O'Reilly on Data

MARCH 18, 2020

Whether it’s controlling for common risk factors—bias in model development, missing or poorly conditioned data, the tendency of models to degrade in production—or instantiating formal processes to promote data governance, adopters will have their work cut out for them as they work to establish reliable AI production lines.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two.

Unstructured Data 133

Unstructured Data Data Analytics Analytics Structured Data 133

CIO Business Intelligence

MAY 31, 2022

These new, digitally enhanced worlds, realities, and business models are poised to revolutionize both life and enterprise in the next decade, as explored in Accenture’s recent Technology Vision 2022 report. Here are five implications these technologies will have on security and privacy as we build our collective future. .

Technology 116

Technology Experimentation Interactive IoT 116

Rocket-Powered Data Science

JULY 7, 2019

Autonomous Vehicles: Self-driving (guided without a human), informed by data streaming from many sensors (cameras, radar, LIDAR), and makes decisions and actions based on computer vision algorithms (ML and AI models for people, things, traffic signs,…). Examples: Cars, Trucks, Taxis. See [link]. 2) Connected cars. (3) 5) Industry 4.0.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

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. You’re changing things fundamentally in how you build and ship software.

Management 110

Management Machine Learning Experimentation Metrics 110

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. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

AWS Big Data

JULY 3, 2023

We use Iceberg tags to label market data snapshots to avoid look-ahead bias in the research data lake, which will enable us to test various trade entry and exit scenarios and assess the respective profitability of each. He is a former data engineer and passionate about quantitative investment research.

Snapshot 87

Snapshot Data Lake Testing Strategy 87

Cloudera

JUNE 6, 2019

This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. Furthermore, both Compute Cluster 1 and Compute Cluster 2 can operate on the same copy of the data with a single set of security policies. A Better Approach: Virtual Private Clusters.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. You already have the paid analytics software combos mentioned above. Things of course get worse when you think of warehousing in traditional software only solutions. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

datapine

JANUARY 31, 2022

Your Chance: Want to try a professional BI analytics software? The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. Your Chance: Want to try a professional BI analytics software?

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214