Cloudera

FEBRUARY 8, 2021

Continuing from my previous blog post about how awesome and easy it is to develop web-based applications backed by Cloudera Operational Database (COD), I started a small project to integrate COD with another CDP cloud experience, Cloudera Machine Learning (CML). . For this purpose, I will use the LSTM (Long Short-term memory) algorithm.

Machine Learning 72

Machine Learning Statistics Modeling Dashboards 72

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L.

Forecasting 64

Forecasting Statistics Modeling Software 64

IBM Big Data Hub

SEPTEMBER 28, 2023

They also need secured access to business-relevant models that can help accelerate time to value and insights. emerges as a compelling solution,” says Atsushi Hasegawa, Chief Engineer, Honda R&D. emerges as a compelling solution,” says Atsushi Hasegawa, Chief Engineer, Honda R&D.

Testing 94

Testing Finance Enterprise Modeling 94

Cloudera

SEPTEMBER 24, 2021

Initially, network monitoring and service assurance systems like network probes tended not to persist information: they were designed as reactive, passive monitoring tools that would allow you to see what was going on at a point in time, after a network problem had occurred, but the data was never retained.

Analytics 91

Analytics IoT Cost-Benefit Big Data 91

Jedox

MARCH 12, 2020

Renewing business technologies that have slowly seen their value erode over time or become problematic ultimately means added value for your organization. Employees need time to get familiar with new solutions, data must be migrated and so on. This blog post is part of our series on EPM Tech Refresh.

Forecasting 59

Forecasting Optimization Technology Modeling 59

Jet Global

AUGUST 28, 2020

Blog posts : 99. R Ray Wang has held executive roles in product, marketing, strategy, and consulting at companies such as Forrester Research, Oracle, PeopleSoft, Deloitte, Ernst & Young, Personify, and Johns Hopkins Hospital. Blog posts : 13. Book Credits : – Blog posts : 32. Score : 4.0. YouTube Videos : 73.

Sales 52

Sales Manufacturing Strategy Software 52

AWS Big Data

MAY 4, 2023

Traditional solutions in use today, particularly with climate data, are time consuming and unsustainable, replicating datasets cross Regions. Amazon’s Open Data Sponsorship Program allows organizations to host free of charge on AWS. The AWS CDK solution deploys a network of Dask workers across two AWS Regions, connecting into a client Region.

Data Processing 92

Data Processing Metadata Informatics Interactive 92

The Unofficial Google Data Science Blog

APRIL 17, 2017

by ERIC TASSONE, FARZAN ROHANI We were part of a team of data scientists in Search Infrastructure at Google that took on the task of developing robust and automatic large-scale time series forecasting for our organization. So it should come as no surprise that Google has compiled and forecast time series for a long time.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Darkhorse

DECEMBER 11, 2017

I suggest that there are five distinct job descriptions: SUBSCRIBE TO OUR BLOG. He’ll use tools such as TensorFlow, R, MATLAB, ArcInfo, SAS, Tableau, and SPSS. He makes sure that results are timely and fast. In other instances, organizations will try to shoehorn engineers into the roles “in their spare time”.

Analytics 80

Analytics Visualization Data Warehouse Data-driven 80

Cloudera

MAY 28, 2019

Most would maintain that the majority of data scientists’ time is still spent on collecting and preparing data for analysis. Most would maintain that the majority of data scientists’ time is still spent on collecting and preparing data for analysis. Data management for ML/AI – what’s the big deal? Stay tuned. Register today!

Data Science 47

Data Science Machine Learning Data Strategy Cost-Benefit 47

Domino Data Lab

AUGUST 6, 2021

This is an extremely time-consuming and inefficient process that would be much more efficient using an ML model. Missing data needs to be addressed, data needs to be categorized, extraneous data and duplicate columns need to be removed, and only data that is available during the time of prediction may be used. Identify the Problem.

