Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

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ODSC - Open Data Science

DECEMBER 19, 2023

The field of artificial intelligence is growing rapidly and with it the demand for professionals who have tangible experience in AI and AI-powered tools. A recent study by Gartner predicts that the global AI market will grow from $15.7 So let’s check out some of the top remote AI jobs for pros to look out for in 2024.

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AWS Machine Learning Blog

JUNE 18, 2024

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. Qiong (Jo) Zhang , PhD, is a Senior Partner Solutions Architect at AWS, specializing in AI/ML.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Whether youre new to AI development or an experienced practitioner, this post provides step-by-step guidance and code examples to help you build more reliable AI applications. Chaithanya Maisagoni is a Senior Software Development Engineer (AI/ML) in Amazons Worldwide Returns and ReCommerce organization.

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DagsHub

DECEMBER 11, 2023

It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. However, these tools have functional gaps for more advanced data workflows. It provides both community and enterprise editions.

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IBM Journey to AI blog

OCTOBER 12, 2023

But the implementation of AI is only one piece of the puzzle. The tasks behind efficient, responsible AI lifecycle management The continuous application of AI and the ability to benefit from its ongoing use require the persistent management of a dynamic and intricate AI lifecycle—and doing so efficiently and responsibly.

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IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. ” Are foundation models trustworthy?

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The MLOps Blog

JUNE 27, 2023

With Azure Machine Learning, data scientists can leverage pre-built models, automate machine learning tasks, and seamlessly integrate with other Azure services, making it an efficient and scalable solution for machine learning projects in the cloud. Might be useful Unlike manual, homegrown, or open-source solutions, neptune.ai

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Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

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ODSC - Open Data Science

OCTOBER 15, 2023

Here is the second half of our two-part series of companies changing the face of AI. AI is quickly scaling through dozens of industries as companies, non-profits, and governments are discovering the power of artificial intelligence. They offer a variety of services, including data warehousing, data lakes, and machine learning.

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ODSC - Open Data Science

JANUARY 30, 2024

Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Cleanlab GitHub | Website Cleanlab is focused on data-centric AI (DCAI), providing algorithms/interfaces to help companies (across all industries) improve the quality of their datasets and diagnose/fix various issues in them. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Tableau

DECEMBER 7, 2022

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get. Bring your own AI with AWS.

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Tableau

DECEMBER 7, 2022

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get. Bring your own AI with AWS.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Sherry Ding is a Senior AI/ML Specialist Solutions Architect.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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DagsHub

OCTOBER 23, 2024

This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data. The different tools used in unstructured data management. What is Unstructured Data?

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

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ODSC - Open Data Science

FEBRUARY 14, 2023

It also addresses the strategies and best practices for implementing a data mesh. Applying Engineering Best Practices in Data Lakes Architectures Einat Orr | Ceo and Co-Founder | Treeverse This talk examines why agile methodology, continuous integration, and continuous deployment and production monitoring are essential for data lakes.

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ODSC - Open Data Science

FEBRUARY 15, 2024

How to Practice Data-Centric AI and Have AI Improve its Own Dataset Jonas Mueller | Chief Scientist and Co-Founder | Cleanlab Data-centric AI is poised to be a game changer for Machine Learning projects. Manual labor is no longer the only option for improving data.

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Ocean Protocol

JANUARY 17, 2024

challenges - Conclusion Summary With Predictoor, you can run AI-powered prediction bots or trading bots on crypto price feeds to earn $. The repo provides starting-point predictoor bots, which gather historical CEX price data and build AI/ML models. The pdr-backend v0.2 flows - Challenges in v0.1

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Women in Big Data

NOVEMBER 27, 2024

It features Synapse Studio, a collaborative workspace for data integration, exploration, and analysis, allowing users to manage data pipelines seamlessly. architecture for both structured and unstructured data.

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ODSC - Open Data Science

FEBRUARY 2, 2023

In the realm of data science, this entails becoming familiar with new frameworks and tools, seeing what’s trending in AI, and being able to adapt to changing business requirements. This pushes into big data as well, as many companies now have significant amounts of data and large data lakes that need analyzing.

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Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

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Pickl AI

JULY 30, 2024

Flow-Based Programming : NiFi employs a flow-based programming model, allowing users to create complex data flows using simple drag-and-drop operations. This visual representation simplifies the design and management of data pipelines.

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Alation

JUNE 21, 2022

Over time, we called the “thing” a data catalog , blending the Google-style, AI/ML-based relevancy with more Yahoo-style manual curation and wikis. Thus was born the data catalog. In our early days, “people” largely meant data analysts and business analysts. Data engineers want to catalog data pipelines.

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Iguazio

JANUARY 16, 2024

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals.

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Iguazio

JANUARY 16, 2024

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals.

ML 52

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Pickl AI

DECEMBER 11, 2024

Summary: Lean data management enhances agility by streamlining data processes, reducing waste, and ensuring accuracy and relevance. By leveraging AI and automation, organisations optimise operations and maintain competitive advantage in fast-changing markets. It enables faster decisions, better collaboration, and scalability.

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ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

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Pickl AI

JULY 25, 2024

Data Ingestion Meaning At its core, It refers to the act of absorbing data from multiple sources and transporting it to a destination, such as a database, data warehouse, or data lake. Batch Processing In this method, data is collected over a period and then processed in groups or batches.

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Tableau

DECEMBER 7, 2022

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. But good data—and actionable insights—are hard to get. What is Salesforce Data Cloud for Tableau?

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Precisely

OCTOBER 3, 2024

Companies once relied heavily on on-premises ETL and data lakes, but today, there’s a shift towards cloud-native data environments. DO implement portable data management practices Your data management and integration practices need to be designed with the future in mind.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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