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

AWS Big Data

MAY 19, 2023

Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS , data warehouses ( Amazon Redshift ), search ( Amazon OpenSearch Service ), NoSQL ( Amazon DynamoDB ), machine learning ( Amazon SageMaker ), and more.

Machine Learning 84

Machine Learning Metrics Big Data Management 84

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

Sisense

MAY 26, 2020

As both words are semantically close to each other, machine learning models can easily understand that “delicious” also refers to the pasta tasting good. Word embedding is a type of word representation that allows words with similar meanings to be understood by machine learning algorithms.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

datapine

MAY 17, 2021

Without a doubt, the best way to drive maximum value from the metrics, insights, and information is through something called data discovery. Studies say that more data has been generated in the last two years than in the entire history before and that since 2012 the industry has created around 13 million jobs around the world.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

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. from sklearn import metrics. It can be implemented as either unsupervised (e.g. from imblearn.over_sampling import SMOTE.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

datapine

JANUARY 6, 2022

In fact, a Digital Universe study found that the total data supply in 2012 was 2.8 Powered by technologies such as artificial intelligence and machine learning, predictive analytics practices enable businesses to spot trends or potential issues and plan informed strategies in advance. trillion gigabytes!

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Domino Data Lab

JUNE 2, 2019

Secondly, I talked backstage with Michelle, who got into the field by working on machine learning projects, though recently she led data infrastructure supporting data science teams. Just doing machine learning is not enough, and sometimes not even necessary.”. First off, her slides are fantastic! Nick Elprin.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

Smart Data Collective

APRIL 5, 2019

For example, it hones in on metrics in social media like retweets, engagement rates, mention, and story completions. Its goal was to “turn anonymous social media audiences into qualified prospects for sales and marketing” and the company raised $300,000 from a VC firm in 2012. Big Data Makes Social Listening More Viable than Ever.

Big Data 79

Big Data Interactive Data-driven Metrics 79

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. Let’s analyze text data from the party conventions during the 2012 US Presidential elections. Usually it’s human-generated text, but not always. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Cloudera

AUGUST 6, 2018

This is important because unlike diabetes or high blood pressure we don’t yet have clear metrics for Parkinson’s. Data analytics and machine learning can help organizations to automate tasks in areas like fundraising or program management, among others, and thus free up needed time and money for other activities.

Machine Learning 50

Machine Learning Internet of Things Cost-Benefit Data-driven 50

The Unofficial Google Data Science Blog

AUGUST 27, 2019

Of course, any mistakes by the reviewers would propagate to the accuracy of the metrics, and the metrics calculation should take into account human errors. If we could separate bad videos from good videos perfectly, we could simply calculate the metrics directly without sampling. The missing verdicts create two problems.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

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”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What metrics are used to evaluate success? Introduction.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

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. From a startup in 2012, it is now valued at $3.2 Kyle said: We empower data analysts to create more business value than any other BI platform.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Insight

JANUARY 14, 2020

CNNs have been widely considered state-of-the-art tools for computer vision since 2012, when AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). We pass 3 parameters: loss, optimizer , and metrics. Choosing your evaluation metric Lastly, metrics just refers to your choice of evaluation metric.

Metrics 58

Metrics Optimization Modeling Testing 58

The Unofficial Google Data Science Blog

JUNE 30, 2016

The field of statistical machine learning provides a solution to this problem, allowing exploration of larger spaces. In fact, Hainmueller (2012) show that entropy balancing is equivalent to estimating the weights as a log-linear model of the covariate functions $c_j(X)$. Machine learning 45.1 Machine learning 20.3

Statistics 44

Statistics Optimization Modeling Experimentation 44