Rocket-Powered Data Science

MAY 21, 2024

To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.” RAG is the essential link between two things: (a) the general large language models (LLMs) available in the market, and (b) a specific organization’s local knowledge base.

Cost-Benefit 236

Cost-Benefit Unstructured Data Enterprise Technology 236

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

KDnuggets

JANUARY 30, 2023

The ChatGPT Cheat Sheet • ChatGPT as a Python Programming Assistant • How to Select Rows and Columns in Pandas Using [ ],loc, iloc,at and.iat • 5 Free Data Science Books You Must Read in 2023 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project

Data Collection 131

Data Collection Data Science Modeling 131

Dataiku

OCTOBER 19, 2021

Between energy diversity, climate challenges, and growth in electricity consumption, energy producers and suppliers must constantly optimize their processes and anticipate demand in order to adjust their offers, a strategy based on massive data collection and the deployment of AI solutions.

Modeling 105

Modeling Data Collection Data Science Optimization 105

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Data Science and Beyond

NOVEMBER 22, 2015

Contrary to common belief, the hardest part of data science isn’t building an accurate model or obtaining good, clean data. The not-so-hard parts Before discussing the hardest parts of data science, it’s worth quickly addressing the two main contenders: model fitting and data collection/cleaning.

Data Science 92

Data Science Measurement Data Collection Modeling 92

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Datasphere is not just for data managers.

Data Warehouse 325

Data Warehouse Metadata Digital Transformation Unstructured Data 325

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Rocket-Powered Data Science

MARCH 19, 2021

Focus on the strategies that aim these tools, talents, and technologies on reaching business mission and goals: e.g., data strategy, analytics strategy, observability strategy ( i.e., why and where are we deploying the data-streaming sensors, and what outcomes should they achieve?).

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

KDnuggets

JANUARY 25, 2023

ChatGPT as a Python Programming Assistant • How to Use Python and Machine Learning to Predict Football Match Winners • 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project • 5 Free Data Science Books You Must Read in 2023

Machine Learning 94

Machine Learning Data Collection Data Science Modeling 94

Smart Data Collective

APRIL 5, 2022

The data science profession has become highly complex in recent years. Data science companies are taking new initiatives to streamline many of their core functions and minimize some of the more common issues that they face. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Cost-Benefit 122

Cost-Benefit Machine Learning Data Science Unstructured Data 122

CIO Business Intelligence

MARCH 21, 2022

What is a data scientist? Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Data scientist salary. Semi-structured data falls between the two.

Unstructured Data 136

Unstructured Data Data Analytics Analytics Structured Data 136

CIO Business Intelligence

JANUARY 17, 2024

The first was becoming one of the first research companies to move its panels and surveys online, reducing costs and increasing the speed and scope of data collection. We rely on cloud-scale technologies and proprietary data science and analytics engines built on open standards to handle massive data sets,” says Mohammed.

Digital Transformation 118

Digital Transformation Dashboards Reporting Data Science 118

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 363

Management Experimentation B2B Machine Learning 363

DataRobot Blog

MARCH 8, 2023

Every modern enterprise has a unique set of business data collected as part of their sales, operations, and management processes. Additionally, DataRobot data scientists and support teams have a proven record of success working with thousands of customers on tens of thousands of AI use cases across a wide range of industries.

Enterprise 105

Enterprise Experimentation Machine Learning Modeling 105

O'Reilly on Data

DECEMBER 10, 2019

We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. Nor are building data pipelines and deploying ML systems well understood. Instead, we can program by example.

Software 261

Software Machine Learning Risk Data-driven 261

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning 261

Machine Learning Software Metadata Testing 261

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 135

Analytics Data Science Unstructured Data Data mining 135

Cloudera

OCTOBER 11, 2022

This leads to the obvious question – how do you do data at scale ? Al needs machine learning (ML), ML needs data science. Data science needs analytics. And they all need lots of data. The challenge for AI is how to do data in all its complexity – volume, variety, velocity.

Snapshot 90

Snapshot Data Science Digital Transformation Metadata 90

Analytics Vidhya

SEPTEMBER 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction With technological evolution, data dependence is increasing much faster. Organizations are now employing data-driven approaches all over the world. One of the most widely used data applications […].

Predictive Analytics 264

Predictive Analytics Analytics Data-driven Data Science 264

Rocket-Powered Data Science

JULY 6, 2021

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. One type of implementation of a content strategy that is specific to data collections are data catalogs. Data catalogs are very useful and important.

Strategy 267

Strategy Machine Learning Metadata Knowledge Discovery 267

Rocket-Powered Data Science

JULY 7, 2019

Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics). A reference to a new phase in the Industrial Revolution that focuses heavily on interconnectivity, automation, Machine Learning, and real-time data. Examples: Cars, Trucks, Taxis. See [link].

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process.

Marketing 362

Marketing Experimentation Metrics Testing 362

Insight

MARCH 12, 2020

With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. The AIgent was built with BERT, Google’s state-of-the-art language model. Data Collection The AIgent leverages book synopses and book metadata.

Modeling 59

Modeling Metadata Publishing Sales 59

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

MAY 15, 2019

This Domino Data Science Field Note covers Pete Skomoroch ’s recent Strata London talk. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. It focuses on his ML product management insights and lessons learned. Conclusion.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 71

Data Strategy Strategy Data Warehouse Prescriptive Analytics 71

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 220

Machine Learning Risk Management Statistics 220

CIO Business Intelligence

AUGUST 9, 2022

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers. Data engineer vs. data architect.

Analytics 129

Analytics Data Science Statistics Unstructured Data 129

DataRobot

APRIL 27, 2021

With the power of DataRobot , creating AI and machine learning models with your data becomes less of a bottleneck due to the guardrails and transparency from getting from data to value. DataRobot uncovers insights in data that would be impossible for even expert humans to detect.

Machine Learning 98

Machine Learning Cost-Benefit Predictive Modeling Software 98

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. For one, Python remains the leading language for data science research.

Statistics 106

Statistics Unstructured Data Data Science Predictive Modeling 106

Cloudera

MARCH 29, 2022

Hybrid cloud is the Model of Choice. They want the computing power, cost efficiencies, and other advantages of public cloud – while retaining the flexibility, control, and security of private cloud and on-premises data centers. .

Cost-Benefit 93

Cost-Benefit Strategy Data Science Data Processing 93