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

Jet Global

JUNE 24, 2021

Non-profit metrics quantify the organization’s many endeavours in extending its impact on society. How to choose the right non-profit metrics? There are hundreds of metrics to choose from, and if they are not vetted properly, the organization could end up with too few or too many KPIs. What are non-profit KPIs?

Metrics 98

Metrics Reporting KPI Measurement 98

CIO Business Intelligence

NOVEMBER 20, 2023

The US Bureau of Labor Statistics has projected that the number of software developers will grow 25% from 2021-31. Well-known metrics, such as deployment frequency, are useful when it comes to tracking teams but not individuals. Then we complemented these with the following four “opportunity-focused metrics.” Talent capability.

Measurement 113

Measurement Metrics Software Testing 113

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111

Modeling Statistics Testing Metrics 111

Jet Global

JUNE 24, 2023

Non-profit metrics quantify the organization’s many endeavours in extending its impact on society. How to choose the right non-profit metrics? There are hundreds of metrics to choose from, and if they are not vetted properly, the organization could end up with too few or too many KPIs. What are non-profit KPIs?

Metrics 52

Metrics Reporting KPI Measurement 52

IBM Big Data Hub

AUGUST 7, 2023

Tackling complexities in clinical trial site selection: A playground for a new technology and AI operating model Enrollment strategists and site performance analysts are responsible for constructing and prioritizing robust end-to-end enrollment strategies tailored to specific trials. To do so they require data, which is in no shortage.

Optimization 56

Optimization Forecasting Data-driven Strategy 56

IBM Big Data Hub

APRIL 9, 2024

” Watsonx.data lets organizations optimize workloads for different uses. Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. Watsonx.governance makes it easy to manage and deploy all these models effectively.”

Management 80

Management IT Machine Learning Metrics 80

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 82

Metrics Machine Learning Risk Statistics 82

datapine

JUNE 7, 2022

From political issues to sports statistics and the recent report you received on the ROI of your company blog, the internet as well as informational reports are flooded with examples of misleading data visualization. But, by knowing what to look for, you can avoid connecting with metrics that will lead your organization down the wrong path.

Visualization 267

Visualization Metrics Statistics Dashboards 267

AWS Big Data

MARCH 11, 2024

This means that cost-optimization exercises can happen at any time—they no longer need to happen in the planning phase. These scalable properties of Apache Flink can be key to optimizing your cost in the cloud. With Managed Service for Apache Flink, you can add and remove compute with the click of a button.

Management 99

Management Snapshot Metrics Cost-Benefit 99

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 120

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 120

DataRobot Blog

DECEMBER 6, 2022

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Bureau of Labor Statistics predicts that the employment of data scientists will grow 36 percent by 2031, 1 much faster than the average for all occupations. Read the blog. Read the blog.

Experimentation 145

Experimentation Forecasting Data-driven Machine Learning 145

Occam's Razor

AUGUST 11, 2014

In the post Adil commented that he's observed that attribution modeling is missing from most web analytics dashboards. My reply was that attribution modeling might not be present in a physical manner, but that it should be there below the surface to ensure our executives have a smarter understanding of a channel's true contribution.

Metrics 147

Metrics Dashboards Key Performance Indicator B2B 147

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

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

Machine Learning 217

Machine Learning Risk Management Statistics 217

Jet Global

APRIL 5, 2021

Enterprises actively use financial modeling to guide their financial planning and strategic decision-making. Financial models offer data-driven, quantitative analysis that tells you where your company stands and where it’s heading. That being said, one model can’t do it all. What Is Financial Modeling?

Modeling 98

Modeling Forecasting Metrics Finance 98

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

AWS Big Data

JULY 13, 2023

To reflect the needs of their customers spread across different geographies and industries, Altron has organized their operating model across individual Operating Companies (OpCos). To verify the data quality of the sources through statistically-relevant metrics, AWS Glue Data Quality runs data quality tasks on relevant AWS Glue tables.

Optimization 92

Optimization B2B Data Quality Sales 92

CIO Business Intelligence

JANUARY 24, 2024

Generative AI and large language models (LLMs) like ChatGPT are only one aspect of AI. Model sizes: ~5 billion to >1 trillion parameters. Model sizes: ~Millions to billions of parameters. Downsides: Not generative; model behavior can be a black box; results can be challenging to explain.

