Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. These data scientists require the flexibility to use a constantly-evolving software and hardware stack to optimize each step of their model lifecycle. Reflections. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

Insight

MARCH 27, 2019

It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. To address this problem, Google published AutoAugment last year, which discovers optimized augmentations for the given dataset using reinforcement learning. 2018 , blog ). Be modular and user-friendly.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

CIO Business Intelligence

MARCH 6, 2023

Despite all three teams reporting up a single leader, each team optimized for itself, resulting in individual stovepipes and technology inefficiencies, Stone says. Moreover, the differences between each stovepipe also meant more work for security teams trying to manage and mitigate risks.

IT 132

IT Strategy Cost-Benefit Consulting 132

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

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

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. Pierre-Yves Oudeyer (2018). This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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

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) is unchanged from Strata NY 2018, it’s up three places from Strata NY 2017—and eight places relative to 2016. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Occam's Razor

JULY 24, 2018

The ability to optimize landing pages. At the top-right, you’ll discover my obsession with Profit and Incrementality, which form the basis of competitive advantage in 2018 (and beyond). Ignore the metrics produced as an experimental exercise nine months ago. So, how do we ensure that each has an optimal analytical approach?

Analytics 93

Analytics Metrics Strategy Measurement 93

Domino Data Lab

MAY 8, 2019

2018-06-21). They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Perhaps if machine learning were solely being used to optimize advertising or ecommerce, then Agile-ish notions could serve well enough. Challenges for Transparency ”.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 22, 2019

You can home in on an optimal value by specifying, say, 32 dimensions and varying this value by powers of 2. If we were using CBOW, then a window size of 5 (for a total of 10 context words) could be near the optimal value. Note: This 5,000-word threshold may not be optimal, but we didn’t take the time to test lower or higher values.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111