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). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

IBM Big Data Hub

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 84

Data Science Data Analytics Prescriptive Analytics Analytics 84

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. genetic counseling, genetic testing). underspecified) due to omitted metrics. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

DataKitchen

JANUARY 19, 2022

Imagine a large enterprise yielding significant value from their Matillion-Snowflake integration, but wishing to expand the scope of data pipeline deployment, testing, and monitoring. DataKitchen triggers a Matillion job, then retrieves execution parameters that can be used in DataKitchen tests. Unifying Heterogeneous Toolchains.

Testing 130

Testing Data Integration Data Warehouse Enterprise 130

AWS Big Data

MARCH 27, 2024

By testing the solution against key metrics, a POC provides insights that allow you to make an informed decision on the suitability of the technology for the intended use case. Complete the implementation tasks such as data ingestion and performance testing. Collect data metrics and statistics on the completed tasks.

Testing 98

Testing Data Warehouse Metrics Cost-Benefit 98

IBM Big Data Hub

AUGUST 9, 2023

A phishing simulation is a cybersecurity exercise that tests an organization’s ability to recognize and respond to a phishing attack. After the simulation, organizations also receive metrics on employee click rates and often follow up with additional phishing awareness training. million phishing sites.

Testing 80

Testing Measurement Behavioral Analytics Reporting 80

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. 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. What is MLOps?

Data Science 57

Data Science Machine Learning Cost-Benefit Deep Learning 57

Occam's Razor

MARCH 23, 2021

A key part of how this manifested in our work was doing truly super-advanced machine-learning powered analysis to answer hard questions that few can successfully. The first component is a gloriously scaled global creative pre-testing program. Matched market tests. The benchmark for the beautiful metric AVOC is 15.3%.

Analytics 131

Analytics Scorecard Strategy Testing 131

Ontotext

JUNE 22, 2023

Recent statistics shed light on the realities in the world of current drug development: out of about 10,000 compounds that undergo clinical research, only 1 emerges successfully as an approved drug. The current process involves costly wet lab experiments, which are often performed multiple times to achieve statistically significant results.

Metrics 52

Metrics Statistics Visualization Metadata 52

datapine

JANUARY 6, 2022

More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Let’s quickly review the most common statistical terms: Mean: a mean represents a numerical average for a set of responses. Standard deviation: this is another statistical term commonly appearing in quantitative analysis.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Birst BI

AUGUST 23, 2018

A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. 1] Until then, we observe in another Gartner survey that organizations are outsourcing various tasks in the Machine Learning pipeline. [2].

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

FineReport

DECEMBER 9, 2021

Untracked metrics: data you will not track. This will be a subset of the metrics you track. This is a short project that validates the technical concept by proposing many simplified functions, verifying hypotheses, testing the functionality of the product, and ensuring that the concept can be implemented or vice versa.

Strategy 98

Strategy Business Intelligence KPI Key Performance Indicator 98

Cloudera

DECEMBER 3, 2021

There are a number of metrics that can be used to measure the performance of a system; they include accuracy, precision and F-score to name only three. Tracking key metrics and statistical distributions over time and alerting humans when either of these significantly drift can ensure that systems remain performant and fair.

Uncertainty 98

Uncertainty Measurement Metrics Risk 98

DataRobot

APRIL 20, 2021

The performance of any machine learning model is tightly linked to the data it was trained on and validated against. This includes the basics: Computing summary statistics on each feature Measuring associations between features Observing feature distributions and their correlation with the predictive target Identifying outliers.

Measurement 52

Measurement Visualization Advertising Metrics 52

Cloudera

JUNE 28, 2022

For each transaction, NiFi makes a call to a production model in Cloudera Machine Learning (CML) to score the fraud potential of the transaction. A dashboard feeds from the Kudu database to show fraud summary statistics. The streaming SQL job also saves the fraud detections to the Kudu database. Acquiring with Cloudera DataFlow.

Dashboards 81

Dashboards Machine Learning Statistics KPI 81

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Data science is an interdisciplinary field within STEM that combines math and statistics, computer science, and business acumen to find patterns in complex datasets that can be used to improve decision making. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

AWS Big Data

FEBRUARY 6, 2023

Let’s look at some key metrics. Based on the statistics of individual and aggregated application runs per queue and per user, you can determine the existing workload distribution by user. After analyzing YARN logs by various metrics, you’re ready to design future EMR architectures. You can find peak and off-peak hours in a day.

