Data science
fromBusiness Matters
2 months agoData Cleansing: Harnessing Clean Data to Fuel Business Innovation
Clean and accurate data is essential for effective decision-making and organizational innovation.
#data-quality
#data-quality
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#data-engineering
Business intelligence
fromMedium
3 months agoServing Data in the Data Engineering Lifecycle: A Comprehensive Guide
Data engineering culminates in serving data for analytics, ML, and operations.
Data quality and trust are critical in serving data effectively.
Data science
fromMedium
3 months agoUnderstanding Data Generation in Source Systems: How It Works and Real-Time Applications
Data generation is crucial in data engineering lifecycle for reliable processing and transformation.
#marketing-strategy
Artificial intelligence
fromMarTech
4 months agoOvercoming AI challenges in martech to maximize ROI | MarTech
AI integration into martech stacks is complex but essential for improving marketing efficiency.
Organizations must address resistance to AI and focus on data readiness for successful integration.
Artificial intelligence
fromMarTech
4 months agoOvercoming AI challenges in martech to maximize ROI | MarTech
AI integration into martech stacks is complex but essential for improving marketing efficiency.
Organizations must address resistance to AI and focus on data readiness for successful integration.
fromHackernoon
5 months agoLimitations of Current Biomedical Text Mining Community Challenges | HackerNoon
The evaluation tasks within the biomedical text mining community face significant limitations, particularly regarding data representativeness, quality, and participant innovation in solution development.
Online Community Development
#machine-learning
Data science
fromcontributor.insightmediagroup.io
2 months agoEffortless Spreadsheet Normalisation With LLM
Clean, well-structured data is crucial for accurate analysis and decision-making.
Artificial intelligence
fromInfoWorld
3 months ago10 machine learning mistakes and how to avoid them
Machine learning has vast potential but carries significant risks that can lead to project failures.
Data science
fromHackernoon
4 months agoThe Art of Data Creation: Behind the Scenes of AI Training | HackerNoon
Data creation is essential for AI development, focusing on generating realistic datasets for effective model training.
Artificial intelligence
fromWIRED
2 months agoDatabricks Has a Trick That Lets AI Models Improve Themselves
Databricks has developed a method to enhance AI performance with minimal clean data using reinforcement learning and synthetic data.
frommedium.com
1 month agoSpark Scala Exercise 24: Error Handling and Logging in SparkBuild Safe, Auditable ETL Pipelines
"In this exercise, you'll build a defensive Spark ETL pipeline that handles various data issues, ensuring robust processing and reliability. The focus will be on preventing silent failures."
Scala
Marketing tech
fromDigiday
2 months agoHow data marketplaces are evolving to meet buyers' expectations
Data buyers are prioritizing data quality, privacy compliance, and addressability over broad audience availability.
Modern data marketplaces are evolving into specialized platforms responding to buyer expectations.
Artificial intelligence
fromHarvard Business Review
5 months agoIs AI the Right Tool to Solve That Problem?
Organizations face various obstacles when implementing AI, including data issues and lack of clear objectives.
Google DeepMind offers solutions to help guide organizations in successful AI adoption.
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