Domino Data Lab

JUNE 13, 2019

Many data scientists and researchers have used the MNIST test set of 10,000 samples for training and testing models for over 20 years. 2018 , 2019 ], the rediscovery of the 50,000 lost MNIST test digits provides an opportunity to quantify the degradation of the official MNIST test set over a quarter-century of experimental research.”

Testing 83

Testing Data Science Experimentation Metadata 83

Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. These solutions help data analysts build models by automating tasks in data science, including training models, selecting algorithms, and creating features. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Insight

MARCH 27, 2019

The main benefits and features of DeepAugment are: Reduces the error rate of CNN models (showed 60% decrease in error for CIFAR10 on WRN-28–10) Saves time by automating the process 50 times faster than Google’s previous solution– AutoAugment The finished package is on PyPI. 2018 , blog ). To learn more about how I built this, read on!

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

Cloudera

AUGUST 2, 2018

Data teams in these insurance firms are leading the charge in rebuilding entire business models around data and analytics. Nevertheless, we do recognize the maturity in features developed around Traditional Data Warehouses that are still very relevant, such as, data modeling, governance, security, and workload System Level Agreements.

Data Warehouse 51

Data Warehouse Insurance Experimentation Software 51

Data Science and Beyond

JULY 22, 2018

This article is a short summary of my understanding of the definition of data science in 2018. Kaggle was only about predictive modelling competitions back then, and so I believed that data science is about using machine learning to build models and deploy them as part of various applications. But what does it mean?

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Business Over Broadway

MAY 22, 2019

For those of you who are interested, here is Gartner’s latest (2018) hype cycle on emerging technologies. According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

CIO Business Intelligence

NOVEMBER 4, 2022

When Bob McCowan was promoted to CIO at Regeneron Pharmaceuticals in 2018, he had previously run the data center infrastructure for the $81.5 Empowering scientists through the cloud McCowan set about migrating Regeneron to Amazon Web Services in late 2018. If you are not on the cloud, you are going to be left behind.”

Data Lake 124

Data Lake IT Experimentation Data-driven 124

Occam's Razor

JULY 24, 2018

work (collection, processing, reporting, analysis), processes, org structure, governance models, last-mile gaps , metrics ladders of awesomeness , and… so… much… more. Remember, tools, work, processes, org structure, governance models, last-mile gaps, metrics ladders of awesomeness, and… so… much… more. The Implications of Complexity.

Analytics 93

Analytics Metrics Strategy Measurement 93

Occam's Razor

JUNE 4, 2018

It is 2018—why are there still light gray below-the-fold add to cart buttons? Not easy, but your business model has to change to survive.). Although it may seem impossible to imagine with ecommerce already totaling up to 5% of overall commerce, there’s astronomical growth still to come. youarekillingme.

Strategy 124

Strategy Cost-Benefit Testing Sales 124

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Program Synthesis Papers at ICLR 2018 ” – Illia Polosukhin (2018-05-01). Introduction.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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. Ensure a culture that supports a steady process of learning and experimentation. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. Ontotext develops re-usable domain models as pre-packaged knowledge graphs.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

Ontotext

DECEMBER 30, 2022

9 years of research, prototyping and experimentation went into developing enterprise ready Semantic Technology products. In 2019 the market for graph databases and knowledge graphs started heating up – appearing on Gartner’s hype curves in 2018. The first 18 years: Develop vision and products and deliver to innovation leaders.

Enterprise 52

Enterprise Sales Cost-Benefit Marketing 52

O'Reilly on Data

SEPTEMBER 11, 2019

So, we used a form of the Term Frequency-Inverse Document Frequency (TF/IDF) technique to identify and rank the top terms in this year’s Strata NY proposal topics—as well as those for 2018, 2017, and 2016. 2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. 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. 2018-06-21). Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

CIO Business Intelligence

AUGUST 1, 2023

During keynotes and discussions with CIOs, I remind everyone how strategic priorities evolve significantly every two years or less, from growth in 2018, to pandemic and remote work in 2020, to hybrid work and financial constraints in 2022. Customizing and developing LLMs requires a strong business case, technical expertise, and funding.

Digital Transformation 98

Digital Transformation Unstructured Data Strategy Experimentation 98

O'Reilly on Data

MAY 11, 2020

Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. To understand the menagerie of models that are fundamentally altering our individual and shared realities, we need to build a typology, a classification of their effects and impacts.

Modeling 190

Modeling Advertising Recreation/Entertainment Metrics 190