Domino Data Lab

JUNE 7, 2022

Features are input for machine learning models. The most efficient way to use them across an organization is in a feature store that automates the data transformations, stores them and makes them available for training and inference.

Machine Learning 64

Machine Learning Data Transformation Modeling 64

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps is a term that refers to the set of practices and tools that organizations use to improve the quality and speed of data analytics and machine learning. It involves bringing together people, processes, and technology to enable data-driven decision making and improve the efficiency of data-related workflows.

Machine Learning 130

Machine Learning Data-driven Optimization Modeling 130

Domino Data Lab

JUNE 6, 2022

Features are input for machine learning models. The most efficient way to use them across an organization is in a feature store that automates the data transformations, stores them and makes them available for training and inference.

Machine Learning 52

Machine Learning Data Transformation Modeling 52

Jen Stirrup

SEPTEMBER 30, 2021

Although CRISP-DM is not perfect , the CRISP-DM framework offers a pathway for machine learning using AzureML for Microsoft Data Platform professionals. AI vs ML vs Data Science vs Business Intelligence. They may also learn from evidence, but the data and the modelling fundamentally comes from humans in some way.

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

CIO Business Intelligence

FEBRUARY 16, 2023

My vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,” says Iyengar, director of Data & Tech at Straumann Group North America. The company’s Findability.ai

Unstructured Data 105

Unstructured Data Data Lake Prescriptive Analytics Digital Transformation 105

CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. What are the four types of data analytics? Data analytics methods and techniques.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

CIO Business Intelligence

JULY 5, 2022

What is the difference between business analytics and data analytics? Business analytics is a subset of data analytics. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 131

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 131

Cloudera

OCTOBER 13, 2020

In the beginning, CDP ran only on AWS with a set of services that supported a handful of use cases and workload types: CDP Data Warehouse: a kubernetes-based service that allows business analysts to deploy data warehouses with secure, self-service access to enterprise data. Predict – Data Engineering (Apache Spark).

Data Warehouse 97

Data Warehouse Machine Learning Visualization Data Lake 97

AWS Big Data

DECEMBER 13, 2023

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

Metadata 84

Metadata Data Lake Visualization Data Transformation 84

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). . b) Basic data transformation. Go to runner.py and run it.

Machine Learning 76

Machine Learning Statistics Modeling Dashboards 76

Cloudera

AUGUST 3, 2022

By leveraging Hive to apply Ranger FGAC, Spark obtains secure access to the data in a protected staging area. Since Spark has direct access to the staged data, any Spark APIs can be used, from complex data transformations to data science and machine learning. .

Snapshot 90

Snapshot Cost-Benefit Machine Learning Data Transformation 90

Cloudera

AUGUST 8, 2022

The general availability covers Iceberg running within some of the key data services in CDP, including Cloudera Data Warehouse ( CDW ), Cloudera Data Engineering ( CDE ), and Cloudera Machine Learning ( CML ). Cloudera Data Engineering (Spark 3) with Airflow enabled. Cloudera Machine Learning .

Snapshot 94

Snapshot Data Warehouse Machine Learning Cost-Benefit 94

Cloudera

OCTOBER 19, 2021

Airflow has been adopted by many Cloudera Data Platform (CDP) customers in the public cloud as the next generation orchestration service to setup and operationalize complex data pipelines. Some of them include: More operators – as we mentioned earlier there is a small set of highly used operators.

Data Transformation 119

Data Transformation Interactive Machine Learning Testing 119

AWS Big Data

APRIL 19, 2023

However, when a data producer shares data products on a data mesh self-serve web portal, it’s neither intuitive nor easy for a data consumer to know which data products they can join to create new insights. This is especially true in a large enterprise with thousands of data products.

Technology 78

Technology Data-driven Machine Learning Sales 78

AWS Big Data

APRIL 26, 2023

We also split the data transformation into several modules (Data Aggregation, Data Filtering, and Data Preparation) to make the system more transparent and easier to maintain. Although each module is specific to a data source or a particular data transformation, we utilize reusable blocks inside of every job.

Manufacturing 118

Manufacturing Forecasting Data Lake Big Data 118

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML).

Risk 76

Risk Modeling Management Metadata 76

Sisense

MARCH 30, 2021

Advanced data transformation with Custom Code. With our latest release, you can leverage Python code from Jupyter Notebooks to connect to services such as Amazon SageMaker, Amazon Comprehend, and Amazon Translate to transform and augment data inside your ElastiCube models. If so, it’s your lucky day.

Dashboards 52

Dashboards Machine Learning Data Warehouse Marketing 52

Cloudera

JULY 9, 2021

Use the right tools: Organizations must command and control the entire data lifecycle, from initial data ingest to AI/ML based analysis to acting decisively on data-driven intelligence derived from newfound, cloud-enabled impact, the right capabilities produce the holistic, coherent view that drives organizations to the cloud in the first place.

Data Strategy 112

Data Strategy Strategy Data-driven Machine Learning 112

DataRobot Blog

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.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

Ontotext

MARCH 20, 2024

Machine learning (ML): Gain deeper insights from your data with advanced algorithms for data-driven decision-making. Natural Language Processing (NLP): Understand and interpret the meaning of text data to extract valuable insights. Zenia Graph’s Salesforce Accelerator makes this a reality.

