Xebia

MARCH 10, 2021

Data Science and Advanced Analytics encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large data sets [1]. The focus of data science is on improving decision making through the analysis of data [1].

DevOps 130

DevOps Data Analytics Policies 130

Mentormate

OCTOBER 31, 2023

The allure of the latest machine-learning techniques is undeniable, but without a well-structured approach, you risk getting lost in the technological maze. A thorough stakeholder analysis enriches the AI project by making it more comprehensive, ethical, and aligned with the overarching business needs and societal values.

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

O'Reilly Media - Ideas

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

Big Data 87

Big Data Technology Tools Cloud 87

O'Reilly Media - Ideas

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. Machine learning adds uncertainty.

Product Management 139

Product Management Artificial Inteligence Machine Learning Software Review 139

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 85

Data Analytics Cloud Technical Review 85

Altexsoft

JUNE 11, 2021

Process mining is a set of techniques for the analysis of operational processes based on event logs extracted from company’s databases, information systems, or business management software such as enterprise resource planning (ERP), customer relationship management (CRM), electronic health records (EHR), etc. What is process mining?

Applications 69

Applications Weak Development Team Systems Review Software Review 69

Cloudera

MAY 22, 2018

Access any data anywhere – from cloud object storage to data warehouses, CDSW provides connectivity not only to CDH but the systems your data science teams rely on for analysis. This leads to wasted time and effort during research and collaboration or, worse, compliance risk.

Data 42

Data Load Balancer Machine Learning Artificial Inteligence 42

Altexsoft

SEPTEMBER 9, 2022

Efficient metadata management ensures data integrity , consistency, trustworthiness, and compliance. You can get more information about data labeling in machine learning from another post (it’s one of the main steps of preparing datasets for ML). Metadata quality assurance.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

FEBRUARY 8, 2021

To support the planning process, predictive analytics and machine learning (ML) techniques can be implemented. We have previously described demand forecasting methods and the role of machine learning solutions in a dedicated article. Comparison between traditional and machine learning approaches to demand forecasting.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Cloudera

DECEMBER 10, 2020

These file formats not only help avoid data duplication into proprietary storage formats but also provide highly efficient storage formats. Multiple analytical engines (data warehousing, machine learning, data engineering, and so on) can operate on the same data in these file formats.

Data 98

Data Technical Review Storage Systems Review 98

Existek

JANUARY 11, 2022

It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. . They focus much attention on advancing user experiences utilizing AI, robotics, machine learning, IoT, etc. . Machine learning. Security, identity, and compliance.

Azure 52

Azure AWS Cloud How To 52

Cloudera

OCTOBER 19, 2023

This exponential growth in connected devices will force telcos to up their game, first by provisioning the capacity they need to scale and maintain next-gen 5G data networks, and later by improving the effectiveness of their data management and governance practices.

Data 78

Data Compliance Architecture Data Engineering 78

ParkMyCloud

JANUARY 17, 2020

Additionally, they must be able to implement and automate security controls, governance processes, and compliance validation. For individuals who perform complex Big Data analyses and have at least two years of experience using AWS. Implement core AWS Big Data services according to basic architecture best practices.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). based Walgreens consolidated its systems of insight into a single data lakehouse.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Altexsoft

NOVEMBER 9, 2021

Traditional statistical methods use mainly internal, historical data to predict trends within relatively stable markets. Meanwhile, machine learning (ML) techniques are capable of processing a wide range of both historical and current data from multiple external and internal sources. Compliance evaluation.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

O'Reilly Media - Ideas

JANUARY 25, 2024

While our analysis of search trends shows that interest in ChatGPT has peaked among our platform’s users, interest in natural language processing (NLP) showed a 195% increase—and from a much higher starting point. PyTorch, the Python library that has come to dominate programming in machine learning and AI, grew 25%.

Trends 118

Trends Technical Review Technology Artificial Inteligence 118

DataRobot

MARCH 16, 2023

To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. Agility and enterprise security, compliance, and governance are often at odds. Why are we focusing on this? Because the current ML lifecycle process is broken.

Data 52

Data Machine Learning Artificial Inteligence Agile 52

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

John Snow Labs

MAY 24, 2023

Almost 90% of the machine learning models encounter delays and never make it into production. Developing a machine learning model requires a big amount of training data. Therefore, the data needs to be properly labeled/categorized for a particular use case. Select a workforce with subject matter expertise.

