CIO Business Intelligence

JANUARY 6, 2025

One of them is Katherine Wetmur, CIO for cyber, data, risk, and resilience at Morgan Stanley. Wetmur says Morgan Stanley has been using modern data science, AI, and machine learning for years to analyze data and activity, pinpoint risks, and initiate mitigation, noting that teams at the firm have earned patents in this space.

IT 140

IT Cost-Benefit Measurement Experimentation 140

CIO Business Intelligence

AUGUST 23, 2024

Bayer Crop Science sees generative AI as a key catalyst for enabling thousands of its data scientists and engineers to innovate agricultural solutions for farmers across the globe. Plans for the first major release of Decision Science Ecosystem are within the next couple of months.

Data Science 101

Data Science Experimentation Testing Modeling 101

AWS Big Data

NOVEMBER 19, 2024

Customers maintain multiple MWAA environments to separate development stages, optimize resources, manage versions, enhance security, ensure redundancy, customize settings, improve scalability, and facilitate experimentation. Over the years, he has helped multiple customers on data platform transformations across industry verticals.

Metadata 111

Metadata Cost-Benefit Metrics Optimization 111

Cloudera

JANUARY 30, 2019

It’s official – Cloudera and Hortonworks have merged , and today I’m excited to announce the availability of Cloudera Data Science Workbench (CDSW) for Hortonworks Data Platform (HDP). Trusted by large data science teams across hundreds of enterprises —. Sound familiar? What is CDSW?

Data Science 76

Data Science Machine Learning Experimentation Visualization 76

Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. Framing data science projects within the four steps of the data science lifecycle (DSLC) makes it much easier to manage limited resources and control timelines, while ensuring projects meet or exceed the business requirements they were designed for.

Data Science 75

Data Science Testing Modeling Publishing 75

O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

Management 317

Management Machine Learning Metrics Modeling 317

Rocket-Powered Data Science

OCTOBER 16, 2020

Be sure to listen to the full recording of our lively conversation, which covered Data Literacy, Data Strategy, Data Leadership, and more. The data age has been marked by numerous “hype cycles.” We build models to test our understanding, but these models are not “one and done.” The Age of Hype Cycles.

Digital Transformation 142

Digital Transformation Experimentation Data Science Data Strategy 142

Domino Data Lab

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Data Science 111

Data Science and Beyond

SEPTEMBER 19, 2016

Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. The hardest parts of data science.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

CIO Business Intelligence

NOVEMBER 7, 2022

CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects. Are data science teams set up for success? Have business leaders defined realistic success criteria and areas of low-risk experimentation?

Machine Learning 128

Machine Learning Data Science Experimentation IoT 128

CIO Business Intelligence

JUNE 14, 2023

Organization: AWS Price: US$300 How to prepare: Amazon offers free exam guides, sample questions, practice tests, and digital training. CDP Data Analyst The Cloudera Data Platform (CDP) Data Analyst certification verifies the Cloudera skills and knowledge required for data analysts using CDP.

Big Data 126

Big Data Data Analytics Analytics Predictive Modeling 126

Domino Data Lab

MARCH 20, 2019

Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.

Management 97

Management Experimentation Data Science Modeling 97

CIO Business Intelligence

MARCH 7, 2023

While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. Here are some force-multiplying differences achievable by agile data teams: Want that dashboard, then update the data catalog.

Digital Transformation 145

Digital Transformation Data Governance Machine Learning Experimentation 145

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Dataiku

MAY 23, 2023

Once a data science project has progressed through the stages of data cleaning and preparation, analysis and experimentation, modeling, testing, and evaluation, it reaches a critical point.

Data Science 52

Data Science Experimentation Machine Learning Testing 52

The Unofficial Google Data Science Blog

JULY 22, 2020

Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.

Experimentation 96

Experimentation Statistics Testing Knowledge Discovery 96

CIO Business Intelligence

AUGUST 20, 2024

But most enterprises can’t operate like young startups with complete autonomy handed over to devops and data science teams. High-performance teams are self-organizing and want significant autonomy in prioritizing work, solving problems, and leveraging technology platforms.

IT 141

IT Digital Transformation Experimentation Risk 141

The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups.

