KDnuggets

MARCH 9, 2020

Also: Linear to Logistic Regression, Explained Step by Step; Trends in Machine Learning in 2020; Tokenization and Text Data Preparation with TensorFlow & Keras; The Death of Data Scientists — will AutoML replace them?

Machine Learning 136

Machine Learning Machine Learning Data Preparation Data Scientist 136

DataRobot

DECEMBER 12, 2019

Back in 2012, Harvard Business Review called data scientists “the sexiest job of the 21st century.” That may or may not be true, but I do believe that one of the hardest jobs in the latter half of this decade is that of the executive responsible for developing and implementing AI strategy in the enterprise.

Data Preparation 19

Data Preparation Data Scientist AI AI 19

Data Science Dojo

JUNE 7, 2023

The primary aim is to make sense of the vast amounts of data generated daily by combining statistical analysis, programming, and data visualization. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science 195

Data Science Data Visualization Data Scientist Machine Learning 195

Dataconomy

MARCH 17, 2025

By analyzing data from IoT devices, organizations can perform maintenance tasks proactively, reducing downtime and operational costs. Data preparation Data preparation is a crucial step that includes data cleaning, transforming, and structuring historical data for analysis.

Decision Trees 91

Decision Trees Predictive Analytics Data Preparation Machine Learning 91

Data Science Dojo

APRIL 3, 2023

These tools provide a visual interface for building machine learning pipelines, making the process easier and more efficient for data scientists. These tools are designed to be user-friendly and do not require any coding skills, making it easier for data scientists to build models quickly and efficiently.

ML 195

ML ML Machine Learning Machine Learning 195

AWS Machine Learning Blog

NOVEMBER 21, 2024

Have an S3 bucket to store your data prepared for batch inference. Have an AWS Identity and Access Management (IAM) role for batch inference with a trust policy and Amazon S3 access (read access to the folder containing input data and write access to the folder storing output data).

AI 111

AI AI AWS Data Preparation 111

Data Science Dojo

AUGUST 28, 2023

Similar to traditional Machine Learning Ops (MLOps), LLMOps necessitates a collaborative effort involving data scientists, DevOps engineers, and IT professionals. Some projects may necessitate a comprehensive LLMOps approach, spanning tasks from data preparation to pipeline production.

Exploratory Data Analysis 195

Exploratory Data Analysis Data Preparation Machine Learning Machine Learning 195

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone allows you to create and manage data zones , which are virtual data lakes that store and process your data, without the need for extensive coding or infrastructure management. Solution overview In this section, we provide an overview of three personas: the data admin, data publisher, and data scientist.

Machine Learning 119

Machine Learning Machine Learning Data Governance ML 119

Pickl AI

SEPTEMBER 5, 2024

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

Pickl AI

JULY 2, 2024

Summary: Demystify time complexity, the secret weapon for Data Scientists. Explore practical examples, tools, and future trends to conquer big data challenges. Introduction to Time Complexity for Data Scientists Time complexity refers to how the execution time of an algorithm scales in relation to the size of the input data.

Data Scientist 52

Data Scientist Algorithm Data Science Machine Learning 52

Data Science Dojo

JANUARY 12, 2023

Through data crawling, cataloguing, and indexing, they also enable you to know what data is in the lake. To preserve your digital assets, data must lastly be secured. To comprehend and transform raw, unstructured data for any specific business use, it typically takes a data scientist and specialized tools.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

DataRobot

MARCH 25, 2019

Data scientist time is a precious, expensive commodity. Do you truly understand what your data science talent works on all day? Are they spending way too much time researching data science theory, coding the same data preparation tasks over and over again, and maintaining scripts for model factories?

Data Science 80

Data Science Data Scientist Data Preparation AI 80

DagsHub

FEBRUARY 29, 2024

Data, is therefore, essential to the quality and performance of machine learning models. This makes data preparation for machine learning all the more critical, so that the models generate reliable and accurate predictions and drive business value for the organization. Why do you need Data Preparation for Machine Learning?

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

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? After all the data preparation is time to re-train our baseline model. Have we achieved the performance expected?

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Quality 52

Dataconomy

JULY 10, 2023

Automation can be used to automate a number of tasks involved in decision-making, such as data collection, data preparation, and model deployment. Data science is a broader field that encompasses the collection, cleaning, analysis, and visualization of data.

Data Science 182

Data Science Machine Learning Machine Learning Data Scientist 182

AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

Data Warehouse 111

Data Warehouse Machine Learning Machine Learning Cloud Data 111

Pickl AI

OCTOBER 17, 2023

Learn how Data Scientists use ChatGPT, a potent OpenAI language model, to improve their operations. ChatGPT is essential in the domains of natural language processing, modeling, data analysis, data cleaning, and data visualization. It facilitates exploratory Data Analysis and provides quick insights.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Data Science 52

Alation

AUGUST 31, 2021

In this blog, we will discuss how Alation provides a platform for data scientists and analysts to complete projects and analysis with speed. How will you support your key users in the Data Cloud? Your data scientists and analysts will need access if they’re to conduct modeling and analysis at speed.

Data Scientist 52

Data Scientist Machine Learning Machine Learning Cloud Data 52

AWS Machine Learning Blog

MARCH 22, 2023

However, higher education institutions often lack ML professionals and data scientists. Amazon SageMaker Canvas is a low-code/no-code ML service that enables business analysts to perform data preparation and transformation, build ML models, and deploy these models into a governed workflow.

