CIO Business Intelligence

NOVEMBER 4, 2024

Artificial Intelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. Currently, enterprises primarily use AI for generative video, text, and image applications, as well as enhancing virtual assistance and customer support. Nutanix commissioned U.K.

Marketing 128

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Analytics Vidhya

SEPTEMBER 7, 2022

This article was published as a part of the Data Science Blogathon. Introduction We go back in time to discuss the history of containers and virtualization. It’s important to bear in mind that the containers can exist because of the possibility of the machines being virtualized.

Data Science 318

Data Science Publishing Analytics IT 318

Analytics Vidhya

JANUARY 16, 2023

This article was published as a part of the Data Science Blogathon. Introduction Artificial intelligence (AI) is rapidly becoming a fundamental part of our daily lives, from self-driving cars to virtual personal assistants. The use of AI […].

Data Science 319

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Analytics Vidhya

JULY 6, 2022

This article was published as a part of the Data Science Blogathon. The post Create a Virtual Machine for Free on Microsoft Azure appeared first on Analytics Vidhya. The prerequisite is that you must have a school or university email address […].

Data Science 306

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Analytics Vidhya

MAY 17, 2022

This article was published as a part of the Data Science Blogathon. A MySQL view can be defined as a virtual table based on the result of SQL statements, which helps in simplifying the data for analysis and reporting and offers better security. Here is how to create MySQL views and update, drop or rename […].

Data Science 291

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Teradata

MARCH 25, 2021

Learn how best to host a virtual hackathon, or any virtual event, with these tips and tricks from our Teradata team. Read more.

Data Processing 139

Data Processing Data Science 139

Analytics Vidhya

OCTOBER 14, 2021

This article was published as a part of the Data Science Blogathon Overview Text analysis is one of the most interesting advancements in the domain of Natural Language Processing (NLP). Text analysis is used in virtual assistants like Alexa, Google Home, and others.

Data Science 271

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Analytics Vidhya

JANUARY 27, 2022

This article was published as a part of the Data Science Blogathon. The post Virtual Zoom using OpenCV appeared first on Analytics Vidhya. Introduction Zoom In! OpenCV has revolutionized the entire image processing world. Today we are going to implement something […].

Data Science 265

Data Science Publishing Analytics 265

DataKitchen

APRIL 13, 2021

This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Companies that implement DataOps find that they are able to reduce cycle times from weeks (or months) to days, virtually eliminate data errors, increase collaboration, and dramatically improve productivity.

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Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science. Plus, AI can also help find key insights encoded in data.

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CIO Business Intelligence

JANUARY 21, 2025

We may look back at 2024 as the year when LLMs became mainstream, every enterprise SaaS added copilot or virtual assistant capabilities, and many organizations got their first taste of agentic AI. Even simple use cases had exceptions requiring business process outsourcing (BPO) or internal data processing teams to manage.

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O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. Why is high-quality and accessible data foundational? Re-analyzing existing data is often very bad.”

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CIO Business Intelligence

NOVEMBER 7, 2024

And although AI talent is expensive , the use of pre-trained models also makes high-priced data-science talent unnecessary. For example, the company has built a chatbot to help employees with IT service incidents, as well as a virtual agent to provide information for customer service requests.

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O'Reilly on Data

APRIL 9, 2019

Or, why science and engineering are still different disciplines. "A A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering.

Data Science 280

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Rocket-Powered Data Science

JULY 6, 2021

In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade. 2) MLOps became the expected norm in machine learning and data science projects.

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Machine Learning Data-driven Deep Learning IoT 279

O'Reilly on Data

FEBRUARY 18, 2020

It’s also the data source for our annual usage study, which examines the most-used topics and the top search terms. [1]. This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. A drill-down into data, AI, and ML topics.

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Data-driven Software Statistics Marketing 363

datapine

AUGUST 14, 2019

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

NOVEMBER 12, 2024

The hunch was that there were a lot of Singaporeans out there learning about data science, AI, machine learning and Python on their own. We’ve trained more than 400 Singaporeans to become AI engineers, and nearly all of them are today AI engineers, AI consultants, managers, or data scientists.

Modeling 116

Modeling Machine Learning Consulting Deep Learning 116

IBM Big Data Hub

MARCH 30, 2020

As we learn to cope at a personal level with the dynamic development of the COVID-19 outbreak, we are seeing an increasing impact to businesses in every industry. Supply chains are broken, demands are shifting and resources are shrinking. How do you prepare your business for the economic and operational impact?

Data Science 92

Data Science 92

Smart Data Collective

MARCH 2, 2022

Data science is a growing profession. Standards and expectations are rapidly changing, especially in regards to the types of technology used to create data science projects. Most data scientists are using some form of DevOps interface these days. Benefits of Kubernetes for Data Science.

Cost-Benefit 145

Cost-Benefit Data Science Interactive Management 145

O'Reilly on Data

APRIL 5, 2023

That’s because AI algorithms are trained on data. By its very nature, data is an artifact of something that happened in the past. Data is a relic–even if it’s only a few milliseconds old. When we decide which data to use and which data to discard, we are influenced by our innate biases and pre-existing beliefs.

