DataRobot Blog

NOVEMBER 15, 2022

Organizations are looking to deliver more business value from their AI investments, a hot topic at Big Data & AI World Asia. At the well-attended data science event, a DataRobot customer panel highlighted innovation with AI that challenges the status quo. Automate with Rapid Iteration to Get to Scale and Compliance.

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Big Data Experimentation Data-driven Machine Learning 52

CIO Business Intelligence

DECEMBER 19, 2023

After all, every department is pressured to drive efficiencies and is clamoring for automation, data capabilities, and improvements in employee experiences, some of which could be addressed with generative AI. As every CIO can attest, the aggregate demand for IT and data capabilities is straining their IT leadership teams.

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Digital Transformation Unstructured Data Machine Learning Risk Management 143

CIO Business Intelligence

NOVEMBER 21, 2023

After all, 41% of employees acquire, modify, or create technology outside of IT’s visibility , and 52% of respondents to EY’s Global Third-Party Risk Management Survey had an outage — and 38% reported a data breach — caused by third parties over the past two years. There may be times when department-specific data needs and tools are required.

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IT Risk Cost-Benefit Risk Management 137

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. Visual IDE for data pipelines; RPA for rote tasks. Highlights.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

Domino Data Lab

AUGUST 6, 2021

An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. As a business becomes model driven, Model Risk Management can help limit risks for sustainable growth in almost any industry vertical. Types of Model Risk. Model implementation.

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Risk Management Risk Modeling Enterprise 64

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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DataRobot Blog

FEBRUARY 28, 2023

Integrating different systems, data sources, and technologies within an ecosystem can be difficult and time-consuming, leading to inefficiencies, data silos, broken machine learning models, and locked ROI. Exploratory Data Analysis After we connect to Snowflake, we can start our ML experiment.

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Data Processing Experimentation Machine Learning Modeling 72

DataRobot Blog

OCTOBER 18, 2022

Organizations are looking for AI platforms that drive efficiency, scalability, and best practices, trends that were very clear at Big Data & AI Toronto. DataRobot Booth at Big Data & AI Toronto 2022. Monitoring and Managing AI Projects with Model Observability. Accelerating Value-Realization with Industry Specific Use Cases.

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Big Data Forecasting Experimentation Metrics 96

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics).

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Internet of Things Machine Learning Manufacturing IoT 157

Ontotext

NOVEMBER 2, 2023

Achieving this advantage is dependent on their ability to capture, connect, integrate, and convert data into insight for business decisions and processes. This is the goal of a “data-driven” organization. We call this the “ Bad Data Tax ”. This is partly because integrating and moving data is not the only problem.

IT 69

IT Cost-Benefit Data-driven Technology 69

CIO Business Intelligence

OCTOBER 1, 2022

The partners say they will create the future of digital manufacturing by leveraging the industrial internet of things (IIoT), digital twin , data, and AI to bring products to consumers faster and increase customer satisfaction, all while improving productivity and reducing costs. Data and AI as digital fundamentals.

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Manufacturing Digital Transformation IoT Internet of Things 145

IBM Big Data Hub

NOVEMBER 3, 2023

As a result, I see access to real-time data as a necessary foundation for building business agility and enhancing decision making. Stream processing is at the core of real-time data. Apache Kafka streams get data to where it needs to go, but these capabilities are not maximized when Apache Kafka is deployed in isolation.

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Cost-Benefit Experimentation Testing Digital Transformation 78

DataRobot

JANUARY 13, 2022

Many companies find that they have a treasure trove of data but lack the expertise to use it to improve ROI. AI can help them wrangle the data that’s collected and quickly answer important questions. But they often don’t have enough data scientists with the full range of domain expertise to realize its full value.

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ROI Machine Learning Experimentation Data Science 52

Cloudera

APRIL 9, 2021

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Cloudera

AUGUST 20, 2020

Businesses had to literally switch operations, and enable better collaboration and access to data in an instant — while streamlining processes to accommodate a whole new way of doing things. This year, we hope to see even more stories of ML and AI driven innovation among the finalists. That’s really important.