Data Science 75

Data Science Testing Modeling Publishing 75

Cloudera

SEPTEMBER 23, 2020

You can find part 1 of this series, here. . Apache Ranger fine-grained policies enable dynamic row filtering through SQL query compile time when SQL based relational constructs are used on OpDB (Hive on HBase). We are going to talk about auditing, different security levels, security features of Data Catalog, and Client Considerations.

Data Lake 80

Data Lake Metadata IoT Enterprise 80

DataRobot Blog

DECEMBER 7, 2019

This blog provides a unique take on using machine learning to predict free agent signings in the off-season. To do this, we built a series of models that predict the key outcomes of contract negotiations. age, service time, position). age, service time, position). Think of this as the “Scott Boras Premium”.

Machine Learning 52

Machine Learning Forecasting Metrics Modeling 52

CDW Research Hub

JULY 31, 2019

In this series, I’ll walk you through the design, development and deployment of a contextual AI assistant that designs curated travel experiences. Things like time, place and and name of a person all provide additional context and information related to an intent. x at the time of this post) is not fully compatible.

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

The Unofficial Google Data Science Blog

APRIL 23, 2024

This blog post discusses such a comprehensive approach that is used at Youtube. Figure 1: Changes in the amount of time users spent watching videos from channels that they have never before seen, and changes in the overall amount of time users spent watching any videos at all, vs change in a system parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Domino Data Lab

JANUARY 6, 2019

Welcome back to our monthly series about data science. However, right now is an interesting point in time to consider: “What needs to be taught? And my favorite topic: what are some of the best books, blogs, podcasts, etc., Find good people to mentor and invest your time. Introduction. for beginning study in data science?

Data Science 56

Data Science Machine Learning Reporting Visualization 56

The Unofficial Google Data Science Blog

JUNE 30, 2016

The first assumption makes clear the fundamental problem of causal inference: for any given unit we observe at most one of the potential outcomes $Y=Ttimes Y_1 +(1-T)times Y_0$. For example, imagine that you are working for a car manufacturer. You are in charge of assessing whether the campaign had an impact on sales.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. pip install -r requirements.txt. The raw data is in a series of CSV files. Introduction. What is RAPIDS. See < [link] > for more details. Get the Dataset.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

The Data Visualisation Catalogue

JANUARY 24, 2023

After consuming a number of YouTube videos, blog posts, articles, and playing around with ChatGPT, I felt the need to write down my thoughts and observations on the topic. This is probably due to this tool demonstrating the potential to revolutionise the way we search and interact with information over the internet.

Visualization 114

Visualization Interactive Data-driven Testing 114

DataRobot Blog

NOVEMBER 3, 2022

Data scientists have used the DataRobot AI Cloud platform to build time series models for several years. However, tedious and redundant tasks in exploratory data analysis, model development, and model deployment can stretch the time to value of your machine learning projects. That’s not because people aren’t intelligent.

Forecasting 52

Forecasting Sales Modeling Marketing 52

DataKitchen

MAY 10, 2021

They cause people to work long hours at the expense of personal and family time. If you have been in the data profession for any length of time, you probably know what it means to face a mob of stakeholders who are angry about inaccurate or late analytics. Data sources must deliver error-free data on time.

Testing 214

Testing Manufacturing Data Quality Statistics 214

DataRobot Blog

DECEMBER 15, 2022

AI-powered Time Series Forecasting may be the most powerful aspect of machine learning available today. Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

AWS Big Data

JUNE 7, 2023

This is a guest blog post co-written with Sumesh M R from Cargotec and Tero Karttunen from Knowit Finland. In this blog, we discuss the technical challenges faced by Cargotec in replicating their AWS Glue metadata across AWS accounts, and how they navigated these challenges successfully to enable cross-account data sharing.

Metadata 55

Metadata Data Lake Machine Learning Big Data 55

Sisense

JULY 15, 2020

The model outputs the 4 following time series: S – The number of Susceptible individuals E – The number of people Exposed to an infected individual and are potential carriers I – Infected individuals R – Recovered individuals. Calling on Quest to handle complex calculations with ease.

