ODSC - Open Data Science

APRIL 25, 2023

Hands-on Data-Centric AI: Data Preparation Tuning — Why and How? Be sure to check out her talk, “ Hands-on Data-Centric AI: Data preparation tuning — why and how? Given that data has higher stakes , it only means that you should invest most of your development investment in improving your data quality.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Quality 52

Pickl AI

JULY 24, 2024

Summary: Data quality is a fundamental aspect of Machine Learning. Poor-quality data leads to biased and unreliable models, while high-quality data enables accurate predictions and insights. What is Data Quality in Machine Learning? Bias in data can result in unfair and discriminatory outcomes.

Data Quality 52

Data Quality Machine Learning Machine Learning Clean Data 52

IBM Data Science in Practice

NOVEMBER 28, 2022

Ensuring high-quality data A crucial aspect of downstream consumption is data quality. Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics. This leaves more time for data analysis.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

AWS Machine Learning Blog

NOVEMBER 13, 2024

It helps organizations comply with regulations, manage risks, and maintain operational efficiency through robust model lifecycles and data quality management. Prepare the data to build your model training pipeline. We have two example notebooks in GitHub repository: AbaloneExample and DirectMarketing.

ML 117

ML ML AWS Data Preparation 117

ODSC - Open Data Science

MARCH 13, 2023

Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation. You can also get data science training on-demand wherever you are with our Ai+ Training platform.

Machine Learning 94

Machine Learning Machine Learning Data Wrangling Data Science 94

Dataconomy

MARCH 27, 2023

Limitations: Bias and interpretability:  Machine learning algorithms may reflect biases present in the data used to train them, and it may be challenging to interpret how they arrived at their decisions. On the other hand, ML requires a significant amount of data preparation and model training before it can be deployed.

ML 133

ML ML Machine Learning Machine Learning 133

Pickl AI

MAY 30, 2024

Summary: The Data Science and Data Analysis life cycles are systematic processes crucial for uncovering insights from raw data. Quality data is foundational for accurate analysis, ensuring businesses stay competitive in the digital landscape. billion INR by 2026, with a CAGR of 27.7%. billion INR by 2027.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

Data Science Dojo

JANUARY 12, 2023

Data Lakes compared to Data Warehouses – two different approaches What a data lake is not also helps to define it. Users: data scientists vs business professionals People who are not used to working with raw data frequently find it challenging to explore data lakes.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Check out the Metaflow Docs.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Flipboard

MARCH 22, 2023

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Data Wrangler creates the report from the sampled data.

AWS 123

AWS Data Preparation Azure Data Scientist 123

Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. Join thousands of data leaders on the AI newsletter.

Machine Learning 98

Machine Learning Machine Learning AI AI 98

AWS Machine Learning Blog

MARCH 29, 2023

With Canvas, you can take ML mainstream throughout your organization so business analysts without data science or ML experience can use accurate ML predictions to make data-driven decisions. This means empowering business analysts to use ML on their own, without depending on data science teams.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Pickl AI

DECEMBER 3, 2024

By leveraging GenAI, businesses can personalize customer experiences and improve data quality while maintaining privacy and compliance. Introduction Generative AI (GenAI) is transforming Data Analytics by enabling organisations to extract deeper insights and make more informed decisions.

Analytics 52

Analytics Analytics Data Quality AI 52

AWS Machine Learning Blog

NOVEMBER 14, 2024

It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle. Runs are executions of some piece of data science code and record metadata and generated artifacts.

AWS 108

AWS ML ML Machine Learning 108

Dataconomy

JULY 28, 2023

How to become a data scientist Data transformation also plays a crucial role in dealing with varying scales of features, enabling algorithms to treat each feature equally during analysis Noise reduction As part of data preprocessing, reducing noise is vital for enhancing data quality.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Conduct exploratory analysis and data preparation.

ML 120

ML ML AWS Machine Learning 120

AWS Machine Learning Blog

NOVEMBER 16, 2023

The data science team expected an AI-based automated image annotation workflow to speed up a time-consuming labeling process. Enable a data science team to manage a family of classic ML models for benchmarking statistics across multiple medical units.

ML 130

ML ML AWS AI 130

AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. Matúš Chládek is a Senior Engineering Manager for ML Ops at Zeta Global.