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Enterprise Experimentation Uncertainty Deep Learning 120

O'Reilly on Data

MARCH 19, 2020

We use it as a data source for our annual platform analysis , and we’re using it as the basis for this report, where we take a close look at the most-used and most-searched topics in machine learning (ML) and artificial intelligence (AI) on O’Reilly [1]. The chatbot was one of the first applications of AI in experimental and production usage.

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Deep Learning Machine Learning Data-driven Modeling 276

Domino Data Lab

AUGUST 5, 2019

This Domino Data Science Field Note covers Chris Wiggins ‘s recent data ethics seminar at Berkeley. Data Scientists, Tradeoffs, and Data Ethics. As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong.

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Data Science Data-driven Modeling Machine Learning 93

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. Hernán and Robins are both epidemiologists, which means they often have to deal with data with strong limitations on sample size and feasibility of experiments.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Domino Data Lab

NOVEMBER 14, 2019

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Yet, despite the complexity (or because of it), data scientists and researchers curate and use different languages, tools, packages, techniques, and frameworks to tackle the problem they are trying to solve.

Modeling 71

Modeling Machine Learning Data Science Experimentation 71

AWS Big Data

NOVEMBER 2, 2023

GoDaddy is a data-driven company, and getting meaningful insights from data helps us drive business decisions to delight our customers. As a part of this vision, GoDaddy’s Data & ML Platform team plans to use EMR Serverless as one of the compute solutions under the hood.

Cost-Benefit 88

Cost-Benefit Big Data Testing Optimization 88

DataRobot

APRIL 19, 2021

Data Scientists Are AI Experts. But for more complex business decisions, including those that use less structured data, we have artificial intelligence systems. Data scientists train the algorithms using datasets that contain curated learning examples. They need data assembly, data science, integration, and visualization skills.

Machine Learning 52

Machine Learning Visualization Data-driven Data Science 52

Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. How can good data lead to faulty conclusions? How does this happen? 9/10 = 90%.

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Testing Risk Data-driven Statistics 104

IBM Big Data Hub

JANUARY 17, 2023

Due to the convergence of events in the data analytics and AI landscape, many organizations are at an inflection point. Furthermore, a global effort to create new data privacy laws, and the increased attention on biases in AI models, has resulted in convoluted business processes for getting data to users. Data governance.

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Data-driven Enterprise Data Governance Prescriptive Analytics 57

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.

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Management Experimentation B2B Machine Learning 363

Alation

SEPTEMBER 28, 2022

The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What exactly is DataOps ?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

O'Reilly on Data

OCTOBER 19, 2021

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Why: Data Makes It Different. Not only is data larger, but models—deep learning models in particular—are much larger than before.

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IT Testing Experimentation Software 346

Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade.

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Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

Testing 307

Testing Machine Learning Consulting Data Quality 307

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

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. You can identify data drift, missing information, and other issues, and take corrective action before bigger problems occur.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

DataRobot Blog

JANUARY 12, 2023

By outsourcing the day-to-day management of the data science platform to the team who created the product, AI builders can see results quicker and meet market demands faster, and IT leaders can maintain rigorous security and data isolation requirements. Delivering more than 1.4 Sudhir Hasbe.

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Management Data-driven ROI Cost-Benefit 59

CIO Business Intelligence

MARCH 7, 2023

Some IT organizations elected to lift and shift apps to the cloud and get out of the data center faster, hoping that a second phase of funding for modernization would come. There are similar concerns for CIOs looking to build data and analytics capabilities. Release an updated data viz, then automate a regression test.

Digital Transformation 145

Digital Transformation Data Governance Experimentation Machine Learning 145

AWS Big Data

JUNE 20, 2023

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) and data sources residing in AWS, on-premises, or other cloud systems using SQL or Python. Solution overview Data scientists are generally accustomed to working with large datasets.

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Data Lake Data Science Recreation/Entertainment Experimentation 73

IBM Big Data Hub

OCTOBER 20, 2023

AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models.

Machine Learning 79

Machine Learning Manufacturing Cost-Benefit Deep Learning 79

DataRobot Blog

OCTOBER 27, 2022

Data drift is a phenomenon that reflects natural changes in the world around us, such as shifts in consumer demand, economic fluctuation, or a force majeure. The key, of course, is your response time: how quickly data drift can be analyzed and corrected. Change is inevitable. Growth is optional. John Maxwell.

Data Science 52

Data Science Experimentation Visualization Modeling 52

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

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