Unite.AI

DECEMBER 6, 2024

When framed in the context of the Intelligent Economy RAG flows are enabling access to information in ways that facilitate the human experience, saving time by automating and filtering data and information output that would otherwise require significant manual effort and time to be created.

AI 130

AI AI Data Platform LLM 130

IBM Journey to AI blog

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

ESG 259

ESG Metadata AI AI 259

Unite.AI

JANUARY 18, 2024

AI has proven to be useful in task automation and process optimization, as well as in promoting creativity and innovation. However, as data complexity and diversity continue to increase, there is a growing need for more advanced AI models that can comprehend and handle these challenges effectively.

Computer Vision 264

Computer Vision OpenAI Deep Learning Artificial Intelligence 264

Pickl AI

APRIL 14, 2023

Falling into the wrong hands can lead to the illicit use of this data. Hence, adopting a Data Platform that assures complete data security and governance for an organization becomes paramount. In this blog, we are going to discuss more on What are Data platforms & Data Governance.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization. Workflow B corresponds to model quality drift checks.

Machine Learning 139

Machine Learning DevOps Data Scientist Data Quality 139

IBM Journey to AI blog

OCTOBER 14, 2024

Internally, we’ve implemented a marketing-automation use case where IBM’s brand guidelines and examples were ingested to generate new marketing content and curate it for consistent quality and tone. Similarly, the proliferation of agents will infuse data into an exploding volume and variety of automated workflows.

Business Intelligence 195

Business Intelligence AI AI Machine Learning 195

Marktechpost

JUNE 5, 2024

In addition, organizations that rely on data must prioritize data quality review. Data profiling is a crucial tool. For evaluating data quality. Data profiling gives your company the tools to spot patterns, anticipate consumer actions, and create a solid data governance plan.

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Data Quality Metadata Data Integration ETL 113

IBM Journey to AI blog

JULY 3, 2024

Align your data strategy to a go-forward architecture, with considerations for existing technology investments, governance and autonomous management built in. Look to AI to help automate tasks such as data onboarding, data classification, organization and tagging.

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Data Quality Metadata AI Business Intelligence 112

Unite.AI

DECEMBER 13, 2024

Travel involves dreaming, planning, booking, and sharingprocesses that generate immense amounts of data. However, this data has remained largely underutilized. Yanoljas commitment to leveraging AI and advanced data platforms to improve these experiences was inspiring.

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Software Engineer Data Quality Data Platform Automation 100

TheSequence

JULY 5, 2023

Not surprisingly, data quality and drifting is incredibly important. Many data drift error translates into poor performance of ML models which are not detected until the models have ran. A recent study of data drift issues at Uber reveled a highly diverse perspective.

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Data Drift Data Quality Metadata Data Platform 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. These tools automate the process, making it faster and more accurate.

ETL 52

ETL Data Quality Business Intelligence Machine Learning 52

Flipboard

MARCH 22, 2023

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler. Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Data Wrangler creates the report from the sampled data.

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IDP Data Scientist Categorization Data Quality 123

AWS Machine Learning Blog

NOVEMBER 15, 2023

In order analyze the calls properly, Principal had a few requirements: Contact details: Understanding the customer journey requires understanding whether a speaker is an automated interactive voice response (IVR) system or a human agent and when a call transfer occurs between the two.

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Data Ingestion Metadata NLP Data Scientist 120

The MLOps Blog

MARCH 15, 2023

As the volume of data keeps increasing at an accelerated rate, these data tasks become arduous in no time leading to an extensive need for automation. This is what data processing pipelines do for you. Let’s understand how the other aspects of a data pipeline help the organization achieve its various objectives.

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ETL Categorization Data Integration Automation 59

Pickl AI

OCTOBER 6, 2024

This phase is crucial for enhancing data quality and preparing it for analysis. Transformation involves various activities that help convert raw data into a format suitable for reporting and analytics. Normalisation: Standardising data formats and structures, ensuring consistency across various data sources.

ETL 52

ETL Data Quality Data Integration Big Data 52

AWS Machine Learning Blog

AUGUST 4, 2023

Use the newly launched SageMaker provided project template for Salesforce Data Cloud integration to streamline implementing the preceding steps by providing the following templates: An example notebook showcasing data preparation, building, training, and registering the model. Choose clone repo for both notebooks.

ML 98

ML Categorization AI AI 98

Pickl AI

JUNE 23, 2023

Data Annotation In many AI applications, data annotation is necessary to label or tag the data with relevant information. Data annotation can be done manually or using automated techniques. Training Data Selection A critical aspect of data-centric AI is selecting the right subset of data for training the AI models.

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Continuous Learning Data Quality AI AI 52

ODSC - Open Data Science

OCTOBER 20, 2023

From Data Collection to ML Model Deployment in Less Than 30 Minutes Hudson Buzby | Qwak Solution Architect | Qwak Explore Qwak MLOps Platform, a comprehensive platform tailored to empower data scientists, engineers, and organizations.

Data Science 52

Data Science NLP Machine Learning Data Analysis 52

Marktechpost

JULY 17, 2023

Consider retail automation in the shape of an Amazon Go location, where a computer vision system monitors shoppers to ensure no one leaves with any five-finger discounts. To educate self-driving cars on how to avoid killing people, the business concentrates on some of the most challenging use cases for its synthetic data platform.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

IBM Journey to AI blog

JULY 27, 2023

One example of this is reducing labor burdens by automating ticket assistance through IT operations. Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos.

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Generative AI Chatbots Data Scientist AI 173

Unite.AI

JANUARY 22, 2025

Saket Saurabh , CEO and Co-Founder of Nexla, is an entrepreneur with a deep passion for data and infrastructure. He is leading the development of a next-generation, automated data engineering platform designed to bring scale and velocity to those working with data.

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Auto-complete Automation Machine Learning Data Integration 147

The MLOps Blog

MARCH 21, 2023

It should be able to version the project assets of your data scientists, such as the data, the model parameters, and the metadata that comes out of your workflow. Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Pickl AI

JUNE 11, 2024

Self-Service Analytics User-friendly interfaces and self-service analytics tools empower business users to explore data independently without relying on IT departments. Best Practices for Maximizing Data Warehouse Functionality A data warehouse, brimming with historical data, holds immense potential for unlocking valuable insights.

ETL 52

ETL Data Mining Data Integration Actionable Intelligence 52

IBM Journey to AI blog

AUGUST 4, 2023

It’s often described as a way to simply increase data access, but the transition is about far more than that. When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard.

Machine Learning 183

Machine Learning Metadata Automation AI 183

Pickl AI

DECEMBER 25, 2024

Key Takeaways Business Analytics targets historical insights; Data Science excels in prediction and automation. Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. These tools enable professionals to turn raw data into digestible insights quickly.

Data Science 52

Data Science Data Scientist Machine Learning Algorithm 52