Machine Learning 93

Machine Learning Machine Learning Data Scientist ML 93

KDnuggets

JULY 31, 2019

Learn the essential skills needed to become a Data Science rockstar; Understand CNNs with Python + Tensorflow + Keras tutorial; Discover the best podcasts about AI, Analytics, Data Science; and find out where you can get the best Certificates in the field.

Data Science 121

Data Science Analytics Analytics Data Scientist 121

Dataconomy

SEPTEMBER 13, 2023

Data scientists dedicate a significant chunk of their time to data preparation, as revealed by a survey conducted by the data science platform Anaconda. This process involves rectifying or discarding abnormal or non-standard data points and ensuring the accuracy of measurements.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Flipboard

NOVEMBER 20, 2024

Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. For detailed instructions on setting up a knowledge base, including data preparation, metadata creation, and step-by-step guidance, refer to Amazon Bedrock Knowledge Bases now supports metadata filtering to improve retrieval accuracy.

AWS 152

AWS Natural Language Processing Machine Learning Machine Learning 152

IBM Journey to AI blog

JULY 30, 2024

Data Scientists and AI experts: Historically we have seen Data Scientists build and choose traditional ML models for their use cases. Data Scientists will typically help with training, validating, and maintaining foundation models that are optimized for data tasks. IBM watsonx.ai

AI 106

AI AI Data Scientist Data Preparation 106

Snorkel AI

DECEMBER 2, 2024

This reduces the reliance on manual data labeling and significantly speeds up the model training process. At its core, Snorkel Flow empowers data scientists and domain experts to encode their knowledge into labeling functions, which are then used to generate high-quality training datasets.

AWS 52

AWS Machine Learning Machine Learning Data Preparation 52

Alation

APRIL 2, 2021

Recently I sat down with the study authors and data scientists at Alation, Andrea Levy and Naveen Kalyanasamy. Talo Thomson, Content Marketing Manager, Alation: You two are data scientists. Why will other data people be interested in these case studies? Get the latest data cataloging news and trends in your inbox.

Data Scientist 52

Data Scientist Analytics Analytics Data Science 52

AWS Machine Learning Blog

DECEMBER 1, 2023

From data collection and cleaning to feature engineering, model building, tuning, and deployment, ML projects often take months for developers to complete. And experienced data scientists can be hard to come by. Data preparation is typically the most time-intensive phase of the ML workflow.

Machine Learning 113

Machine Learning Machine Learning Data Preparation ML 113

Flipboard

MARCH 22, 2023

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. You can import data from multiple data sources, such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Amazon EMR , and Snowflake.

AWS 123

AWS Data Preparation Azure Data Scientist 123

Heartbeat

FEBRUARY 1, 2023

MLOps acts as the link between data scientists and the production team’s operations (a team consisting of machine learning engineers, software engineers, and IT operations professionals) as they work together to develop ML models and supervise the use of ML models in production. They might also help with data preparation and cleaning.

Machine Learning 96

Machine Learning Machine Learning Data Scientist ML 96

Data Science Dojo

SEPTEMBER 11, 2024

In the sales context, this ensures that sales data remains consistent, accurate, and easily accessible for analysis and reporting. Synapse Data Science: Synapse Data Science empowers data scientists to work directly with secured and governed sales data prepared by engineering teams, allowing for the efficient development of predictive models.

Power BI 244

Power BI Data Pipeline Data Warehouse Data Engineering 244

AWS Machine Learning Blog

OCTOBER 5, 2023

We go through several steps, including data preparation, model creation, model performance metric analysis, and optimizing inference based on our analysis. We also go through best practices and optimization techniques during data preparation, model building, and model tuning.

Data Preparation 122

Data Preparation ML ML AWS 122

Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

ML 144

ML ML AWS AI 144

AWS Machine Learning Blog

FEBRUARY 6, 2024

Hugging Face integrates seamlessly with SageMaker, which is a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale. In this section, we describe the major steps involved in data preparation and model training.

AWS 125

AWS ML ML Data Preparation 125

Smart Data Collective

SEPTEMBER 16, 2020

However, many data scientists and business analysts can’t readily lean on automated regression techniques like logistic regression and linear regression. This stems, largely, from the fact that there are certain data regulations in place when it comes to marketing tech and predictive analytics software.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

AWS Machine Learning Blog

NOVEMBER 1, 2024

We discuss the important components of fine-tuning, including use case definition, data preparation, model customization, and performance evaluation. This post dives deep into key aspects such as hyperparameter optimization, data cleaning techniques, and the effectiveness of fine-tuning compared to base models.

Data Preparation 96

Data Preparation Machine Learning Machine Learning ML 96

IBM Data Science in Practice

MARCH 21, 2023

This may be a daunting task for a non-data scientist or a data scientist with little to no experience. This article will walk you though how to approach deep learning modeling through the MVI platform from data preparation to your first deployment. You’re all set!

ML 130

ML ML Deep Learning Deep Learning 130

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The process begins with data preparation, followed by model training and tuning, and then model deployment and management. Data preparation is essential for model training and is also the first phase in the MLOps lifecycle.

AWS 105

AWS Data Preparation ML ML 105

AWS Machine Learning Blog

JANUARY 6, 2023

The vendors evaluated for this MarketScape offer various software tools needed to support end-to-end machine learning (ML) model development, including data preparation, model building and training, model operation, evaluation, deployment, and monitoring. AI life-cycle tools are essential to productize AI/ML solutions.

AWS 90

AWS ML ML Data Preparation 90