Tools 52

Tools Machine Learning Artificial Inteligence Software Review 52

OpenCredo

MARCH 20, 2020

It is used extensively across the business for reporting, analysis and powering services. To optimise our use of data, we need services which store it reliably, provide interfaces for analysis and automate transformation. Security, because we work with sensitive data, client information and intellectual property.

Cloud 40

Cloud Data Weak Development Team Compliance 40

BPM

JANUARY 18, 2019

The current Artificial Intelligence (AI) fascination is unfortunately completely biased on Deep Neural Networks (DNN) and Machine Learning (ML) for everything. That augmentation must be in a form attractive to humans while enabling security, compliance, authenticity and auditability. Denis Gagne Trisotech [link].

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Altexsoft

MARCH 18, 2021

ETL and ELT are the most widely applied approaches to deliver data from one or many sources to a centralized system for easy access and analysis. These are data engineers who are responsible for implementing these processes. Moreover, raw data can undergo numerous transformations as it is stored indefinitely.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Cloudera

OCTOBER 25, 2022

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

O'Reilly Media - Data

MARCH 22, 2018

Over the years, machine learning (ML) has come a long way, from its existence as experimental research in a purely academic setting to wide industry adoption as a means for automating solutions to real-world problems. Exploratory data analysis and visualization. What is model interpretation?

Off-The-Shelf 115

Off-The-Shelf Machine Learning Artificial Inteligence Weak Development Team 115

Altexsoft

MARCH 2, 2023

Data management is a technical implementation of data governance and involves the practical aspects of working with data, such as data storage, retrieval, and analysis. Learn how data is prepared for machine learning in our dedicated video. Compliance. Improved data management.

Government 52

Government Tools Data Weak Development Team 52

Altexsoft

AUGUST 22, 2022

Remember that a data fabric is NOT a single software tool you can buy and deploy overnight. It is a general design approach that leverages data virtualization principles. It is also driven by metadata, which it uses to recognize patterns, map data, and perform continuous analysis. Consolidated data protection.

Architecture 52

Architecture Artificial Inteligence Technical Review Data 52

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. As each new service is started, data is typically copied in for analysis. There is a better way. It’s time for SDX.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Altexsoft

JANUARY 14, 2021

A data warehouse is often abbreviated as DW or DWH. You may also find it under the name of an enterprise data warehouse (EDW). It is usually created and used primarily for data reporting and analysis purposes. As such, it is possible to retrieve old archived data if needed. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

NOVEMBER 10, 2021

At the same time, it brings structure to data and empowers data management features similar to those in data warehouses by implementing the metadata layer on top of the store. Typically used for data analysis and reporting, data warehouses rely on ETL mechanisms to extract , transform , and load data into a destination.

Architecture 52

Architecture Data Storage Machine Learning 52

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO DQ will become the new data quality product family, through an evolution of our current data quality offerings, significantly enhancing current capabilities available throughout the TIBCO data fabric with built-in AI and ML to automate quality, detection, monitoring, and anomaly resolution.

Innovation 26

Innovation Analytics Virtualization Cloud 26

O'Reilly Media - Ideas

MARCH 1, 2023

Software development is followed by IT operations (18%), which includes cloud, and by data (17%), which includes machine learning and artificial intelligence. When you add searches for Go and Golang, the Go language moves from 15th and 16th place up to 5th, just behind machine learning.

Trends 136

Trends Technical Review Technology Software Review 136

Agile Engine

DECEMBER 27, 2023

From emerging trends to hiring a data consultancy, this article has everything you need to navigate the data analytics landscape in 2024. What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7. Table of contents 1.

Big Data 52

Big Data Analytics Data Analysis 52

O'Reilly Media - Ideas

JANUARY 25, 2022

Our approach to Answers was to do a simple “bag of words” analysis: count the number of times each word was used in all Answers queries. Our analysis only included organic questions; we didn’t count clicks on the question bank. Keep in mind that a title like Machine Learning in the AWS Cloud would match both terms.)

Trends 110

Trends Technical Review Technology Artificial Inteligence 110

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. Besides that, it’s fully compatible with various data ingestion and ETL tools. How data engineering works in 14 minutes.

Weak Development Team 64

Weak Development Team Machine Learning Artificial Inteligence Software Review 64

Altexsoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. Data democratization.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Hu's Place - HitachiVantara

JANUARY 15, 2019

For lack of similar capabilities, some of our competitors began implying that we would no longer be focused on the innovative data infrastructure, storage and compute solutions that were the hallmark of Hitachi Data Systems.

Systems Review 72

Systems Review Storage System Software Review 72