AWS 126

AWS Machine Learning Machine Learning ML 126

IBM Journey to AI blog

DECEMBER 7, 2023

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources. IBM watsonx.data is the next generation OLAP system that can help you make the most of your data.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

Dataconomy

MARCH 27, 2023

Limitations: Bias and interpretability:  Machine learning algorithms may reflect biases present in the data used to train them, and it may be challenging to interpret how they arrived at their decisions. On the other hand, ML requires a significant amount of data preparation and model training before it can be deployed.

ML 70

ML ML Machine Learning Machine Learning 70

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance 102

Data Governance Analytics Analytics Tableau 102

DataRobot Blog

JULY 21, 2022

Evaluate the computing resources and development environment that the data science team will need. Large projects or those involving text, images, or streaming data may need specialized infrastructure. Exploring and Transforming Data. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Machine Learning Data Scientist Data Quality 98

Tableau

SEPTEMBER 23, 2021

Data privacy policy: We all have sensitive data—we need policy and guidelines if and when users access and share sensitive data. Data quality: Gone are the days of “data is data, and we just need more.” Now, data quality matters. Data modeling. Data migration .

Data Governance 97

Data Governance Analytics Analytics Tableau 97

AWS Machine Learning Blog

JULY 31, 2023

Data preparation, feature engineering, and feature impact analysis are techniques that are essential to model building. These activities play a crucial role in extracting meaningful insights from raw data and improving model performance, leading to more robust and insightful results.

ML 98

ML ML Data Preparation Machine Learning 98

Alation

SEPTEMBER 23, 2021

Guided Navigation – Guided navigation provides intelligent suggestions, which guide correct usage of data. Behavioral intelligence, embedded in the catalog, learns from user behavior to enforce best practices through features like data quality flags, which help folks stay compliant as they use data.

Data Preparation 52

Data Preparation SQL Data Governance Data Analysis 52

Pickl AI

NOVEMBER 4, 2024

Additionally, Data Engineers implement quality checks, monitor performance, and optimise systems to handle large volumes of data efficiently. Differences Between Data Engineering and Data Science While Data Engineering and Data Science are closely related, they focus on different aspects of data.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

Data Science 59

Data Science Data Scientist Data Preparation AI 59

Pickl AI

OCTOBER 17, 2023

This blog discusses best practices, real-world use cases, security and privacy considerations, and how Data Scientists can use ChatGPT to their full potential. Machine Learning Models: How Data Scientists Use ChatGPT Data Scientists use ChatGPT as a powerful ally in the ever-evolving field of Data Science.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Data Science 52

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

SEPTEMBER 9, 2024

However, companies can face challenges when using generative AI for data insights, including maintaining data quality, addressing privacy concerns, managing model biases, and integrating AI systems with existing workflows. Domo is a cloud-centered data experiences innovator that empowers users to make data-driven decisions.

AI 126

AI AI AWS ML 126

Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

Data Science 52

Data Science Data Scientist Data Preparation AI 52

Snorkel AI

JANUARY 26, 2024

To achieve the trust, quality, and reliability necessary for production applications, enterprise data science teams must develop proprietary data for use with specialized models. Data scientists can best improve LLM performance on specific tasks by feeding them the right data prepared in the right way.

Data Science 64

Data Science AI AI Machine Learning 64

Pickl AI

JULY 12, 2023

Data manipulation in Data Science is the fundamental process in data analysis. The data professionals deploy different techniques and operations to derive valuable information from the raw and unstructured data. The objective is to enhance the data quality and prepare the data sets for the analysis.

Data Analysis 52

Data Analysis Data Analysis Database Clean Data 52

Alation

FEBRUARY 20, 2020

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. Looking in the rear-view mirror not only affords reflection, but can also show us what’s plausible for the year ahead.

Data Warehouse 52

Data Warehouse Hadoop Data Science Analytics 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

We use a test data preparation notebook as part of this step, which is a dependency for the fine-tuning and batch inference step. When fine-tuning is complete, this notebook is run using run magic and prepares a test dataset for sample inference with the fine-tuned model.

ML 119

ML ML Data Scientist Python 119

Pickl AI

JULY 19, 2023

By maintaining clean and reliable data, businesses can avoid costly mistakes, enhance operational efficiency, and gain a competitive edge in their respective industries. Best Data Hygiene Tools & Software Trifacta Wrangler Pros: User-friendly interface with drag-and-drop functionality. Provides real-time data monitoring and alerts.

Data Quality 40

Data Quality Data Profiling Data Governance Data Preparation 40

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. The right tool can significantly enhance efficiency, scalability, and data quality.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52