CIO Business Intelligence

NOVEMBER 27, 2024

From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.

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Data Architecture Cost-Benefit Machine Learning Experimentation 128

Business Over Broadway

AUGUST 4, 2020

Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g.,

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. Data Science Layers.

IT 363

IT Testing Experimentation Software 363

Cloudera

JULY 27, 2021

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. Only through hands-on experimentation can we discern truly useful new algorithmic capabilities from hype. Not all of them require a unique front-end.

Data Science 117

Data Science Enterprise Experimentation Machine Learning 117

CIO Business Intelligence

JANUARY 19, 2024

According to data from PayScale, $99,842 is the average base salary for a data scientist in 2024. Check out our list of top big data and data analytics certifications.) The exam is designed for seasoned and high-achiever data science thought and practice leaders.

Data Science 126

Data Science Machine Learning Predictive Modeling Forecasting 126

Rocket-Powered Data Science

FEBRUARY 15, 2023

encouraging and rewarding) a culture of experimentation across the organization. These rules are not necessarily “Rocket Science” (despite the name of this blog site), but they are common business sense for most business-disruptive technology implementations in enterprises. Test early and often.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

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

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

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Testing Machine Learning Consulting Data Science 300

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

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

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IoT Machine Learning Metadata Data-driven 100

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

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. But what does it mean?

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Data Science Machine Learning Statistics Predictive Modeling 79

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

CIO Business Intelligence

MAY 23, 2022

Many companies whose AI model training infrastructure is not proximal to their data lake incur steeper costs as the data sets grow larger and AI models become more complex. The cloud is great for experimentation when data sets are smaller and model complexity is light.

Experimentation 137

Experimentation Cost-Benefit Data Lake Data Science 137

DataRobot Blog

DECEMBER 6, 2022

Savvy data scientists are already applying artificial intelligence and machine learning to accelerate the scope and scale of data-driven decisions in strategic organizations. These data science teams are seeing tremendous results—millions of dollars saved, new customers acquired, and new innovations that create a competitive advantage.

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Experimentation Forecasting Data-driven Machine Learning 145

Domino Data Lab

JUNE 10, 2020

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

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.

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Machine Learning Management Experimentation Data Science 111

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

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.

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Data Science Predictive Modeling Experimentation Deep Learning 66

CIO Business Intelligence

JANUARY 21, 2025

Why should CIOs bet on unifying their data and AI practices? It created fragmented practices in the interest of experimentation, rapid learning, and widespread adoption and it paid back productivity dividends in many areas. In 2024, departments and teams experimented with gen AI tools tied to their workflows and operating metrics.

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Cost-Benefit Data-driven Strategy Marketing 142

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.” The Age of Hype Cycles. These apply to everyone, in all organizations and walks of life, in every sector.

Digital Transformation 142

Digital Transformation Experimentation Data Science Data Strategy 142

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

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.

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Metadata Cost-Benefit Metrics Optimization 106

IBM Big Data Hub

MAY 16, 2019

The next chapter is all about moving from experimentation to true transformation. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI as core to the business strategy. Companies are entering “chapter two” of their digital transformation. It’s about gaining speed and scale.

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Experimentation Data Science Digital Transformation Modeling 83

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

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

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

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). Algorithm: A set of rules to follow to solve a problem or to decide on a particular action (e.g., They provide more like an FAQ (Frequently Asked Questions) type of an interaction.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

DataRobot Blog

MARCH 8, 2023

As a result, enterprises can now get powerful insights and predictive analytics from their business data by integrating DataRobot-trained machine learning models into their SAP-specific business processes and applications, while bringing data science and analytics teams and business users closer together for better outcomes.

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Enterprise Experimentation Machine Learning Data Science 105

CIO Business Intelligence

JUNE 14, 2023

Certification of Professional Achievement in Data Sciences The Certification of Professional Achievement in Data Sciences is a nondegree program intended to develop facility with foundational data science skills. How to prepare: No prior computer science or programming knowledge is necessary.

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.

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Management Experimentation Data Science Modeling 97

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

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Data scientist skills.

Unstructured Data 137

Unstructured Data Data Analytics Analytics Data Science 137

Dataiku

DECEMBER 28, 2022

It seems like just yesterday that your data science team was a handful of people, actively having to seek out cool use cases for experimentation. As your demand grows, you may consider investing in a data science platform. Now, like many companies, you likely have the opposite problem.

Data Science 52

Data Science Experimentation IT 52

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. Sensor Data Analysis Examples. The Best Way to Achieve Both Accuracy and Cost Control.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

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

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. In 2019, 57% of respondents cited a lack of ML modeling and data science expertise as an impediment to ML adoption; this year, slightly more—close to 58%—did so. But what kind? Where AI projects are being used within companies.

Enterprise 347

Enterprise Deep Learning Data Governance Risk 347