IBM Journey to AI blog

OCTOBER 12, 2023

They’re built on machine learning algorithms that create outputs based on an organization’s data or other third-party big data sources. Sometimes, these outputs are biased because the data used to train the model was incomplete or inaccurate in some way.

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Data Scientist Metadata Responsible AI Explainability 234

Pickl AI

JANUARY 12, 2025

Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business? Besides, there is a balance between the precision of traditional data analysis and the innovative potential of explainable artificial intelligence.

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Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

Pickl AI

DECEMBER 9, 2024

Summary: This article provides a comprehensive guide on Big Data interview questions, covering beginner to advanced topics. Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 What is Big Data?

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Big Data Explainability Metadata Data Quality 52

Pickl AI

FEBRUARY 17, 2025

Introduction: The Reality of Machine Learning Consider a healthcare organisation that implemented a Machine Learning model to predict patient outcomes based on historical data. However, once deployed in a real-world setting, its performance plummeted due to data quality issues and unforeseen biases.

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Machine Learning Algorithm Explainability Data Quality 52

IBM Journey to AI blog

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

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Metadata Data Quality Data Discovery Data Scientist 130

AWS Machine Learning Blog

FEBRUARY 18, 2025

The following sections further explain the main components of the solution: ETL pipelines to transform the log data, agentic RAG implementation, and the chat application. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

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The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture.

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AWS Machine Learning Blog

MARCH 18, 2025

Regulatory compliance By integrating the extracted insights and recommendations into clinical trial management systems and EHRs, this approach facilitates compliance with regulatory requirements for data capture, adverse event reporting, and trial monitoring. He helps customers implement big data, machine learning, and analytics solutions.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle.

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ML Machine Learning Auto-complete Auto-classification 89

Mlearning.ai

MAY 16, 2023

It involves breaking down the data into smaller chunks that can be processed in parallel across multiple nodes, and then combining the results of those processing tasks to produce a final output. Batch Processing Design Pattern The batch Processing Design Pattern is commonly used for processing large amounts of data in batches.

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Explainability ETL Big Data Machine Learning 52

Pickl AI

OCTOBER 9, 2023

Data Management and Preprocessing for Accurate Predictions Data Quality is Paramount: The foundation of robust ML in demand forecasting lies in high-quality data. Retailers must ensure data is clean, consistent, and free from anomalies. Consistently review and purify data to uphold its accuracy.

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AWS Machine Learning Blog

JUNE 3, 2024

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

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Generative AI Categorization Auto-complete Auto-classification 109

Flipboard

MARCH 22, 2023

We also detail the steps that data scientists can take to configure the data flow, analyze the data quality, and add data transformations. Finally, we show how to export the data flow and train a model using SageMaker Autopilot. Data Wrangler creates the report from the sampled data.

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IDP Data Scientist Categorization Data Quality 123

Unite.AI

AUGUST 15, 2024

Taken together, this explains the poor market adoption of traditional MDM (Master Data Management) solutions. In addition, as new data is added, or data changes, the AI can surface instances where it is struggling to confidently make decisions (“low confidence matches”) and ask the human for input.

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Machine Learning Computer Scientist LLM Large Language Models 147

AWS Machine Learning Blog

AUGUST 29, 2023

The batch inference pipeline includes steps for checking data quality against a baseline created by the training pipeline, as well as model quality (model performance) if ground truth labels are available. If the batch inference pipeline discovers data quality issues, it will notify the responsible data scientist via Amazon SNS.

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Data Scientist Data Quality Python ML 84

Pragnakalp

APRIL 29, 2024

Step 3: Load and process the PDF data For this blog, we will use a PDF file to perform the QnA on it. We’ve selected a research paper titled “DEEP LEARNING APPLICATIONS AND CHALLENGES IN BIG DATA ANALYTICS,” which can be accessed at the following link: [link] Please download the PDF and place it in your working directory.

Deep Learning 59

Deep Learning Big Data Data Science LLM 59

Heartbeat

OCTOBER 23, 2023

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

Pickl AI

JULY 23, 2023

While unstructured data may seem chaotic, advancements in artificial intelligence and machine learning enable us to extract valuable insights from this data type. Big Data Big data refers to vast volumes of information that exceed the processing capabilities of traditional databases.

