AltexSoft

JUNE 26, 2023

Note that in many cases, the process of gathering information never ends since you always need fresh data to re-train and improve existing ML models, gain consumer insights, analyze current market trends, and so on. Find sources of relevant data. Choose data collection methods and tools. Decide on a sufficient data amount.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Snowflake

FEBRUARY 23, 2023

Hospital price transparency helps Americans know the cost of a hospital item or service before receiving it. Workaround processes, as described above, are often complex, time- consuming, and require multiple technologies or infrastructures to make them work.

Healthcare 52

Healthcare Hospitality Insurance Cloud Storage 52

Edureka

AUGUST 2, 2023

Data integration and transformation: Before analysis, data must frequently be translated into a standard format. Data processing analysts harmonise many data sources for integration into a single data repository by converting the data into a standardised structure.

Data Process 52

Data Process Process Data Cleanse Data Mining 52

Edureka

JUNE 1, 2023

In today’s digital era, businesses operate in a data-driven environment where data is generated at an unprecedented rate. Every single activity of our daily life is influenced by connected existence and nowadays all these activities include technology. This is where Data Analytics comes into action.

AWS 52

AWS Data Analytics Cloud Amazon Web Services 52

AltexSoft

MAY 30, 2022

So, it makes sense that more and more companies are looking for a way to implement conversational artificial intelligence (AI) technology to streamline these processes. Let’s dive deeper into core technologies that enable machines to do these awesome things. Conversational AI key concepts and technologies.

Banking 52

Banking Machine Learning Deep Learning Insurance 52

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Major Big Data analytics tools and services.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

MARCH 15, 2016

Hadoop is beginning to live up to its promise of being the backbone technology for Big Data storage and analytics. Companies across the globe have started to migrate their data into Hadoop to join the stalwarts who already adopted Hadoop a while ago.

Hadoop 40

Hadoop Retail Healthcare Banking 40

ProjectPro

AUGUST 24, 2021

But before you send out your resume for any data engineer job, and if you want to get shortlisted for further rounds, you need to have ample knowledge of various data engineering technologies and methods. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

DECEMBER 26, 2023

You can also gain knowledge and certification on big data in detail through Big Data certification online courses. Top Big Data Books for Beginners 1. Big Data: Concepts, Technology and Architecture For data scientists, engineers, and database managers, Big Data is the best book to learn big data.

Big Data 75

Big Data Data Mining Business Intelligence Machine Learning 75

Snowflake

APRIL 20, 2023

The new features also enable customers to easily search in logs and semi-structured data stored in VARIANT, ARRAY, and OBJECT columns, which prove to be especially useful for cybersecurity vendors who perform needle-in-a-haystack-type queries. Health & Life Sciences Medical Data Vision Co., Intellizence Intellizence, Inc.,

Medical 57

Medical Retail Python Pharmaceutical 57

AltexSoft

DECEMBER 15, 2021

In the meantime, we’ll focus on AutoML which drives a considerable part of the MLOps cycle, from data preparation to model validation and getting it ready for deployment. Before diving deeper into technological aspects, let’s take a closer look at key AutoML use cases. Why and when do you critically need data scientists?

Machine Learning 59

Machine Learning Deep Learning Algorithm Telecommunication 59