phData

AUGUST 11, 2023

Data engineering in healthcare is taking a giant leap forward with rapid industrial development. However, data collection and analysis have been commonplace in the healthcare sector for ages. Data Engineering in day-to-day hospital administration can help with better decision-making and patient diagnosis/prognosis.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

ODSC - Open Data Science

JANUARY 11, 2024

Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven Data Modeling How To Get Started With Building AI in High-Risk Industries This guide will get you started building AI in your organization with ease, axing unnecessary jargon and fluff, so you can start today.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

AWS Machine Learning Blog

NOVEMBER 13, 2024

With the integration of SageMaker and Amazon DataZone, it enables collaboration between ML builders and data engineers for building ML use cases. ML builders can request access to data published by data engineers. Also, you can update the model’s deploy status. medium", "ml.m5.large"], medium", "ml.m5.xlarge"],

ML 117

ML ML AWS Data Preparation 117

AWS Machine Learning Blog

FEBRUARY 21, 2025

Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data. Data is stored in HDFS and is accessed via Hive, which provides a tabular interface to the data and integrates with Spark SQL.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

AWS Machine Learning Blog

JANUARY 5, 2024

By the end of the consulting engagement, the team had implemented the following architecture that effectively addressed the core requirements of the customer team, including: Code Sharing – SageMaker notebooks enable data scientists to experiment and share code with other team members.

AWS 136

AWS Data Science ML ML 136

Dataconomy

MAY 2, 2023

By acquiring expertise in statistical techniques, machine learning professionals can develop more advanced and sophisticated algorithms, which can lead to better outcomes in data analysis and prediction. These techniques can be utilized to estimate the likelihood of future events and inform the decision-making process.

ML 110

ML ML Machine Learning Machine Learning 110

ODSC - Open Data Science

MAY 10, 2023

Data-centric AI, in his opinion, is based on the following principles: It’s time to focus on the data — after all the progress achieved in algorithms means it’s now time to spend more time on the data Inconsistent data labels are common since reasonable, well-trained people can see things differently.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

phData

MARCH 11, 2024

Event Tracking : Capturing behavioral events such as page views, add-to-cart, signup, purchase, subscription, etc. Identity Resolution : Merging behavioral events and customer identifiers in an identity graph to create a single comprehensive customer profile. dbt has become the standard for modeling.

Data Warehouse 52

Data Warehouse Cloud Data Data Modeling Data Models 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

Data scientists will typically perform data analytics when collecting, cleaning and evaluating data. By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. Diagnostic analytics: Diagnostic analytics helps pinpoint the reason an event occurred.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

AWS Machine Learning Blog

OCTOBER 20, 2023

Collectively, these modules address governance across various dimensions, such as infrastructure, data, model, and cost. Security OU The accounts in this OU are managed by the organization’s cloud admin or security team for monitoring, identifying, protecting, detecting, and responding to security events.

ML 137

ML ML AWS Data Lakes 137

ODSC - Open Data Science

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing. Well then, you’re in luck. So, what are you waiting for?

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

Data Governance 102

Data Governance Analytics Analytics Tableau 102

Tableau

SEPTEMBER 23, 2021

For example, Tableau data engineers want a single source of truth to help avoid creating inconsistencies in data sets, while line-of-business users are concerned with how to access the latest data for trusted analysis when they need it most. Data modeling. Data migration . Data architecture.

Data Governance 97

Data Governance Analytics Analytics Tableau 97

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. Curated foundation models, such as those created by IBM or Microsoft, help enterprises scale and accelerate the use and impact of the most advanced AI capabilities using trusted data.

AI 88

AI AI Data Warehouse ML 88

Tableau

DECEMBER 1, 2021

Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. April 2018), which focused on users who do understand joins and curating federated data sources. Another key data computation moment was Hyper in v10.5 (Jan Release v1.0 IPO in 2013.

Tableau 145

Tableau ML ML Database 145

Alation

OCTOBER 27, 2022

To answer this question, I sat down with members of the Alation Data & Analytics team, Bindu, Adrian, and Idris. Some may be surprised to learn that this team uses dbt to serve up data to those who need it within the company. Contact title mappings, which are buiilt in some of data models, are documented within our data catalog.

Data Analyst 52

Data Analyst Data Scientist Analytics Analytics 52

Iguazio

DECEMBER 14, 2023

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. Readers learn how to: Build and register the model for use in the production application.

ML 52

ML ML Data Science Data Preparation 52

ODSC - Open Data Science

OCTOBER 15, 2023

Elementl / Dagster Labs Elementl and Dagster Labs are both companies that provide platforms for building and managing data pipelines. Elementl’s platform is designed for data engineers, while Dagster Labs’ platform is designed for data scientists. Interested in attending an ODSC event?

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. Data Pipeline - Manages and processes various data sources. Application Pipeline - Manages requests and data/model validations.

ML 52

ML ML Data Scientist AI 52

Tableau

DECEMBER 1, 2021

Four reference lines on the x-axis indicate key events in Tableau’s almost two-decade history: The first Tableau Conference in 2008. April 2018), which focused on users who do understand joins and curating federated data sources. Another key data computation moment was Hyper in v10.5 (Jan Release v1.0 IPO in 2013.

Tableau 98

Tableau ML ML Database 98

Mlearning.ai

FEBRUARY 16, 2023

Use Multiple Data Models With on-premise data warehouses, storing multiple copies of data can be too expensive. You can use Snowflake cloud computing to store raw data in structured or variant format, using various data models to meet the needs. What will You Attain with Snowflake?

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. This provides end-to-end support for data engineering and MLOps workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

AUGUST 23, 2023

Utilize libraries such as Pandas for data manipulation, NumPy for numerical computations, and Scikit-Learn for Machine Learning tasks. Leverage these libraries to preprocess stock market data, engineer relevant features, and train predictive models.

Data Science 52

Data Science Machine Learning Machine Learning Algorithm 52

Pickl AI

JULY 25, 2024

Surrounding it are dimension tables that provide context to the data, such as time, product, or customer details. Components In a Star Schema, the fact table is the core element, containing measurable data, often called facts. This normalisation helps conserve storage space and maintain a cleaner data model.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

Pickl AI

JULY 18, 2024

MongoDB is a NoSQL database that uses a document-oriented data model. It stores data in flexible, JSON-like documents, allowing for dynamic schemas. Each document can have a different structure, allowing for flexibility in data modelling. How Does MongoDB Handle Large-Scale Data Migrations? What Is MongoDB?

Database 52

Database SQL Data Analyst Database Administration 52