CIO Business Intelligence

NOVEMBER 21, 2023

That’s where an IT strategy that frames shadow IT as an opportunity for improved collaboration can have a profound impact. Set parameters and emphasize collaboration To address one root cause of shadow IT, CIOs must also establish a governance and delivery model for evaluating, procuring, and implementing department technology solutions.

IT 129

IT Risk Cost-Benefit Risk Management 129

CIO Business Intelligence

DECEMBER 19, 2023

This year’s technology darling and other machine learning investments have already impacted digital transformation strategies in 2023 , and boards will expect CIOs to update their AI transformation strategies frequently. Luckily, many are expanding budgets to do so. “94%

Digital Transformation 139

Digital Transformation Unstructured Data Machine Learning Risk Management 139

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

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk? Types of Model Risk.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

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.

Big Data 52

Big Data Experimentation Data-driven Machine Learning 52

DataRobot Blog

FEBRUARY 28, 2023

While this can be an excellent strategy for a future-oriented company, it can prove futile if you don’t maximize the value of your investment. According to Flexera 1 , 92% of enterprises have a multi-cloud strategy, while 80% have a hybrid cloud strategy. Learn more about DataRobot hosted notebooks.

Data Processing 72

Data Processing Experimentation Machine Learning Modeling 72

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 job description. Data scientist salary.

Unstructured Data 132

Unstructured Data Data Analytics Analytics Structured Data 132

CIO Business Intelligence

APRIL 2, 2024

Over the past decade, CIOs have invested significantly in digital transformation initiatives in an effort to improve customer experiences, build data analytics capabilities, and deliver productivity enhancements with automation. It’s like trying to get a jazz quartet, a rock band, a classical orchestra, and a DJ to play in harmony.”

Management 117

Management Digital Transformation Testing Experimentation 117

CIO Business Intelligence

JULY 6, 2022

Laying the foundation for its cloud strategy, BNY Mellon undertook a multi-year application modernization effort. “In On BNY Mellon’s cloud strategy: First and foremost, we view cloud as a journey, not a destination. Today, our strategy is to have a multi-cloud approach. What follows are edited excerpts of that conversation.

Experimentation 85

Experimentation Testing Strategy Risk 85

CIO Business Intelligence

MAY 12, 2022

Driverless AI offers automated pipeline; AI adapts to incoming data. Drag-and-drop Modeler for creating pipelines, IBM integrations. Full IDE for data scientists, automation for non-coding users, drag-and-drop designer. SPSS Modeler is a drag-and-drop tool for creating data pipelines that lead to actionable insights.

Predictive Analytics 137

Predictive Analytics Analytics Statistics Machine Learning 137

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. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

O'Reilly on Data

MARCH 19, 2020

Aggregating artificial intelligence and machine learning topics accounts for nearly 5% of all usage activity on the platform, a touch less than, and growing 50% faster than, the well-established “data science” topic (see Figure 2). The chatbot was one of the first applications of AI in experimental and production usage.

Deep Learning 276

Deep Learning Machine Learning Data-driven Modeling 276

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. The AIgent was built with BERT, Google’s state-of-the-art language model. More relevant to the AIgent is Google’s BERT model, a task-agnostic (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

CIO Business Intelligence

OCTOBER 1, 2022

It has moved past what Cretella calls the “experimentation phase” with scaled solutions and increasingly sophisticated AI applications. Data and AI have since become central to the company’s digital strategy. “We P&G took the first steps in its AI journey more than five years ago.

Manufacturing 144

Manufacturing Digital Transformation IoT Internet of Things 144

FineReport

DECEMBER 11, 2019

In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis. Big data is changing our world.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

Ontotext

NOVEMBER 2, 2023

The pathway forward doesn’t require ripping everything out but building a semantic “graph” layer across data to connect the dots and restore context. However, it will take effort to formalize a shared semantic model that can be mapped to data assets, and turn unstructured data into a format that can be mined for insight.

IT 69

IT Cost-Benefit Data-driven Technology 69

Data Science 101

OCTOBER 15, 2023

And we are still in the science to tech phase, not just battling incredibly challenging lab work, but finding things we can solve. This problem can be witnessed in the business strategies of QC companies. That’s going to use the various existing computing technology in and around the experimentation of this new capability.

Risk 107

Risk Marketing Consulting Technology 107

Insight

MARCH 27, 2019

DeepAugment is an AutoML tool focusing on data augmentation. It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Collecting additional data is not always possible and can be expensive.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

bridgei2i

APRIL 28, 2020

My name is Aruna Babu and I’m a transformation consultant who spent a good part of the last decade crafting strategy that marries business, technology and user needs. And so that process with curation or identifying which data potentially is a leading indicator and then test those leading indicators. Aruna: Got it.

Enterprise 103

Enterprise Data Analytics Analytics Forecasting 103

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.

Management 363

Management Experimentation B2B Machine Learning 363

The Unofficial Google Data Science Blog

JULY 22, 2020

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

Experimentation 95

Experimentation Statistics Testing Strategy 95

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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 Strategy Strategy 142

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Meta-Orchestration . DevOps Infrastructure Tools.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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. I’m also proud to announce an exciting new product: Domino Model Monitor (DMM). Domino’s best-in-class Workbench is now even more powerful for data scientists.

Data Science 82

Data Science Enterprise Experimentation Modeling 82

Cloudera

APRIL 13, 2021

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Before ML can become a catalyst for change, it must first be treated as an integral part of your data strategy. Still, at its core, ML is about science. Optimize later.

Machine Learning 97

Machine Learning Enterprise Experimentation Modeling 97

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

IBM Big Data Hub

JANUARY 17, 2023

Regardless of size, industry or geographical location, the sprawl of data across disparate environments, increase in velocity of data and the explosion of data volumes has resulted in complex data infrastructures for most enterprises. Data science and MLOps. AI is no longer experimental.

Data-driven 57

Data-driven Enterprise Data Governance Prescriptive Analytics 57

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

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. Drill Down into Drift for Rapid Model Diagnostics. Change is inevitable.

Data Science 52

Data Science Experimentation Visualization Modeling 52

Decision Management Solutions

JUNE 25, 2019

Data shows that a traditional data-first approach to analytics is not generating much value for companies and I urged the audience instead to adopt a decisions-first mindset. The last mile – getting ML models embedded into production systems – is critically important for analytic value and yet it is hard and often neglected.

Predictive Analytics 75

Predictive Analytics Analytics Data Science Experimentation 75

Domino Data Lab

FEBRUARY 12, 2020

This post is for people making technology decisions, by which I mean data science team leads, architects, dev team leads, even managers who are involved in strategic decisions about the technology used in their organizations. Motivations for Ray: Training a Reinforcement Learning (RL) Model. If your team has started using ?

Experimentation 137

Experimentation Modeling Data Science Management 137

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

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.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

DataRobot Blog

FEBRUARY 9, 2022

Machine learning operations (MLOps) solutions allow all models to be monitored from a central location, regardless of where they are hosted or deployed. These tools can resolve common model management problems: Challenge 1: Slow Iteration Speed. Refreshing models according to the business schedule or signs of data drift.

IT 52

IT Experimentation Machine Learning Data Processing 52

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. Vignette: Data Science at fluff.ai

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56