Dashboards 73

Dashboards Optimization Data Lake Cost-Benefit 73

CIO Business Intelligence

JULY 20, 2022

The 2022 State of the CIO research confirmed talent acquisition and retention strategies are a key issue for CIOs, cited by 38% of respondents, with cybersecurity skills, data science/analytics, and artificial intelligence (AI) and machine learning (ML) in top demand. It’s death by 1,000 paper cuts. That’s the real issue.”.

IT 131

IT Software Cost-Benefit Strategy 131

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Domino Data Lab

JULY 17, 2021

NOTE : The scope of this blog is a machine learning approach to TISV. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. VoxCeleb1 dev test # of speakers 1,211 40 # of videos 21,819 677 # of utterances 148,642 4,874.

Modeling 64

Modeling Testing Machine Learning Measurement 64

Domino Data Lab

DECEMBER 4, 2020

Having calculated AUC/AUMC, we can further derive a number of useful metrics like: Total clearance of the drug from plasma. Once all packages have been imported, we can move on to loading our test data. We can now pass the preprocessed data to the Pumas NCAReport function, which calculates a wide range of relevant NCA metrics.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

BizAcuity

MAY 16, 2022

Fast food companies like Domino’s, McDonald’s and KFC collect massive amounts of data which includes customer data and other key business metrics for their own operations. His name was William Gosset and he is credited to have developed the student t-test. Almost all restaurants and cafes religiously follow this practice.

Business Intelligence 52

Business Intelligence Data mining Visualization Data Lake 52

Sisense

SEPTEMBER 25, 2020

A data scientist must be skilled in many arts: math and statistics, computer science, and domain knowledge. Statistics and programming go hand in hand. Mastering statistical techniques and knowing how to implement them via a programming language are essential building blocks for advanced analytics. Linear regression.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

DataKitchen

MAY 10, 2021

Some will argue that observability is nothing more than testing and monitoring applications using tests, metrics, logs, and other artifacts. Below we will explain how to virtually eliminate data errors using DataOps automation and the simple building blocks of data and analytics testing and monitoring. .

Testing 214

Testing Manufacturing Data Quality Statistics 214

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve way to solve this problem would be to compare the proportion of buyers between the exposed and unexposed groups, using a simple test for equality of means. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies. What is MMM? Data Requirements. Request a demo.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Insight

MARCH 12, 2020

The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. I tested several different flavors of BERT for use as synopsis classifiers before settling on the DistilBERT model from Hugging Face. On my test set, this approach resulted in~75–95% accuracy and ~.65

Modeling 59

Modeling Metadata Publishing Sales 59

Alation

OCTOBER 12, 2022

These terms that cannot aggregate, like a percentage, are often called non-aggregatable metrics. Traditional business analysis uses numerical methods to paint a picture, often through numerical methods, like statistics. What Is the Role of Statistics in Quantitative Data Analysis? What Is Quantitative Data Analysis?

Statistics 52

Statistics Sales Testing Marketing 52

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

AWS Big Data

MARCH 13, 2024

RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. Each service implements k-nearest neighbor (k-NN) or approximate nearest neighbor (ANN) algorithms and distance metrics to calculate similarity.

Data Processing 110

Data Processing Dashboards Machine Learning Management 110

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

AWS Big Data

MARCH 11, 2024

Managed Service for Apache Flink manages the underlying infrastructure and Apache Flink components that provide durable application state, metrics, logs, and more. However, it’s crucial to recognize that this reduction could adversely affect performance if not thoroughly assessed and tested.

Management 96

Management Snapshot Metrics Cost-Benefit 96

datapine

SEPTEMBER 6, 2022

A content dashboard is an analytical tool that contains critical performance metrics to assess the success of all content-related initiatives. This most insightful of Google Analytics dashboards provide the perfect mix between overview metrics and more detailed insights. Let’s look at some of these metrics in more detail below.

Dashboards 179

Dashboards Reporting ROI Metrics 179

AWS Big Data

APRIL 28, 2023

AWS Glue is a serverless data integration service that makes it simple to discover, prepare, and combine data for analytics, machine learning (ML), and application development. For example, you can configure an Amazon EventBridge rule to invoke an AWS Lambda function to publish CloudWatch metrics every time AWS Glue jobs finish.

Optimization 94

Optimization Metrics Interactive Data Integration 94