Data-driven 52

Data-driven Strategy Sales Data Integration 52

AWS Big Data

APRIL 11, 2023

Such a solution should use the latest technologies, including Internet of Things (IoT) sensors, cloud computing, and machine learning (ML), to provide accurate, timely, and actionable data. In the inventory management and forecasting solution, AWS Glue is recommended for data transformation.

Forecasting 72

Forecasting Management IoT Data-driven 72

bridgei2i

NOVEMBER 17, 2020

The Lean AI wave can be imagined as a 4 step process: AI use case discovery: Identify the current processes amenable to data and AI driven improvement, design the solution roadmap and proactively think through the potential failure modes of enterprise adoption.

Deep Learning 85

Deep Learning Manufacturing Optimization Data-driven 85

AWS Big Data

DECEMBER 21, 2023

Movement of data across data lakes, data warehouses, and purpose-built stores is achieved by extract, transform, and load (ETL) processes using data integration services such as AWS Glue. AWS Glue provides both visual and code-based interfaces to make data integration effortless.

Analytics 92

Analytics IT Data Lake Visualization 92

AWS Big Data

AUGUST 9, 2023

The advent of rapid adoption of serverless data lake architectures—with ever-growing datasets that need to be ingested from a variety of sources, followed by complex data transformation and machine learning (ML) pipelines—can present a challenge.

Data Lake 83

Data Lake Metrics Testing Cost-Benefit 83

DataKitchen

JULY 27, 2023

You can use it for big data analytics and machine learning workloads. Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. Azure Blob Storage serves as the data lake to store raw data.

Machine Learning 130

Machine Learning Cost-Benefit Data Transformation Testing 130

Smart Data Collective

AUGUST 14, 2019

CIO reports that big data has helped the gaming industry increase its revenue to $40.6 How Is Big Data Transforming Digital Gaming? Today, these platforms are revolutionizing multiple new-age industries such as the Big Data industry and more, largely due to developments in artificial intelligence and machine learning.

Big Data 77

Big Data Technology Recreation/Entertainment Machine Learning 77

DataRobot Blog

APRIL 4, 2022

Before the data is put into the model comes a process called feature engineering – transforming the original data columns to impose certain business assumptions or simply increase model accuracy. The post Bringing MMM to 21st Century with Machine Learning and Automation? Want to See DataRobot in Action?

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Anmut

MARCH 24, 2022

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success. It doesn’t have to be this way.

ROI 52

ROI Measurement Data-driven Data Transformation 52

CIO Business Intelligence

JUNE 13, 2022

According to Evanta’s 2022 CIO Leadership Perspectives study, CIOs’ second top priority within the IT function is around data and analytics, with CIOs seeing advancing organizational use of data as key to reaching enterprise objectives. Others also list data initiatives as a top issue for CIOs.

IT 144

IT Digital Transformation Strategy Internet of Things 144

AWS Big Data

JANUARY 27, 2023

The Post Call Analytics (PCA) solution uses AWS machine learning (ML) services like Amazon Transcribe and Amazon Comprehend to extract insights from contact center call audio recordings uploaded after the call, or from integration with our companion Live Call Analytics (LCA) solution.

Analytics 59

Analytics Reporting Dashboards Visualization 59

AWS Big Data

MARCH 9, 2023

He is passionate about shaping the future of infusing data-rich experiences into products and applications we use every day. Rohit brings a wealth of experience in analytics and machine learning from having worked with leading data companies, and their customers.

Slice and Dice 76

Slice and Dice Dashboards Analytics Interactive 76

O'Reilly on Data

JANUARY 17, 2023

Think about what the model results tell you: “Maybe a random forest isn’t the best tool to split this data, but XLNet is.” ” If none of your models performed well, that tells you that your dataset–your choice of raw data, feature selection, and feature engineering–is not amenable to machine learning.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

Sisense

MARCH 14, 2021

Looking at the diagram, we see that Business Intelligence (BI) is a collection of analytical methods applied to big data to surface actionable intelligence by identifying patterns in voluminous data. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

Alation

FEBRUARY 2, 2023

They are knowledgeable about the different tools and technologies used in the industry, such as programming languages, data processing frameworks, machine learning libraries, databases, and analytics platforms for call history. They know how to choose the best tools for the job and use them effectively.

Management 52

Management Data-driven Visualization Strategy 52

AWS Big Data

APRIL 25, 2023

The following AWS services are used for data ingestion, processing, and load: Amazon AppFlow is a fully managed integration service that enables you to securely transfer data between SaaS applications like Salesforce, SAP, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift , in just a few clicks.

Sales 68

Sales Visualization Software Marketing 68

AWS Big Data

FEBRUARY 6, 2023

He mainly works with enterprise customers to help data lake migration and modernization, and provides guidance and technical assistance on big data projects such as Hadoop, Spark, data warehousing, real-time data processing, and large-scale machine learning.

Dashboards 72

Dashboards Optimization Data Lake Cost-Benefit 72

Analytics Vidhya

JULY 27, 2020

Overview Understand the requirement of feature transformation and training techniques Get to know different feature transformation and scaling techniques including- MinMax Scaler Standard Scaler. The post Feature Transformation and Scaling Techniques to Boost Your Model Performance appeared first on Analytics Vidhya.

Modeling 333

Modeling Analytics Data Transformation Machine Learning 333