Testing 59

Testing Experimentation Measurement Modeling 59

Domino Data Lab

JUNE 13, 2019

Recently, Chhavi Yadav (NYU) and Leon Bottou (Facebook AI Research and NYU) indicated in their paper, “ Cold Case: The Lost MNIST Digits ”, how they reconstructed the MNIST (Modified National Institute of Standards and Technology) dataset and added 50,000 samples to the test set for a total of 60,000 samples. Did they overfit the test set?

Testing 83

Testing Data Science Experimentation Metadata 83

DataRobot

JANUARY 20, 2022

As health and care delivery converges, analytical staff will be required to work across more boundaries with larger volumes of data than ever before. . The DataRobot and Snowflake platforms include extensive built-in trust features to enable explainability and end-to-end bias and fairness testing and monitoring over time.

Data-driven 145

Data-driven Experimentation Predictive Modeling Data Warehouse 145

The Unofficial Google Data Science Blog

APRIL 23, 2024

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

DECEMBER 19, 2023

In the 2023 State of Data Science and Machine Learning Report , only 18% of respondents said that at least half their machine learning models make it into production. If CIOs don’t improve conversions from pilot to production, they may find their investors losing patience in the process and culture of experimentation.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

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 140

Management Digital Transformation Testing Experimentation 140

Domino Data Lab

JULY 28, 2021

The data science lifecycle (DLSC) has been defined as an iterative process that leads from problem formulation to exploration, algorithmic analysis and data cleaning to obtaining a verifiable solution that can be used for decision making. The data science process in a business environment begins with the Manage stage.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

Data Science 101

JANUARY 15, 2020

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Suppose we want to test the effectiveness of a new drug against a particular disease. Bias can cause a huge error in experimentation results so we need to avoid them. We randomly recruit subjects for that.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Our scientists and teams are familiar with working in Spark on EMR and using data with our existing feature store and data warehouse.

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

CIO Business Intelligence

JULY 6, 2022

Where cloud is most effective: One area I think cloud is just going to be really effective is any area which involves experimentation and has a high opportunity cost. Because when you can experiment, you can potentially enter a new business quickly, test an idea. At its core, it is sophisticated and complex math on data.

Experimentation 98

Experimentation Testing Strategy Risk 98

CIO Business Intelligence

AUGUST 1, 2023

1 for CIOs, CTOs, and CDOs is to enable secure, scalable access to a growing range of generative AI models and enable data science teams to develop and operationalize fine-tuned LLMs tailored for the organization’s data and use cases,” says Kjell Carlsson, head of data science strategy and evangelism at Domino.

Digital Transformation 98

Digital Transformation Unstructured Data Strategy Data Science 98

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. He also encouraged data scientists to understand how new data science algorithms rearrange power as well as how the history of data is a story of truth and power.

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

IBM Big Data Hub

JUNE 10, 2024

Cloud-based XaaS solutions provide scalability, flexibility and access to a wide range of AI tools and services, while on-premises XaaS offerings enable greater control over data governance, compliance and security. Embracing a culture of experimentation helps businesses drive innovation while minimizing financial risk.

Experimentation 62

Experimentation Optimization Risk Management Machine Learning 62

Sisense

JANUARY 21, 2020

In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. A new drug promising to reduce the risk of heart attack was tested with two groups. Now, let’s check a slightly different case in which grouping the data leads to incorrect results. It really depends on the circumstances.

Testing 104

Testing Data-driven Risk Statistics 104

The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

Modeling 63

Modeling Experimentation Knowledge Discovery KDD 63

Data Science 101

SEPTEMBER 1, 2023

If I had more room for experimentation though, I’d definitely give svelte and solidjs a try. Everything related to spinning up a web server in development, writing code, hot reloading, running tests, cicd, deployments, etc. Honestly, the answer to this question changes every day for me. Learn JavaScript, and then TypeScript.

Experimentation 116

Experimentation Testing Technology IT 116

CIO Business Intelligence

APRIL 15, 2024

Given the speed required, Lowden established a specialized team for the project to encourage a culture of experimentation and “moving fast to learn fast.” “You Three layers of content integrity Another big part of ensuring the integrity of the content was testing, which consisted of three layers. We don’t write all the code.

Testing 102

Testing Machine Learning Data Quality Experimentation 102

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. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Cloudera

APRIL 9, 2021

To effectively leverage their predictive capabilities and maximize time-to-value these companies need an ML infrastructure that allows them to quickly move models from data pipelines, to experimentation and into the business. A/B testing). model packaging, deployment and serving. model monitoring. The ML Use Case.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98