IBM Big Data Hub

MARCH 6, 2024

Traditional metrics, such as lines of code written or hours worked, often fall short in capturing the intricacies of complex workflows. DevOps Research and Assessment metrics (DORA), encompassing metrics like deployment frequency, lead time and mean time to recover , serve as yardsticks for evaluating the efficiency of software delivery.

Testing 108

Testing Metrics Measurement Key Performance Indicator 108

IBM Big Data Hub

APRIL 13, 2023

After developing a machine learning model, you need a place to run your model and serve predictions. If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Also, a column in the dataset indicates if each flight had arrived on time or late.

Modeling 83

Modeling Statistics Machine Learning Testing 83

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. Calendars can also help you understand seasonality and incorporate it into the forecast model.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Cloudera

MAY 23, 2024

Information is often redundant and analyzing data requires combining across multiple formats, including written documents, streamed data feeds, audio and video. However, training models require huge hardware resources, significant budgets and specialist teams. The image above demonstrates a KMS built using the llama3 model from Meta.

Management 78

Management Metrics Data Processing Data Lake 78

CIO Business Intelligence

MAY 1, 2024

Gen AI takes us from single-use models of machine learning (ML) to AI tools that promise to be a platform with uses in many areas, but you still need to validate they’re appropriate for the problems you want solved, and that your users know how to use gen AI effectively. Now nearly half of code suggestions are accepted.

Cost-Benefit 135

Cost-Benefit Metrics Insurance Measurement 135

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? Audit has been an integral aspect of model risk management (MRM) in consumer finance for years, and colleagues at BLDS and QuantUniversity have been conducting model audits for some time.

Risk Management 287

Risk Management Risk Testing Measurement 287

AWS Big Data

NOVEMBER 29, 2023

dbt lets data engineers quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, continuous integration and continuous delivery (CI/CD), and documentation. The team uses dbt-glue to build a transformed gold model optimized for business intelligence (BI).

Data Lake 105

Data Lake Management Metrics Data Warehouse 105

DataRobot Blog

SEPTEMBER 30, 2022

How do you track the integrity of a machine learning model in production? Model Observability can help. By tracking service, drift, prediction data, training data, and custom metrics, you can keep your models and predictions relevant in a fast-changing world. Model Observability Features.

Slice and Dice 52

Slice and Dice Modeling Metrics Experimentation 52

AWS Big Data

APRIL 2, 2024

We also detail how the feature works and what criteria was applied for the model and prompt selection while building on Amazon Bedrock. Data consumers need detailed descriptions of the business context of a data asset and documentation about its recommended use cases to quickly identify the relevant data for their intended use case.

Metadata 91

Metadata Metrics Data-driven Modeling 91

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 361

Marketing Experimentation Metrics Testing 361

CIO Business Intelligence

MAY 13, 2024

IBM is betting big on its toolkit for monitoring generative AI and machine learning models, dubbed watsonx.governance , to take on rivals and position the offering as a top AI governance product, according to a senior executive at IBM. watsonx.governance is a toolkit for governing generative AI and machine learning models.

Risk 101

Risk Machine Learning Modeling Enterprise 101

CIO Business Intelligence

JANUARY 23, 2024

For example, McKinsey suggests five metrics for digital CEOs , including the financial return on digital investments, the percentage of leaders’ incentives linked to digital, and the percentage of the annual tech budget spent on bold digital initiatives. As a result, outcome-based metrics should be your guide.

Measurement 123

Measurement Digital Transformation KPI Metrics 123

Jet Global

NOVEMBER 2, 2021

KPIs are a select few metrics that provide insight on the operation of all entities contributing to the supply chain. Metrics help supply chain management track its progress in achieving its goals and respond quickly if needed. Measurable: Is your metric quantifiable? That’s where KPI monitoring comes into play.

Metrics 52

Metrics Reporting KPI Key Performance Indicator 52

CIO Business Intelligence

MAY 6, 2024

The problem In years past, teams pursuing a traditional project approach would typically have some form of documentation that details what will be delivered and what it will cost. Previously, technology teams were effectively only tracking IT metrics, reporting on whether they delivered on time and on budget.

Digital Transformation 57

Digital Transformation Metrics Sales Technology 57

Jet Global

MAY 15, 2020

A financial Key Performance Indicator (KPI) or metric is a quantifiable measure that a company uses to gauge its financial performance over time. These three statements are data rich and full of financial metrics. The Fundamental Finance KPIs and Metrics – Cash Flow. What is a Financial KPI? Current Ratio. View Guide Now.

Metrics 98

Metrics Finance Reporting KPI 98

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. Once you move your model into production, you need to monitor and manage your models to ensure that you can trust predictions and turn them into the right business decisions.

Metrics 145

Metrics Statistics Modeling Data Science 145

IBM Big Data Hub

FEBRUARY 8, 2024

The cornerstone of the EU AI Act: safeguards to prevent unacceptable risk The EU AI Act requires that general purpose AI models, including generative AI systems such as large language (LLMs) and foundation models, adhere to a classification system based on systematic risk tiers.

Risk Management 74

Risk Management Risk Metrics Machine Learning 74

erwin

MARCH 24, 2020

Technical teams charged with maintaining and executing these processes require detailed tasks, and business process modeling is integral to their documentation. erwin’s Evolve software is integral to modeling process flow requirements, but what about the technology side of the equation?

