O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

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 132

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 132

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

CIO Business Intelligence

JUNE 5, 2023

By integrating remote working capabilities, the CIO plays a hand in an organization’s shift to an increasingly remote or hybrid workforce model—a move that can significantly reduce a company’s carbon footprint. This opportunity to collaborate with sustainability in mind extends to the influence CIOs hold over where and how employees work.

Software 104

Software Measurement Visualization Business Objectives 104

AWS Big Data

FEBRUARY 26, 2024

When you start the process of designing your data model for Amazon Keyspaces, it’s essential to possess a comprehensive understanding of your access patterns, similar to the approach used in other NoSQL databases. It empowers businesses to explore and gain insights from large volumes of data quickly. Choose the Test tab.

Dashboards 66

Dashboards Testing Metrics Optimization 66

DataRobot

OCTOBER 20, 2021

Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

CIO Business Intelligence

MAY 16, 2023

AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data. This helps to ensure accuracy and neutrality.

Advertising 95

Advertising Recreation/Entertainment Experimentation Enterprise 95

datapine

JANUARY 6, 2022

Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

CIO Business Intelligence

SEPTEMBER 8, 2022

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

Cost-Benefit 117

Cost-Benefit Internet of Things Data-driven Data Lake 117

IBM Big Data Hub

FEBRUARY 23, 2024

DL models can improve over time through further training and exposure to more data. When a user sends a message, the system uses NLP to parse and understand the input, often by using DL models to grasp the nuances and intent. Marketing and sales: Conversational AI has become an invaluable tool for data collection.

Enterprise 105

Enterprise Interactive Data-driven Key Performance Indicator 105

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications. How the models are stored.

Data Science 58

Data Science Machine Learning Cost-Benefit Deep Learning 58

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. Last, we’ll provide a case study of how xRR can be used to measure improvements in a data-labeling platform.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

CIO Business Intelligence

FEBRUARY 13, 2023

Larger is Better involves a “lower specification limit,” such as test scores — where the target is 100%. Measure performance of the current process by establishing a data collection plan to determine defects and gather metrics. Improve the process by eliminating root causes of defects through innovative solutions.

Management 84

Management Statistics Manufacturing Measurement 84

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. It is similar to R&D. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

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

AWS Big Data

NOVEMBER 15, 2023

Each CDH dataset has three processing layers: source (raw data), prepared (transformed data in Parquet), and semantic (combined datasets). It is possible to define stages (DEV, INT, PROD) in each layer to allow structured release and test without affecting PROD.

Dashboards 99

Dashboards Analytics Metadata Data Warehouse 99

Jet Global

SEPTEMBER 29, 2021

How Finance Leaders Use Data to Support Decision-Making. Data-driven Finance leaders use metrics to look at what the business should do next, rather than only look back at how well they performed in the past. In response to changing circumstances, real-time data is used to understand variances and isolate issues.

Data-driven 52

Data-driven Finance Data Collection Metrics 52

Juice Analytics

MARCH 30, 2021

(3) Data Product Ecosystem To enable the flow of information and the creation and sharing of data products, you need standards, tools, and processes in place. A good example is what Apple did with the App Store in creating a platform and standards by which apps are created, tested, distributed, and reviewed.

Cost-Benefit 131

Cost-Benefit Statistics Metrics Dashboards 131

The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Description : What’s the objective interpretation of this data?

Metrics 107

Metrics Measurement Statistics Data Collection 107

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. We must correct for multiple hypothesis tests. We ought not dredge our data.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Insight

JANUARY 14, 2020

a “pre-trained” model) to be recycled and reused for many different tasks. In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. We pass 3 parameters: loss, optimizer , and metrics.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Further, imbalanced data exacerbates problems arising from the curse of dimensionality often found in such biological data. Their tests are performed using C4.5-generated return synthetic.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Smart Data Collective

APRIL 2, 2019

Data lakes were designed to be agile and provide analytics data on the fly while processing incoming data at a remarkable speed. There were a handful of problems that bogged the system down and made it extremely difficult for data scientists to replicate their test bed results in a real-world environment.

Data Lake 83

Data Lake Big Data OLAP Testing 83

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. Who builds their models? Or something.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Ontotext

APRIL 4, 2019

Within a large enterprise, there is a huge amount of data accumulated over the years – many decisions have been made and different methods have been tested. Milena Yankova : We help the BBC and the Financial Times to model the knowledge available in various documents so they can manage it. What exactly do you do for them?

Recreation/Entertainment 76

Recreation/Entertainment Testing Enterprise Knowledge Discovery 76

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Gathering the Data. test: py.test --nbval notebooks/*.ipynb.

Statistics 59

Statistics Machine Learning Testing Modeling 59

datapine

SEPTEMBER 1, 2020

Your Chance: Want to test a market research reporting software? An effective modern means of extracting real value from your research results such as brand analysis, market research reports present and arrange data in a way that is digestible and logical in equal measures through professional online reporting software and tools.

Reporting 247

Reporting Marketing KPI Dashboards 247

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. Under modern day reporting standards, companies are formally obligated to present their financial data in the following statements: balance sheet, income statement, and cash flow statement.

Metrics 98

Metrics Finance Reporting KPI 98

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

JULY 18, 2011

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. That means: All of these metrics are off.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Cloudera

AUGUST 17, 2020

For this step, we used Spark ML in CML ( Cloudera Machine Learning ) to train a Naïve Bayes model to identify what a technical sentence looks like in the context of a case comment. Data Collection Request – A sentence requesting data from the customer. Testing the Results. Extract Technical Sentences.

Testing 84

Testing Machine Learning Interactive Metrics 84

Alation

OCTOBER 12, 2022

It is important to take into account that not all numeric data can be part of a calculation or aggregated. These terms that cannot aggregate, like a percentage, are often called non-aggregatable metrics. What Is Quantitative Data Analysis? What Is the Role of Statistics in Quantitative Data Analysis?

Statistics 52

Statistics Sales Testing Marketing 52

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

datapine

JUNE 13, 2019

So they taste test frequently throughout the whole process. They can be of various forms: a daily sales report format will track sales metrics that are relevant on a daily basis: the number of phone calls or meetings set up by a rep, number of leads created. Instead, you want to focus more on process metrics.

Sales 264

Sales Reporting KPI B2B 264