to monitor for performance which has some pretty fine-grained metrics in terms of identifying where bottlenecks are located in code. A lot of the current approaches feel very experimental and are tough to see as maintainable, so there’s certainly still room for growth here.

Software 111

Software Experimentation Testing Marketing 111

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

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively. Pilots can offer value beyond just experimentation, of course.

Cost-Benefit 143

Cost-Benefit Metrics Insurance Measurement 143

DataRobot Blog

SEPTEMBER 30, 2022

How do you track the integrity of a machine learning model in production? Model Observability can help. By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability Features.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

Occam's Razor

AUGUST 12, 2013

than multi-channel attribution modeling. By the time you are done with this post you'll have complete knowledge of what's ugly and bad when it comes to attribution modeling. You'll know how to use the good model, even if it is far from perfect. Multi-Channel Attribution Models. Linear Attribution Model.

Modeling 161

Modeling Optimization Marketing Interactive 161

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Remember that digital transformation is about transforming your business and operating models with technology.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. It should be noted that inverse probability weighting is not generally optimal (i.e., This is often referred to as the positivity assumption. the curse of dimensionality).

Statistics 44

Statistics Optimization Modeling Experimentation 44

Occam's Razor

DECEMBER 9, 2009

some reports going out on schedule (even if data pukes) then an optimal path might be to centralize some where (see item #2 below). [B] Which physical organizational model will work best for you? My organization redesign plans have recommended either one of the three models. Ok, only if it's legal where you live. ;)].

Analytics 95

Analytics Data-driven Marketing Sales 95

Occam's Razor

SEPTEMBER 15, 2014

Company UX leaders are happy to stink less by taking the sub-optimal path of responsive design, rather than create a mobile-unique experience (your customers tend to do different things on your desktop site than your mobile site!). Media-Mix Modeling/Experimentation. Many reasons. CEOs still don't get it.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

To not have it as an active part of your marketing portfolio is sub-optimal. The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. Just to ensure you are executing against your right mental model. You must use metrics that are unique to the medium.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

datapine

FEBRUARY 4, 2020

Structure your metrics. As with any report you might need to create, structuring and implementing metrics that will tell an interesting and educational data-story is crucial in our digital age. That way you can choose the best possible metrics for your case. Regularly monitor your data. 1) Marketing CMO report.

Reporting 280

Reporting Marketing Advertising Metrics 280

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . A complete DataOps program will have a unified, system-wide view of process metrics using a common data store.

Testing 300

Testing Machine Learning Consulting Data Quality 300

CIO Business Intelligence

MARCH 5, 2024

Many other platforms, such as Coveo’s Relative Generative Answering , Quickbase AI , and LaunchDarkly’s Product Experimentation , have embedded virtual assistant capabilities but don’t brand them copilots. As copilot technology capabilities are changing rapidly, leaders should frequently identify metrics and evaluate strategies.

Cost-Benefit 123

Cost-Benefit Advertising Experimentation Software 123

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. That means: All of these metrics are off.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

The Unofficial Google Data Science Blog

APRIL 16, 2019

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Such decisions involve an actual hypothesis test on specific metrics (e.g.

Experimentation 68

Experimentation Sales Metrics Measurement 68

AWS Big Data

JULY 20, 2023

Amazon SageMaker is used to build, train, and deploy a range of ML models. Additionally, SageMaker training jobs are employed for training the models. After the models are trained, they are deployed and used to identify anomalies and alert customers in real time to potential security threats.

Data Lake 90

Data Lake Analytics Snapshot Optimization 90

AWS Big Data

NOVEMBER 14, 2023

With data streaming, you can power data lakes running on Amazon Simple Storage Service (Amazon S3), enrich customer experiences via personalization, improve operational efficiency with predictive maintenance of machinery in your factories, and achieve better insights with more accurate machine learning (ML) models.

IoT 56

IoT Data-driven Data Lake Data Strategy 56

Occam's Razor

DECEMBER 12, 2016

Digital Marketing & Measurement Model. My solution to this, incredibly real and frustrating problem, is to insist on seeing the signed in blood version of the company's Digital Marketing & Measurement Model. What one critical metric will help you clearly measure performance for each strategy above? That's it.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Ensure a culture that supports a steady process of learning and experimentation. Problems with training ML models efficiently.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Corinium

APRIL 25, 2019

Life insurance needs accurate data on consumer health, age and other metrics of risk. All assets need to be optimally leveraged for maximum business value while also being protected from misuse, whether there was malicious intent or not, and this needs to be the responsibility of whomever is responsible for that asset in the company.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

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

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. The vector index supports up to 1,000 fields.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

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. This model increases predictability without creating undesirable data silos. This approach seeks to optimize resource utilization or infrastructure efficiency.

Metadata 85

Metadata Data Lake Optimization Strategy 85