Cost-Benefit 309

Cost-Benefit Data-driven Measurement Visualization 309

Rocket-Powered Data Science

JULY 7, 2019

AGI (Artificial General Intelligence): AI (Artificial Intelligence): Application of Machine Learning algorithms to robotics and machines (including bots), focused on taking actions based on sensory inputs (data). Analytics: The products of Machine Learning and Data Science (such as predictive analytics, health analytics, cyber analytics).

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

AWS Big Data

DECEMBER 4, 2024

This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.

Data Analytics 96

Data Analytics Analytics Data Lake Data Quality 96

O'Reilly on Data

SEPTEMBER 15, 2021

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The average salary for data and AI professionals who responded to the survey was $146,000. We didn’t use the data from these respondents; in practice, discarding this data had no effect on the results.

Machine Learning 363

Machine Learning Statistics Reporting Consulting 363

Rocket-Powered Data Science

MAY 21, 2024

Pure Storage empowers enterprise AI with advanced data storage technologies and validated reference architectures for emerging generative AI use cases. Summary AI devours data. I believe that the time, place, and season for artificial intelligence (AI) data platforms have arrived.

Cost-Benefit 239

Cost-Benefit Unstructured Data Enterprise Technology 239

O'Reilly on Data

FEBRUARY 11, 2020

We suspected that data quality was a topic brimming with interest. The responses show a surfeit of concerns around data quality and some uncertainty about how best to address those concerns. Key survey results: The C-suite is engaged with data quality. Data quality might get worse before it gets better.

Data Quality 363

Data Quality Metadata Data Governance Publishing 363

Smart Data Collective

FEBRUARY 3, 2022

Docker is one of the two most popular DevOps platforms for data scientists. There are a lot of compelling reasons that Docker is becoming very valuable for data scientists and developers. If you are a Data Scientist or Big Data Engineer, you probably find the Data Science environment configuration painful.

Data Science 133

Data Science Data Processing Testing Big Data 133

DataKitchen

SEPTEMBER 17, 2021

Below is our final post (5 of 5) on combining data mesh with DataOps to foster innovation while addressing the challenges of a data mesh decentralized architecture. We see a DataOps process hub like the DataKitchen Platform playing a central supporting role in successfully implementing a data mesh.

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Testing Data Architecture Measurement Visualization 237

DataKitchen

SEPTEMBER 20, 2021

Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. DataOps can and should be implemented in small steps that complement and build upon existing workflows and data pipelines. Figure 1: The four phases of Lean DataOps. production).

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AWS Big Data

NOVEMBER 19, 2024

Amazon Managed Workflows for Apache Airflow (Amazon MWAA), is a managed Apache Airflow service used to extract business insights across an organization by combining, enriching, and transforming data through a series of tasks called a workflow. His core area of expertise includes technology strategy, data analytics, and data science.

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Metadata Cost-Benefit Metrics Optimization 111

O'Reilly on Data

APRIL 30, 2024

Google, Meta, and virtually all other major online aggregators have, over time, come to preference their economic interests over their original promise to their users and to their ecosystems of content and product suppliers or application developers. But it is far from alone.

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Risk Advertising Cost-Benefit Sales 317

datapine

DECEMBER 4, 2019

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

datapine

APRIL 14, 2022

“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran. When you think of big data, you usually think of applications related to banking, healthcare analytics , or manufacturing. However, the usage of data analytics isn’t limited to only these fields. Discover 10.

Big Data 244

Big Data Recreation/Entertainment Analytics Data-driven 244

Smart Data Collective

DECEMBER 3, 2021

Are you looking to get a job in big data? However, it is not easy to get a career in big data. We decided to share some of them here: How do you balance the need for variance with minimizing data bias? You need to make sure that you can answer them accurately, articulately and succinctly to get a job as a data scientist.

Machine Learning 132

Machine Learning Data Science Internet of Things Big Data 132

CIO Business Intelligence

AUGUST 27, 2024

Enterprises can modify the sample applications using their own business data and run the resulting gen AI applications across accelerated data centers and clouds. There are trillions of PDFs generated every year across enterprises, and these PDFs include multiple data types, including text, images, charts, and tables,” Boitano said.

Manufacturing 128

Manufacturing Interactive Enterprise Software 128

DataKitchen

JANUARY 5, 2021

Remote working has revealed the inconsistency and fragility of workflow processes in many data organizations. The data teams share a common objective; to create analytics for the (internal or external) customer. Data Science Workflow – Kubeflow, Python, R. Data Engineering Workflow – Airflow, ETL.

Testing 147

Testing Data Governance Metadata Visualization 147

AWS Big Data

DECEMBER 16, 2024

The need to integrate diverse data sources has grown exponentially, but there are several common challenges when integrating and analyzing data from multiple sources, services, and applications. First, you need to create and maintain independent connections to the same data source for different services.

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