Domino Data Lab

MAY 15, 2019

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. If you can’t walk, you’re unlikely to run.

Management 327

Management Machine Learning Experimentation Metrics 327

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 79

Advertising Recreation/Entertainment Experimentation Enterprise 79

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Depict Data Studio

JULY 13, 2021

Data Teams are for answering questions in real life; experimental design and statistics are for answering scientific questions. ( Ask us more about Data Teams! You HAVE TO GRAPH Your Data to See How it Looks. If you are looking at disparities, what’s the risk ratio of one group having a poor outcome compared to the other?

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Cloudera

JUNE 6, 2019

In this case, an individual with different types of workloads may have access to multiple clusters; the admin trades increased risk of a noisy neighbor for better infrastructure efficiency. 3) By workload priority. A third strategy splits clusters based on the overall priority of the workloads running on those clusters.

Metadata 86

Metadata Data Lake Optimization Strategy 86

AWS Big Data

JULY 3, 2023

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Sell 1 (PVH, PVH) 2022-09-06 18321.729571 55.15

Snapshot 86

Snapshot Data Lake Testing Strategy 86

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. Yet case studies in some sense reduced risk, even if they were simply over blown marketing fluff written by the vendor. There is such little risk to actually trying. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118