AWS Big Data

SEPTEMBER 11, 2024

Hewlett-Packard acquired Aruba Networks in 2015, making it a wireless networking subsidiary with a wide range of next-generation network access solutions. With 14 years of experience in the IT industry, Ritesh has a strong background in Data Analytics, Data Management, Big Data systems and Machine Learning.

Data Architecture 86

Data Architecture Optimization Data Warehouse Metadata 86

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. See Hainmueller (2012), and the work of Zhao & Percival (2015) for more details on how this optimization problem is solved, and for further discussion. Machine learning 45.1 Machine learning 20.3

Statistics 44

Statistics Optimization Modeling Experimentation 44

Alation

FEBRUARY 20, 2020

Combine machine learning, crowdsourcing, and stewardship by experts to optimize both quantity and quality of information. . If I see a metric in a report, I want to be able to look it up, even if it shouldn’t have been used in the first place. vs. Machine-Learning. Inventory vs. Catalog. Context is King.

Metrics 52

Metrics Consulting Machine Learning Statistics 52

CIO Business Intelligence

AUGUST 12, 2022

With its business rapidly growing and customer expectations rising, Thermo Fisher Scientific is turning to machine learning and robotic process automation (RPA) to transform the customer experience. in 2013, Alfa Aesar in 2015, Affymetrix and FEI Co. in 2016, and BD Advanced Bioprocessing in 2018. Catalyzing change.

IT 105

IT Data Lake Sales Interactive 105

Sisense

FEBRUARY 24, 2021

But that number rose sharply afterwards, with the team noting there were over 1,000 people in this role by 2015. The right analytics tool will empower the chief data officer to play defense by improving metrics that affect the bottom line and go on offense by generating revenue by monetizing analytics.

Data Warehouse 69

Data Warehouse Big Data Metrics Visualization 69

AWS Big Data

JULY 26, 2023

The vector engine provides a simple, scalable, and high-performing similarity search capability in Amazon OpenSearch Serverless that makes it easy for you to build modern machine learning (ML) augmented search experiences and generative artificial intelligence (AI) applications without having to manage the underlying vector database infrastructure.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

Insight

JUNE 10, 2019

Photo by Wayne Chan on Unsplash The race to the future I don’t know about you, but I distinctly remember a promise from 1989 that flying cars would be commonplace by 2015. It can be quantitatively defined as: We can then combine these useful metrics with a confusion matrix to provide a powerful summary of the performance of the model.

Breakeven 69

Breakeven Modeling Testing Marketing 69

Domino Data Lab

FEBRUARY 4, 2019

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. I don’t have a metric to estimate the time it takes to change company culture because that’s what we call a very small dataset.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

IBM Big Data Hub

FEBRUARY 22, 2024

In 2013, IBM embarked on the journey of explainability and transparency in AI and machine learning. IBM is a leader in AI ethics, appointing an AI ethics global leader in 2015 and creating an AI ethics board in 2018. These experts work to help ensure our principles and commitments are upheld in our global business engagements.

Metadata 91

Metadata Measurement Risk Modeling 91

AWS Big Data

FEBRUARY 1, 2023

Furthermore, it created further opportunities for our data scientists and engineers to use AI and machine learning (ML) services to continuously feed and analyze data. AWS Glue Data Catalog The Data Catalog helped us maintain the catalog as an index to the data’s location, schema, and runtime metrics in Amazon S3.

Optimization 52

Optimization Forecasting Data Lake Metadata 52

Rita Sallam

NOVEMBER 11, 2016

Machine learning-enabled smart recommendations will surface data sets and ultimately content based on user and user cohort group behavior. Users will also be able to define and monitor metrics and alerts to changes based on defined or dynamic thresholds.

Visualization 49

Visualization Metrics Machine Learning Enterprise 49

Domino Data Lab

JULY 1, 2021

statistical model-based techniques – Using Machine Learning we can streamline and simplify the process of building NER models, because this approach does not need a predefined exhaustive set of naming rules. The process of statistical learning can automatically extract said rules from a training dataset. nunique())).

Modeling 111

Modeling Statistics Testing Metrics 111

CIO Business Intelligence

MAY 17, 2022

Around 2015, Capital One went to AWS re:Invent and set forth our aspirational goal to modernize our entire technology infrastructure. Specifically for our customer data platform, there are two metrics that we’ve actively tracked: recovery point and recovery time objectives. How did you cope with this change?

ROI 52

ROI Risk Marketing Modeling 52

Insight

NOVEMBER 5, 2019

Collected over two years (2014 and 2015) from ICUs across the US, it contains an enormous amount of data about more than 200,000 patient-stays in the ICU. The Data: Preprocessing & Feature Engineering Overview of the Data I used the eICU Collaborative Research Database for this project. I had a lot of fun on this project.

Deep Learning 61

Deep Learning Modeling Insurance Metrics 61

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. 2015) for additional details. See Wei et al. Bahdanau, D.,

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Smart Data Collective

JUNE 26, 2019

Get Rid of Blind Spots in Statistical Models With Machine Learning. RiskSpan is a company that built a machine learning algorithm that can flag error-prone parts of a statistical model and indicate which associated outputs may be unreliable. This way of using machine learning is still in its early stages.

ROI 100

ROI Statistics Machine Learning Measurement 100

CIO Business Intelligence

JUNE 5, 2024

In 2015, Major League Baseball revolutionized a sport already known for its sophisticated use of data with MLB Statcast, a tracking technology that collects enormous amounts of game data. Not only do we have the traditional ball tracking metrics like velocity and spin rate, we’ve also got player positioning data,” Booth says.

Optimization 125

Optimization Predictive Modeling Data-driven Modeling 125

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. You could cluster (k=2) on NPS scores (a customer evaluation metric) then replace the Democrat/Republican dimension with the top two components from the clustering. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110