Modeling 97

Modeling Data Processing Software Finance 97

CIO Business Intelligence

NOVEMBER 15, 2023

We’ve been working on large language models for years.” When Bedi talks of enhancing “agent” productivity, he uses the term broadly to mean HR staff, IT service desk operatives, customer service agents, and sales staff, all of whom can benefit from generative AI’s ability to find answers in masses of documentation.

Measurement 129

Measurement Digital Transformation Metrics Software 129

AWS Big Data

AUGUST 21, 2023

Lexical search looks for words in the documents that appear in the queries. For the demo, we’re using the Amazon Titan foundation model hosted on Amazon Bedrock for embeddings, with no fine tuning. In lexical search, the search engine compares the words in the search query to the words in the documents, matching word for word.

Data Processing 94

Data Processing Experimentation Visualization Metrics 94

Occam's Razor

APRIL 8, 2013

To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This should not be news to you. But it is not routine.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

AWS Big Data

MARCH 25, 2024

Foundation models (FMs) are large machine learning (ML) models trained on a broad spectrum of unlabeled and generalized datasets. This scale and general-purpose adaptability are what makes FMs different from traditional ML models. As new data appears, you will have to continuously fine-tune or further train the model.

Data Lake 92

Data Lake Unstructured Data Management Modeling 92

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

IBM Big Data Hub

APRIL 9, 2024

” The historical sources watsonx.data accesses comprise relational, object and document databases, including IBM® Db2® , IBM® Cloudant , IBM Cloud® Object Storage and PostgreSQL. “For the Masters we use 290 traditional AI models to project where golf balls will land,” says Baughman.

Management 80

Management IT Machine Learning Metrics 80

AWS Big Data

JANUARY 9, 2024

Lexical search In lexical search, the search engine compares the words in the search query to the words in the documents, matching word for word. Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Only items that have words the user typed match the query.

Cost-Benefit 94

Cost-Benefit Visualization Modeling Machine Learning 94

Rocket-Powered Data Science

SEPTEMBER 20, 2021

We would be able to go far beyond searching for correctly spelled column headings in databases or specific keywords in data documentation, to find the data we needed (assuming we even knew the correct labels, metatags, and keywords used by the dataset creators). Sharing and integrating such important data streams has never been such a dream.

Data Science 286

Data Science Forecasting Business Intelligence Sales 286

BA Learnings

MARCH 26, 2020

When identifying benefits particularly for the purpose of calculating Return on Investment (ROI), keep in mind that calculating ROI for a single project can be tricky as some process metrics or financial gains tend to be influenced by process changes, software implementation and other projects happening in parallel.

Key Performance Indicator 207

Key Performance Indicator Cost-Benefit ROI Measurement 207

IBM Big Data Hub

FEBRUARY 22, 2024

Earning trust in the outputs of AI models is a sociotechnical challenge that requires a sociotechnical solution. There must be a concerted effort to make these principles a reality through consideration of the functional and non-functional requirements in the models and the governance systems around those models.

Metadata 94

Metadata Measurement Risk Modeling 94

DataRobot Blog

JULY 21, 2022

Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Not every project needs machine learning.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

CIO Business Intelligence

MAY 15, 2024

Generative AI (genAI) arrived on the scene with use cases such as “support chatbots” or “talk to your documentation apps” that were so obviously useful that many companies are well on their way to taking them into production.

Digital Transformation 111

Digital Transformation Strategy Metrics Machine Learning 111

erwin

MARCH 28, 2019

What is business process modeling (BPM)? It also combines process/ workflow, functional, organizational and data/resource views with underlying metrics such as costs, cycle times and responsibilities to provide a foundation for analyzing value chains, activity-based costs, bottlenecks, critical paths and inefficiencies.

Modeling 71

Modeling Digital Transformation Cost-Benefit Visualization 71

CIO Business Intelligence

DECEMBER 19, 2023

Embrace metrics and iterate To achieve maximum efficiency, Cziomer also suggests focusing service efforts on DevOps Research and Assessment (DORA) metrics, such as “lead time for change” and “time to restore service.” Have a service delivery model that’s predictable and consistent.”

IT 138

IT Testing Metrics Optimization 138

Smart Data Collective

AUGUST 30, 2021

Unlike marketing metrics, knowledge management is challenging to measure. While there is no magic wand that you can swish and flick, there are certain metrics that you can track to measure the success of your knowledge base. Top 6 Knowledge Management Metrics to Measure Employee Engagement. So, regularly monitor these metrics.

Management 134

Management Analytics Metrics Measurement 134

Analytics Vidhya

NOVEMBER 21, 2023

These representations are the vector embeddings generated by the Embedding Models. The vector stores have become an integral part of developing apps with Deep Learning Models, especially the Large Language Models.

Deep Learning 272

Deep Learning Structured Data Modeling Analytics 272

CIO Business Intelligence

MAY 2, 2023

Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria. One recent study shows that only 50% follow a product-centric operating model focusing on customer centricity and delivering delightful customer experiences.

Digital Transformation 129

Digital Transformation IT Data-driven Metrics 129