Big Data Data Collection Experimentation Modeling 
Rocket-Powered Data Science
JULY 6, 2021
2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.
IBM Big Data Hub
MARCH 27, 2024
This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Figure 1 illustrates the framework for Scope 3 emission estimation employing a large language model.
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CIO Business Intelligence
MARCH 21, 2022
A data scientist’s chief responsibility is data analysis, which begins with data collection and ends with business decisions based on analytic results. The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data. Data scientist skills.
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]. Industry 4.0 2) Connected cars. (3)
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. AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Conversations suggest that AI is already transforming most major industries.
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).
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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.
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