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 58

Data Science Machine Learning Cost-Benefit Deep Learning 58

Cloudera

FEBRUARY 25, 2021

Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses. The work of a machine learning model developer is highly complex. We work with the largest companies in the world to help tackle their most challenging ML problems.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

Cloudera

DECEMBER 3, 2021

In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI. 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. We need to get to the root of the problem. Model Drift. Find out more.

Uncertainty 103

Uncertainty Measurement Metrics Risk 103

Analytics Vidhya

DECEMBER 16, 2020

This article was published as a part of the Data Science Blogathon. Introduction There are so many performance evaluation measures when it comes to. The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya.

Measurement 295

Measurement Modeling Data Science Publishing 295

IBM Big Data Hub

AUGUST 7, 2023

2,3 When clinical trials are prematurely discontinued due to trial site underperformance, the research questions remain unanswered and research findings end up not published. AI algorithms have the potential to surpass traditional statistical approaches for analyzing comprehensive recruitment data and accurately forecasting enrollment rates.

Optimization 60

Optimization Forecasting Data-driven Strategy 60

Cloudera

JUNE 28, 2022

This blog will be published in two parts. 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. This is what we call the first-mile problem.

Dashboards 86

Dashboards Machine Learning Statistics KPI 86

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. He was saying this doesn’t belong just in statistics. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. often without even looking at it. In other words, Galbraith had chops?—?but

Modeling 59

Modeling Metadata Publishing Sales 59

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

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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 90

Optimization Metrics Interactive Data Integration 90

AWS Big Data

JUNE 28, 2023

The data becomes available in Amazon Redshift within seconds, allowing users 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 81

Data Warehouse Analytics Metrics Dashboards 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 83

Data Warehouse Metrics Statistics Optimization 83

Domino Data Lab

AUGUST 10, 2021

At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. There are various types of machine learning approaches, including: Supervised learning: used when you know what the model needs to learn, typically in prediction, regression or classification.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

MAY 8, 2019

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. We will follow the example of Janson and Olsson , and start from this generalized definition of the metric, which they call iota.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

IBM Big Data Hub

JULY 28, 2022

To take full advantage metadata enrichments, you’ll want to publish data assets, enriched with metadata‚ to a catalog where users can quickly find and access the right information. The workflow and user experience for discovering, enriching and publishing a large number of data assets from a source to a catalog has been entirely revisited.

Metadata 57

Metadata Data Quality Machine Learning Statistics 57

DataRobot

NOVEMBER 9, 2021

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. The dataset also provides centralized and windowed statistics features. More than a hundred years later, this phrase still rings true, especially for data scientists.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

In conferences and research publications, there is a lot of excitement these days about machine learning methods and forecast automation that can scale across many time series. Nor can we learn prediction intervals across a large set of parallel time series, since we are trying to generate intervals for a single global time series.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

datapine

AUGUST 29, 2022

And with that understanding, you’ll be able to tap into the potential of data analysis to create strategic advantages, exploit your metrics to shape them into stunning business dashboards , and identify new opportunities or at least participate in the process. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

AWS Big Data

SEPTEMBER 13, 2023

AWS has invested in native service integration with Apache Hudi and published technical contents to enable you to use Apache Hudi with AWS Glue (for example, refer to Introducing native support for Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue for Apache Spark, Part 1: Getting Started ).

Data Lake 87

Data Lake Data Processing Metadata Snapshot 87

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. We had big surprises at several turns and have subsequently published a series of reports. In order, those are: “5 findings from O’Reilly’s machine learning adoption survey companies should know”. “

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Sign up to learn more about the Insight Fellows programs and start your application today.

Risk 67

Risk ROI Modeling Metrics 67

Sisense

DECEMBER 11, 2019

He outlined how critical measurable results are to help VCs make major investment decisions — metrics such as revenue, net vs gross earnings, sales , costs and projections, and more. Kyle said: We empower data analysts to create more business value than any other BI platform. Kyle Dempsey, Senior Sales Engineer, Sisense.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

O'Reilly on Data

JANUARY 25, 2021

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. AI, Machine Learning, and Data. Artificial intelligence, machine learning, and data.

Machine Learning 363

Machine Learning Software Testing Technology 363

Andrew White

JANUARY 11, 2021

Gartner also published the same piece of research for other roles, such as Application and Software Engineering. The work of data science is more tied to machine learning and so AI and those projects do not focus only on analysis but also automation. We publish research for small organizations, not just larger organizations.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. Monitoring tools track performance metrics such as latency, throughput, and error rates. What is an ETL pipeline?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 1, 2023

As a result, end users can better view shared metrics (backed by accurate data), which ultimately drives performance. When treating a patient, a doctor may wish to study the patient’s vital metrics in comparison to those of their peer group. They can also create custom calculations and metrics, and build new data visualizations.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52