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Data Analysis Explainability Algorithm Machine Learning 52

Heartbeat

OCTOBER 25, 2023

The following are some critical challenges in the field: a) Data Integration: With the advent of high-throughput technologies, enormous volumes of biological data are being generated from diverse sources. Developing methods for model interpretability and explainability is an active area of research in bioinformatics.

Machine Learning 52

Machine Learning Data Scientist Convolutional Neural Networks Algorithm 52

Pickl AI

OCTOBER 15, 2024

Diagnostic Analytics Diagnostic analytics goes a step further by explaining why certain events occurred. It uses data mining , correlations, and statistical analyses to investigate the causes behind past outcomes. Organisations that harness Big Data can gain comprehensive insights into customer preferences and market trends.

Machine Learning 52

Machine Learning Big Data Artificial Intelligence Artificial Intelligence 52

Pickl AI

NOVEMBER 28, 2024

Knowledge of Cloud Computing and Big Data Tools As complex Machine Learning (ML) models grow, robust infrastructure for large datasets and intensive computations becomes increasingly important. Big Data Tools Integration Big data tools like Apache Spark and Hadoop are vital for managing and processing massive datasets.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

ODSC - Open Data Science

OCTOBER 20, 2023

The Tangent Information Modeler, Time Series Modeling Reinvented Philip Wauters | Customer Success Manager and Value Engineer | Tangent Works Existing techniques for modeling time series data face limitations in scalability, agility, explainability, and accuracy.

Data Science 52

Data Science NLP Machine Learning Data Analysis 52

Pickl AI

APRIL 26, 2024

Top 50+ Interview Questions for Data Analysts Technical Questions SQL Queries What is SQL, and why is it necessary for data analysis? SQL stands for Structured Query Language, essential for querying and manipulating data stored in relational databases. Data Visualisation What are the fundamental principles of data visualisation?

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Data Analysis Machine Learning ETL Explainability 52

Snorkel AI

MAY 12, 2023

Kishore will then double click into some of the opportunities we find here at Capital One, and Bayan will finish us off with a lean into one of our open-source solutions that really is an important contribution to our data-centric AI community. Compute, big data, large commoditized models—all important stages.

Machine Learning 52

Machine Learning Data Scientist Data Science ML 52

Snorkel AI

MAY 12, 2023

Kishore will then double click into some of the opportunities we find here at Capital One, and Bayan will finish us off with a lean into one of our open-source solutions that really is an important contribution to our data-centric AI community. Compute, big data, large commoditized models—all important stages.

Machine Learning 52

Machine Learning Data Scientist Data Science ML 52

The MLOps Blog

AUGUST 11, 2023

But some of these queries are still recurrent and haven’t been explained well. More specifically, embeddings enable neural networks to consume training data in formats that allow extracting features from the data, which is particularly important in tasks such as natural language processing (NLP) or image recognition.

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Unite.AI

JULY 5, 2024

Robust data management is another critical element. Establishing strong information governance frameworks ensures data quality, security and regulatory compliance. Transparent, explainable AI models are necessary for informed decision-making. is currently no universally accepted framework for coding SDOH data.

Data Integration 147

Data Integration Data Quality Algorithm AI Modeling 147

Unite.AI

MARCH 21, 2024

She has innovated and delivered several product lines and services specializing in distributed systems, cloud computing, big data, machine learning and security. Can you explain the concept of ‘data democracy' in the context of today's AI-driven business environment?

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AWS Machine Learning Blog

OCTOBER 24, 2024

The benefits of this solution are: You can flexibly achieve data cleaning, sanitizing, and data quality management in addition to chunking and embedding. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale. You can choose a wide variety of embedding models.

LLM 111

LLM Big Data Architect Software Development Big Data 111

AWS Machine Learning Blog

NOVEMBER 13, 2024

Unified model governance architecture ML governance enforces the ethical, legal, and efficient use of ML systems by addressing concerns like bias, transparency, explainability, and accountability. Prepare the data to build your model training pipeline. The Amazon DataZone project ID is captured in the Documentation section.

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