Analytics 90

Analytics Modeling Visualization Dashboards 90

Cloudera

SEPTEMBER 20, 2021

In this blog series, we will discuss each of these deployments and the deployment choices made along with how they impact reliability. R s = R 1 * R 2 … R n. R s = R 1 * R 2 … R n. R S is the Reliability of the total system. Serial Systems Reliability. 2 components.

Data Processing 115

Data Processing Software IT 115

Cloudera

NOVEMBER 3, 2021

One of the best ways to make a substantial improvement in processing time is to, if you haven’t already, switched from CPUs to GPUs. Time required to configure an environment with GPUs. Time required to configure an environment with GPUs. Time required to refactor CPU code. Photo Credit: Kaggle. Checkmate reason 2.

Deep Learning 116

Deep Learning Machine Learning Data Science Cost-Benefit 116

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. While the discussion is about methods and applications, the blog also contains pointers to research papers and to the TensorFlow Lattice package that provides an implementation of these solutions. But we also know more.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

IBM Big Data Hub

AUGUST 15, 2023

With more than 50 years of experience in the research and development (R&D) and manufacturing of chains, Suzhou Universal Chain is a leader in the domestic chain industry. It is also a national high-tech enterprise that integrates chain R&D, manufacturing and sales.

Software 55

Software Manufacturing Enterprise Sales 55

The Unofficial Google Data Science Blog

JULY 18, 2023

Image from Data Excellence: Better Data for Better AI by Lora Aroyo At the same time, the inherent noisiness of human-labeled data forces us to think about the quality of the measurement instrument, especially when collecting these types of labels at Google-scale. That’s the focus of this blog post.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Sisense

JUNE 26, 2020

Both Python and R are advanced coding languages that can produce beautiful images that allow humans to understand vast datasets with ease. As datasets become bigger and more complex, only AI, materialized views, and more sophisticated coding languages will be able to glean insights from them. The good news is, you don’t have to!

Visualization 97

Visualization Unstructured Data Measurement Data-driven 97

datapine

SEPTEMBER 16, 2022

Now that you’re sold on the power of data analytics in addition to data-driven BI, it’s time to take your journey a step further by exploring how to effectively communicate vital metrics and insights in a concise, inspiring, and accessible format through the power of visualization. Data visualization: What You Need To Know.

Visualization 304

Visualization Dashboards Data-driven Statistics 304

Sisense

JANUARY 15, 2021

But in this second post in our Building Bridges series, we are focused on the data teams driving the adoption of a BI and analytics tool with their business intelligence counterparts. Big data is now modeled and queried using advanced coding languages like SQL, Python, and R. Implementing analytics at your company is a multi-team job.

Analytics 75

Analytics Data-driven Business Intelligence Visualization 75

Sisense

AUGUST 9, 2021

>>>Reveal deeper intelligence from your data with Python and R. . >>>Reveal >>>Reveal deeper intelligence from your data with Python and R. Pat Bhatt (PB): The Notebooks functionality empowers data analysts with the tools they need to conduct advanced analysis using SQL, Python, and R.

Analytics 52

Analytics Visualization Marketing Data Warehouse 52

Sisense

MAY 26, 2020

Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. It locates and classifies different entities such as person names, organizations, locations, time expressions, quantities, monetary values, and percentages. Real-time data.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Alation

FEBRUARY 8, 2022

In our first blog in this series , we define the terms data fabric and data mesh. The second blog took a deeper dive into data fabric, examining its key pillars and the role of the data catalog in each. The “data textile” wars continue! Yet technology still plays a key enabling role in making this shift a reality. Discoverable.

Data Governance 52

Data Governance Data-driven Metadata Enterprise 52

Data Science and Beyond

JANUARY 7, 2019

Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. We take r bootstrap resamples from the original data sample, where each resample is a sample with replacement of size n. However, I’ve learned in the past few weeks that there are quite a few pitfalls in bootstrapping.

Statistics 77

Statistics Uncertainty Testing Modeling 77

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. coefficient times feature value) would be indicative of what the model deemed noteworthy.

IT 68

IT Visualization Modeling Uncertainty 68