IBM Big Data Hub

MARCH 4, 2024

Figure 2: ROI potential by transforming into an AI+ enterprise Organizations with high data maturity that embed an AI+ transformation model into the enterprise fabric and culture can generate up to 2.6 Consider the following: Do you need a public foundation model? times higher ROI. Should you build your own? If so, where will it run?

Enterprise 101

Enterprise ROI Risk Strategy 101

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 100

Advertising Recreation/Entertainment Experimentation Enterprise 100

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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

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. Features: DistilBERT Text Embeddings Once I had a raw dataset, I could begin engineering features and building a natural language processing (NLP) model.

Modeling 59

Modeling Metadata Publishing Sales 59

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. NCA doesn’t require the assumption of a specific compartmental model for either drug or metabolite; it is instead assumption-free and therefore easily automated [1]. Mean residence time. and many others.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. After adding the preferred code, teams can take advantage of the existing DataRobot capabilities, such as metrics, explainability, visualizations, deployment, monitoring, collaboration, and governance. So let’s dig in!

Machine Learning 52

Machine Learning Experimentation Modeling Visualization 52

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, this assumption no longer holds when samples interact with each other, such as in a network. Consider the case where experiment metrics are evaluated at the per-user level.

Testing 58

Testing Experimentation Measurement Modeling 58

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

MARCH 23, 2021

The other dimension to consider is most Analtyics teams kick into gear after the campaign is concluded, after the customer interaction has taken place in the call center, and after the funds budgeted have already been spent. The benchmark for the beautiful metric AVOC is 15.3%. More shouting is not really better – and it is expensive!

Analytics 131

Analytics Scorecard Strategy Testing 131

CIO Business Intelligence

JUNE 27, 2022

David Cramer: I love the open source community so I would build a lot of things in open source to interact with my peers. 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.

Software 111

Software Experimentation Testing Marketing 111

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

datapine

FEBRUARY 4, 2020

While your keyboard is burning and your fingers try to keep up with your brain and comprehend all the data you’re writing about, using an interactive online data visualization tool to set specific time parameters or goals you’ve been tracking can bring a lot of saved time and, consequently, a lot of saved money. Structure your metrics.

Reporting 280

Reporting Marketing Advertising Metrics 280

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Media-Mix Modeling/Experimentation. Mobile content consumption, behavior along key metrics (time, bounces etc.) It is easy to find.

Metrics 141

Metrics Reporting Analytics Marketing 141

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

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

Corinium

APRIL 25, 2019

Life insurance needs accurate data on consumer health, age and other metrics of risk. This is a much more proactive and scalable model. And it’s become a hyper-competitive business, so enhancing customer service through data is critical for maintaining customer loyalty. The 2nd biggest mistake, I’ll call it “silo mentality”.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

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 26, 2023

Using augmented ML search and generative AI with vector embeddings Organizations across all verticals are rapidly adopting generative AI for its ability to handle vast datasets, generate automated content, and provide interactive, human-like responses. The vector index supports up to 1,000 fields.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Domino Data Lab

JUNE 10, 2020

We’re introducing groundbreaking new features – including On-demand Spark clusters, enhanced project management, and the ability to export models – that give enterprises unprecedented power to scale their data science capabilities by addressing common struggles. I’m also proud to announce an exciting new product: Domino Model Monitor (DMM).

Data Science 82

Data Science Enterprise Experimentation Modeling 82

The Unofficial Google Data Science Blog

JULY 31, 2016

To figure this out, let's consider an appropriate experimental design. In other words, the teacher is our second kind of unit, the unit of experimentation. This type of experimental design is known as a group-randomized or cluster-randomized trial. The second source of dependence comes from student interactions.

Experimentation 42

Experimentation Testing Measurement Metrics 42

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

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

It turns out, they have some mathematical models for how this might play out, so they could do this in silico. They have a model based on past experiments which suggests that an improvement in recommendation Uptake Rate leads to greater engagement in the long term. The model may not hold well for such extrapolation.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. A Better Approach: Virtual Private Clusters.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

DECEMBER 12, 2016

AND, that if I have good ideas, they will get to market very quickly – making our engagement worth the current interaction and the continued success of new ideas after the engagement. Digital Marketing & Measurement Model. What one critical metric will help you clearly measure performance for each strategy above?

Analytics 141

Analytics Data-driven Strategy Consulting 141

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Domino Data Lab

AUGUST 26, 2019

Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters.

Testing 79

Testing Modeling Machine Learning Metrics 79

Occam's Razor

JULY 26, 2010

PALM: People Against Lonely Metrics]. So why not your metrics? This is the problem with lonely metrics. Why not find a BFF for your lonely metric and present something like this. I found a "you complete me" for my Visits metric, Bounce Rate. Or an actual outcome metric. 2: Join the PALM club.

Metrics 96

Metrics Measurement Dashboards Reporting 96

Occam's Razor

MARCH 23, 2010

Because every tool uses its own sweet metrics definitions, cookie rules, session start and end rules and so much more. If you don't kill 25% of your metrics each year, you are doing something wrong. Why do you think introducing a completely different set of numbers is going to make your life easier? Likely not. success measures.

Analytics 118

Analytics Consulting Metrics Experimentation 118

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. These quasi-explanations usually involve large, real effects and interactions so complex that arguments based on them are often non-falsifiable.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45