DataKitchen

JANUARY 20, 2021

A centralized team can publish a set of software services that support the rollout of Agile/DataOps. Develop/execute regression testing . Test data management and other functions provided ‘as a service’ . The center of excellence (COE) model leverages the DataOps team to solve real-world challenges. Deploy to production.

Metrics 243

Metrics Experimentation Measurement Testing 243

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). Why AI software development is different. This shift requires a fundamental change in your software engineering practice.

Management 332

Management Machine Learning Experimentation Metrics 332

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

CIO Business Intelligence

MARCH 7, 2023

OpenAI’s text-generating ChatGPT, along with its image generation cousin DALL-E, are the most prominent among a series of large language models, also known as generative language models or generative AI, that have captured the public’s imagination over the last year. And, he says, using generative AI for coding has worked well.

Enterprise 113

Enterprise IT Experimentation Unstructured Data 113

CIO Business Intelligence

MARCH 10, 2022

Fujitsu remains very much interested in the mainframe market, with a new model still on its roadmap for 2024, and a move under way to “shift its mainframes and UNIX servers to the cloud, gradually enhancing its existing business systems to optimize the experience for its end-users.”

Sales 124

Sales Testing Experimentation Data Processing 124

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. Instead of focusing on a narrowly defined task with minimal testing and feedback, DataOps focuses on adding value. Create tests. What is DataOps.

Testing 157

Testing Dashboards Measurement Experimentation 157

Domino Data Lab

MARCH 20, 2019

Our mission at Domino is to enable organizations to put models at the heart of their business. Today we’re announcing two major new capabilities in Domino that make model development easier and faster for data scientists. This pain point is magnified in organizations with teams of data scientists working on numerous experiments.

Management 97

Management Experimentation Data Science Modeling 97

CIO Business Intelligence

JUNE 28, 2023

Belcorp operates under a direct sales model in 14 countries. As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

Digital Transformation 87

Digital Transformation Cost-Benefit Informatics Data mining 87

Domino Data Lab

MAY 15, 2019

Unfortunately, a common challenge that many industry people face includes battling “ the model myth ,” or the perception that because their work includes code and data, their work “should” be treated like software engineering. These steps also reflect the experimental nature of ML product management.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

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

JANUARY 31, 2022

Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated. 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.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. Just don't fall for their bashing of all other vendors or their silly claims, false, of "superiority" in terms of running 19 billion combinations of tests or the bonus feature of helping you into your underwear each morning. And I meant every word of it.

Testing 112

Testing Experimentation Risk Optimization 112

Smarten

MAY 17, 2023

Create a Sandbox Environment If you are in the business of technology, you probably have a robust sandbox environment in which you can use new tools and leverage opportunities with test data to understand the value of tools like Azure OpenAI, GPT 3.x

Digital Transformation 52

Digital Transformation Experimentation Risk Testing 52

IBM Big Data Hub

DECEMBER 14, 2023

Another year has passed—it felt like the whole world was talking about and trying out tools powered by generative AI and Large Language Models (LLMs). Platform engineering, internal development platform – Platform engineering is at the forefront of modern software development, driving innovation and collaboration across teams.

Experimentation 73

Experimentation Testing Software Management 73

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 96

Cost-Benefit Modeling Manufacturing Interactive 96

IBM Big Data Hub

NOVEMBER 3, 2023

Eileen Lowry, VP of Product Management for IBM Automation, Integration Software, touches on the innovation that IBM is doing with Apache Flink: “We realize investing in event-driven architecture projects can be a considerable commitment, but we also know how necessary they are for businesses to be competitive.

Cost-Benefit 78

Cost-Benefit Experimentation Testing Digital Transformation 78

Smarten

APRIL 18, 2023

and GPT-4 families of large language models, and fine-tuned using supervised and reinforcement learning techniques. While new medical techniques and tools can take time to refine and prove, doctors often leverage experimental techniques to save lives. One of the most intriguing AI developments is the recent focus on ChatGPT.

Digital Transformation 52

Digital Transformation Experimentation ROI Technology 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

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

CIO Business Intelligence

AUGUST 17, 2023

We’ve been shortlisting and building out potential proof-of-concept options and modelling revenue impact and already taken one concept through our innovation lab and into live production.” We’re testing ChatGPT at an individual level,” he says. “An Medica’s O’Brien issues a caution as well.

Risk 92

Risk Cost-Benefit Strategy Experimentation 92

CIO Business Intelligence

OCTOBER 18, 2023

Of roughly 2,500 CIOs surveyed recently by Gartner, 9% say they have already deployed gen AI applications, and a staggering 55% say they will deploy large language models (LLMs) in production by the end of 2025. Snap, LexisNexis, and Lonely Planet are also developing and training LLM models, each leveraging their own data stored on AWS. “We

Risk 134

Risk Manufacturing Enterprise Technology 134

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. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

APRIL 7, 2019

To build such systems, your software must understand the vocabulary, grammar and semantics of the text’s human language. A good NLP library will make it easy to both train your own NLP models and integrate with the downstream ML or DL pipeline. We assume that you need to build production-grade software. Pretrained models.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

AWS Big Data

JULY 3, 2023

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. Solution overview To set up and test this experiment, we complete the following high-level steps: Create an S3 bucket.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

AWS Big Data

MAY 2, 2023

Test the feature To test this feature, run the producer DAG. Removal of experimental Smart Sensors. This feature is particularly useful if you want to externally process various files, evaluate multiple machine learning models, or extraneously process a varied amount of data based on a SQL request. Apache Airflow v2.4.3

Testing 100

Testing Experimentation Management Metadata 100

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? We rely heavily on automated testing.

Software 99

Software Experimentation Testing Marketing 99

Cloudera

JANUARY 30, 2019

Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation. Run experiments with historical reference for hyperparameter tuning, feature engineering, grid searches, A/B testing and more. Sound familiar?

Data Science 72

Data Science Machine Learning Experimentation Cost-Benefit 72

CIO Business Intelligence

JULY 31, 2023

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. How has, say, ChatGPT hit your business model?” This is an issue for CIOs.

Strategy 95

Strategy Risk Enterprise Risk Management 95

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. Prior to GA, we plan to offer two features that will enable us to reduce the cost of your first collection.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Domino Data Lab

JULY 28, 2021

For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. Roles are assigned and work priorities are established before any research and development can begin.

Data Science 59

Data Science Enterprise Modeling Experimentation 59

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

Domino Data Lab

AUGUST 5, 2019

As more companies become model-driven , data scientists are uniquely positioned to drive innovation and to help their companies remain economically strong. the model context ) when building and using models enable data scientists to directly impact business problems. data munging, building models, etc.).

Data Science 93

Data Science Data-driven Modeling Machine Learning 93

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

FineReport

DECEMBER 11, 2019

The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. Follow FineReport Reporting Software on Facebook to know more about data visualization! – Data Divination: Big Data Strategies. Data analysis is setting off a revolution.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

O'Reilly on Data

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. All ML projects are software projects.

IT 346

IT Testing Experimentation Software 346