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. He entered the big data space in 2013 and continues to explore that area. This is where the Retrieval Augmented Generation (RAG) technique comes in.

Data Processing 132

Data Processing Dashboards Machine Learning Metrics 132

AWS Big Data

NOVEMBER 14, 2023

Wallapop’s initial data architecture platform Wallapop is a Spanish ecommerce marketplace company focused on second-hand items, founded in 2013. Since its creation in 2013, it has reached more than 40 million downloads and more than 700 million products have been listed. The marketplace can be accessed via mobile app or website.

Data Warehouse 64

Data Warehouse Analytics Testing Cost-Benefit 64

Domino Data Lab

DECEMBER 1, 2019

In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance. Step 4: Generate the test, train and noisy MNIST data sets. Generate the train and test sets (x_train, _), (x_test, _) = mnist.load_data() x_train = x_train.astype('float32') / 255.

Machine Learning 77

Machine Learning Modeling Deep Learning Testing 77

bridgei2i

JULY 28, 2020

It is my immense pleasure to introduce you all to our guest today Ria Persad, she’s named as international woman of the year by Renewable Energy World in power engineering in 2013 and the lifetime achievement leader by Platts Global Energy awards in 2014. We need people who can test.

Enterprise 98

Enterprise Digital Transformation Insurance B2B 98

Howard Dresner

FEBRUARY 11, 2014

Collaboration BI At one of my weekly #BIWisdom tweetchats this month, collaboration, social media and text analytics turned up in a discussion about 2013 BI predictions that didn’t pan out. Vendors need to automate and decrease that effort.” • “I tested a social analytics tool; I was less than impressed.

Analytics 45

Analytics Business Intelligence Data Processing Machine Learning 45

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. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Cloudera

AUGUST 6, 2018

For example, Crisis Text Line , which provides online support to people in crisis, received a total of 8 m illion text messages in the first two years of its existence between 2013 and 2015. Fox Foundation is testing a watch-type wearable device in Australia to continuously monitor the symptoms of patients with Parkinson’s disease.

Machine Learning 50

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

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

by OMKAR MURALIDHARAN Many machine learning applications have some kind of regression at their core, so understanding large-scale regression systems is important. But most common machine learning methods don’t give posteriors, and many don’t have explicit probability models. Figure 4 shows the results of such a test.

KDD 40

KDD Testing Machine Learning Measurement 40

Domino Data Lab

AUGUST 22, 2019

word2vec is an unsupervised learning technique—that is, it is applied to a corpus of natural language without making use of any labels that may or may not happen to exist for the corpus. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. Note: Mikolov, T.,

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

BizAcuity

MAY 10, 2022

2013: Google launches Google Compute Engine (IaaS), its own version of EC2. Microsoft launches Azure ML Studio for machine learning capabilities on the cloud. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models. Google releases Kubernetes.

Data-driven 52

Data-driven IoT Unstructured Data Data Lake 52

The Unofficial Google Data Science Blog

APRIL 23, 2024

Multiparameter experiments, however, generate richer data than standard A/B tests, and automated t-tests alone are insufficient to analyze them well. We use PrePost in most of our A/B tests, so we have pre-experiment metric measurements readily available that we can use as covariates in our models. Springer Netherlands, 2013. [16]

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Jen Stirrup

FEBRUARY 1, 2023

The effects of AI will be magnified in the coming decade as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

Recreation/Entertainment 64

Recreation/Entertainment Testing Advertising Insurance 64

IBM Big Data Hub

FEBRUARY 22, 2024

Reliable “The Department’s AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire life cycles.” This is misguided. But it is well worth the effort.

Metadata 91

Metadata Measurement Risk Modeling 91

Insight

MARCH 12, 2020

In 2013, Robert Galbraith?—?an 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 Test Case: Dune Let’s see an example of genre tag prediction in action.

Modeling 59

Modeling Metadata Publishing Sales 59

Jet Global

MAY 1, 2023

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve. Users’ varied needs require a shift in traditional BI thinking. Their dashboards were visually stunning.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52

CIO Business Intelligence

MARCH 26, 2025

The combination of AI and search enables new levels of enterprise intelligence, with technologies such as natural language processing (NLP), machine learning (ML)-based relevancy, vector/semantic search, and large language models (LLMs) helping organizations finally unlock the value of unanalyzed data.

Knowledge Discovery 84

Knowledge Discovery Cost-Benefit Enterprise Modeling 84