IBM Big Data Hub

MARCH 4, 2024

In 2024, companies confront significant disruption, requiring them to redefine labor productivity to prevent unrealized revenue, safeguard the software supply chain from attacks, and embed sustainability into operations to maintain competitiveness. Consider the following: Do you need a public foundation model? times higher ROI.

Enterprise 107

Enterprise ROI Risk Strategy 107

CIO Business Intelligence

APRIL 2, 2024

Joanne Friedman, PhD, CEO, and principal of smart manufacturing at Connektedminds, says orchestrating success in digital transformation requires a symphony of integration across disciplines : “CIOs face the challenge of harmonizing diverse disciplines like design thinking, product management, agile methodologies, and data science experimentation.

Management 119

Management Digital Transformation Testing Experimentation 119

Occam's Razor

AUGUST 12, 2013

than multi-channel attribution modeling. We have fought valiant battles, paid expensive consultants, purchased a crazy amount of software, and achieved an implementation high that is quickly, followed by a " gosh darn it where is my return on investment from all this? Multi-Channel Attribution Models. Grab a Red Bull.

Modeling 161

Modeling Optimization Marketing Interactive 161

datapine

FEBRUARY 4, 2020

Use professional software. To get started, you might want to equip yourself with a marketing BI software to analyze all your data and easily build professional reports. To get started, you might want to equip yourself with a marketing BI software to analyze all your data and easily build professional reports.

Reporting 280

Reporting Marketing Advertising Metrics 280

datapine

JANUARY 31, 2022

Your Chance: Want to try a professional BI analytics software? BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. They’re about having the mindset of an experimenter and being willing to let data guide a company’s decision-making process.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

O'Reilly on Data

APRIL 19, 2021

Relatively few respondents are using version control for data and models. Tools for versioning data and models are still immature, but they’re critical for making AI results reproducible and reliable. The biggest skills gaps were ML modelers and data scientists (52%), understanding business use cases (49%), and data engineering (42%).

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Sisense

JULY 1, 2021

Human thinking will revolve around creating and controlling models, since applying analytical judgment about how and when to use models is still a uniquely human ability. We know in marketing that one of the most powerful ideas is experimentation,” Scott told Sisense. Scott has dubbed this new breed “augmented marketers.”

Marketing 52

Marketing Experimentation Marketing Analytics ROI 52

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Cost-Benefit 91

Cost-Benefit Visualization Modeling Machine Learning 91

AWS Big Data

NOVEMBER 14, 2023

However, in many organizations, data is typically spread across a number of different systems such as software as a service (SaaS) applications, operational databases, and data warehouses. Streaming allows companies to connect the data available within data stores to large language models (LLMs) securely and in real time.

IoT 56

IoT Data-driven Data Lake Data Strategy 56

Sisense

MARCH 6, 2019

From our years in the BI space, we have come to learn that successful BI implementations take more than a solid piece of software. I was very happy to see that the first key message – “Data Driven” – was subtitled with “Adopt an Experimental Mindset”. in bold writing on the screen.

Data Analytics 75

Data Analytics Analytics Experimentation Data-driven 75

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. You’re changing things fundamentally in how you build and ship software.

Management 110

Management Machine Learning Experimentation Metrics 110

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

DataKitchen

OCTOBER 5, 2021

DataOps enables: Rapid experimentation and innovation for the fastest delivery of new insights to customers. The data scientist’s products are models and segmentations. Unlike software dev, data can’t be automated. Cutting-edge software development teams spend about 23% of their time on DevOps. What is DataOps.

Testing 152

Testing Dashboards Measurement Experimentation 152

CIO Business Intelligence

MARCH 21, 2022

A data scientist’s main objective is to organize and analyze data, often using software specifically designed for the task. The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models.

Unstructured Data 134

Unstructured Data Data Analytics Analytics Structured Data 134

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 85

Data Lake Analytics Snapshot Optimization 85

CIO Business Intelligence

JUNE 27, 2022

I had a chance to ask Cramer some questions about continuous shipment, software development, building a startup, and more. Cramer: I’ve always been a builder, and software was a really natural fit for me. Would you talk a bit about the performance product and tracing software performance? How did that happen? Has that changed?

Software 99

Software Experimentation Testing Marketing 99

Domino Data Lab

AUGUST 6, 2021

When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Develop: includes accessing and preparing data and algorithms, researching and development of models and experimentation. 4 Steps in the DSLC. Assess the Project.

Data Science 75

Data Science Testing Modeling Publishing 75

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. He has a long history of working in the enterprise software and cloud services spaces.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

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 140

Cost-Benefit Metrics Insurance Measurement 140

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. 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

AWS Big Data

MAY 2, 2023

If your updates to a dataset triggers multiple subsequent DAGs, then you can use the Airflow metric max_active_tasks_per_dag to control the parallelism of the consumer DAG and reduce the chance of overloading the system. Removal of experimental Smart Sensors. Let’s demonstrate this with a code example. Apache Airflow v2.4.3

Testing 100

Testing Experimentation Management Metadata 100

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

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

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. Here’s our CFP , open through February 26.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

OCTOBER 13, 2020

In traditional software engineering, precedent has been established for the transition of responsibility from development teams to maintenance, user operations, and site reliability teams. This distinction assumes a slightly different definition of debugging than is often used in software development.

Management 244

Management Metrics Machine Learning Modeling 244

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. On the contrary, the software can still be deployed with one click on any public or private cloud, managed, and scaled accordingly.

Modeling 71

Modeling Testing Experimentation Risk Management 71

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

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. So as my tiny gift for you here are five experimentation and testing ideas for you. You can of course test different pretty images, why not try to reinvent your business model using testing? Rather than create prediction models (with faulty assumptions!)

Testing 112

Testing Experimentation Risk Optimization 112

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. You already have the paid analytics software combos mentioned above. If you don't kill 25% of your metrics each year, you are doing something wrong. Likely not. Omniture cannot save you.

Analytics 118

Analytics Consulting Metrics Experimentation 118

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. IT leaders are exploring how different gen AI tools transform the software development lifecycle. Generative AI, IT Strategy

Cost-Benefit 115

Cost-Benefit Advertising Experimentation Software 115

CIO Business Intelligence

FEBRUARY 3, 2023

More recently, The Royal Mint has evolved its business model in the face of declining cash usage, from its core business of coins and metal manufacturing through to bullion trading, a new consumer business and jewellery line, as well as tentative steps into digital gold and recycling e-waste.

IT 52

IT Experimentation Scorecard Digital Transformation 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. In the VPC model, compute nodes are isolated from other compute nodes to provide strong isolation.

Metadata 85

Metadata Data Lake Optimization Strategy 85

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

IBM Big Data Hub

SEPTEMBER 25, 2023

Uber’s DNA as an analytics company At its core, Uber’s business model is deceptively simple: connect a customer at point A to their destination at point B. Next, they build model data sets out of the snapshots, cleanse and deduplicate the data, and prepare it for analysis as Parquet files. It lands as raw data in HDFS.

OLAP 93

OLAP Data Lake Data-driven Snapshot 93