Deep Learning 81

Deep Learning Unstructured Data Machine Learning Modeling 81

AWS Big Data

MARCH 21, 2024

The data becomes available in Amazon Redshift within seconds, allowing you to use the analytics features of Amazon Redshift and capabilities like data sharing, workload optimization autonomics, concurrency scaling, machine learning, and many more. They would like to get these metrics in near real time using a zero-ETL integration.

Data Warehouse 90

Data Warehouse Metrics Statistics Optimization 90

DataKitchen

NOVEMBER 18, 2022

In addition to the tracking of relationships and quality metrics, DataOps Observability journeys allow users to establish baselines?concrete DataOps Observability must also store run data over time for root cause diagnosis and statistical process control analysis. Methods for technology/tool integrations. Data and tool tests.

Testing 130

Testing Statistics Measurement Metrics 130

AWS Big Data

MAY 4, 2023

This has a few advantages: If a pod is optimally sized, the Kubernetes scheduler can efficiently pack in more pods in a single node, leading to better utilization of the underlying cluster. The Amazon EMR on EKS uplift is charged based on the vCPU and memory resources consumed by a Kubernetes pod.This means an optimally sized pod is cheaper.

Metrics 81

Metrics Dashboards Optimization Statistics 81

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. For any given input, the same program won’t necessarily produce the same output; the output depends entirely on how the model was trained.

Management 327

Management Machine Learning Experimentation Metrics 327

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

FineReport

APRIL 3, 2024

Dashboards offer succinct overviews of performance metrics, catering to stakeholders requiring swift insights. Comprehending the distinctions between BI dashboards and reports is crucial for optimizing their effectiveness in data analysis and decision-making processes. Diverse visualization options enable effective communication.

Dashboards 52

Dashboards Visualization Metrics Interactive 52

AWS Big Data

JUNE 28, 2023

read replicas, federated query, analytics accelerators) Move the data to a data store optimized for running analytical queries such as a data warehouse The zero-ETL integration is focused on simplifying the latter approach. They would like to get these metrics in near-real time using a zero-ETL integration. or higher version) database.

Data Warehouse 87

Data Warehouse Analytics Metrics Dashboards 87

DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. In addition to the accuracy of the models we built, we had to consider business metrics, cost, interpretability, and suitability for ongoing operations. Sensor Data Analysis Examples.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Sisense

SEPTEMBER 3, 2020

Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Business Analyst.

Statistics 97

Statistics Metrics Visualization Finance 97

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

Machine Learning 75

Machine Learning Modeling Deep Learning Predictive Modeling 75

Smart Data Collective

DECEMBER 17, 2021

For example, this model can be used for marketing to increase revenue, or it can be used to make predictions. These insights optimized his marketing efforts for better ROI and conversion rates. The first step is setting the goals and defining what success metrics you want to measure. Preparing the Data for Analysis.

Marketing Analytics 93

Marketing Analytics Marketing Data-driven Analytics 93

AWS Big Data

JUNE 9, 2023

This data is then projected into analytics services such as data warehouses, search systems, stream processors, query editors, notebooks, and machine learning (ML) models through direct access, real-time, and batch workflows.

Data Lake 115

Data Lake Metadata Optimization Statistics 115

datapine

JUNE 4, 2020

A CTO dashboard is a critical tool in the process of evaluating, monitoring, and analyzing crucial high-level IT metrics such as support expenses or critical bugs, e.g., with the goal to create a centralized and dynamic point of access for all relevant IT data. Try our professional dashboard software for 14 days, completely free!

Dashboards 130

Dashboards Reporting Metrics Visualization 130

AWS Big Data

JUNE 6, 2023

They have to gather detailed data statistics, such as minimums, maximums, averages, and correlations. They have to then review the data statistics to identify data quality rules, and write code to implement these checks in their data pipelines. This simple pricing model doesn’t lock you into annual licenses.

Data Quality 96

Data Quality Statistics Data Lake Visualization 96

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. from sklearn import metrics. Let’s also look at the basic descriptive statistics for all attributes.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 55

Machine Learning Data Science Statistics Deep Learning 55

Occam's Razor

OCTOBER 24, 2016

Step 1: Optimal Metrics. Step 4: Standard Attribution Models. Step 5: Custom Attribution Modeling. Step 6: Data-driven Attribution Modeling. Step 7: Pan-Existence Modeling. You'll find it here: Digital Metrics Ladder of Awesomeness. Step 1: Optimal Metrics. Tough metrics.

Metrics 146

Metrics Marketing Optimization Modeling 146