Remove 2015 Remove Experimentation Remove Risk
article thumbnail

6 enterprise DevOps mistakes to avoid

CIO Business Intelligence

Driven by the development community’s desire for more capabilities and controls when deploying applications, DevOps gained momentum in 2011 in the enterprise with a positive outlook from Gartner and in 2015 when the Scaled Agile Framework (SAFe) incorporated DevOps.

article thumbnail

Transforming IT from cost center to catalyst

CIO Business Intelligence

In 2015, we attempted to introduce the concept of big data and its potential applications for the oil and gas industry. As we navigate this terrain, it’s essential to consider the potential risks and compliance challenges alongside the opportunities for innovation. Risk management is essential, but it shouldn’t stifle innovation.

IT 122
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. Without better methodology, difficult-to-treat and ill-understood conditions and diseases are at risk of staying that way. Unfortunately, a substantial number of clinical trials fails in these two Phases.

article thumbnail

AI on the mainframe? IBM may be onto something

CIO Business Intelligence

billion in 2015 to less than $6.5 It’s a natural fit and will be interesting to see how these ensemble AI models work and what use cases will go from experimentation to production,” says Dyer. LLMs can drive significant insights in compliance, regulatory reporting, risk management, and customer service automation in financial services.

article thumbnail

Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. Our experimentation platform supports this kind of grouped-experiments analysis, which allows us to see rough summaries of our designed experiments without much work.

article thumbnail

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. For example, imagine a fantasy football site is considering displaying advanced player statistics.

article thumbnail

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

For more background about program synthesis, check out “ Program Synthesis Explained ” by James Bornholt from 2015, as well as the more recent “ Program Synthesis in 2017-18 ” by Alex Polozov from 2018. For details, see their SIGMOD 2015 paper where Michael Armbrust & co. This field is guaranteed to get interesting. SQL and Spark.

Metadata 105