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 139

Machine Learning Machine Learning Data Preparation Data Scientist 139

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 127

Data Science Analytics Analytics Data Scientist 127

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

Data Science Dojo

SEPTEMBER 11, 2024

These experiences facilitate professionals from ingesting data from different sources into a unified environment and pipelining the ingestion, transformation, and processing of data to developing predictive models and analyzing the data by visualization in interactive BI reports.

Power BI 284

Power BI Data Pipeline Data Warehouse Data Engineering 284

Dataconomy

JULY 10, 2023

This training should cover the basics of data science, analytics, and machine learning. Automation can be used to automate a number of tasks involved in decision-making, such as data collection, data preparation, and model deployment. However, there are some key differences between the two fields.

Data Science 182

Data Science Machine Learning Machine Learning Data Scientist 182

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

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. Data Lakes compared to Data Warehouses – two different approaches What a data lake is not also helps to define it.

Data Lakes 221

Data Lakes Data Warehouse Hadoop Machine Learning 221

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. Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling.

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

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 103

Data Warehouse Machine Learning Machine Learning Cloud Data 103

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

IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

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

Becoming Human

MAY 15, 2023

Data Science is a popular as well as vast field; till date, there are a lot of opportunities in this field, and most people, whether they are working professionals or students, everyone want a transition in data science because of its scope. What to do next?

Data Science 75

Data Science Machine Learning Machine Learning Database 75

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

Pickl AI

NOVEMBER 5, 2024

Summary: Data Science and AI are transforming the future by enabling smarter decision-making, automating processes, and uncovering valuable insights from vast datasets. Introduction Data Science and Artificial Intelligence (AI) are at the forefront of technological innovation, fundamentally transforming industries and everyday life.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

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

IBM Data Science in Practice

JANUARY 9, 2024

By Carolyn Saplicki , IBM Data Scientist Industries are constantly seeking innovative solutions to maximize efficiency, minimize downtime, and reduce costs. All data scientists could leverage our patterns during an engagement. We are leveraging Air Compressors data, but the solutions are generalizable.

ML 130

ML ML AI AI 130

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. Understanding their life cycles is critical to unlocking their potential.

Data Analysis 52

Data Analysis Data Analysis Data Science Exploratory Data Analysis 52

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

ODSC - Open Data Science

APRIL 17, 2023

With all the talk about new AI-powered tools and programs feeding the imagination of the internet, we often forget that data scientists don’t always have to do everything 100% themselves. PyCaret allows data professionals to build and deploy machine learning models easily and efficiently. So why is this library so popular?

Data Science 59

Data Science Machine Learning Machine Learning Deep Learning 59

Smart Data Collective

SEPTEMBER 16, 2020

These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning. Data Sourcing. The applications of predictive analytics are extensive and often require four key components to maintain effectiveness.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

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 153

ML ML AWS AI 153

ODSC - Open Data Science

MAY 1, 2023

As such, data scientists need to find a different approach for using MLOps to find structure and create a sense of order as LLMs are developed. MLOps is also ideal for data versioning and tracking, so the data scientists can keep track of different iterations of the data used for training and testing LLMs.

Data Scientist 97

Data Scientist Data Science Supervised Learning Data Preparation 97

ODSC - Open Data Science

FEBRUARY 12, 2024

You can even use generative AI to supplement your data sets with synthetic data for privacy or accuracy. Most businesses already recognize the need to automate the actual analysis of data, but you can go further. Automating the data preparation and interpretation phases will take much time and effort out of the equation, too.

Augmented Analytics 98

Augmented Analytics Analytics Analytics Data Science 98

Pickl AI

FEBRUARY 8, 2023

Data Scientists and Data Analysts have been using ChatGPT for Data Science to generate codes and answers rapidly. For example, a machine learning platform can use ChatGPT to generate synthetic data to train models, increasing the size and diversity of the training data.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

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.

AWS 123

AWS Data Preparation Azure Data Scientist 123

ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. The choice is yours.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

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

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

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 Kubeflow documentation.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Ocean Protocol

NOVEMBER 28, 2024

Introduction The Formula 1 Prediction Challenge: 2024 Mexican Grand Prix brought together data scientists to tackle one of the most dynamic aspects of racing — pit stop strategies. The challenge demonstrated the intersection of sports and data science by combining real-world datasets with predictive modeling.

Cross Validation 85

Cross Validation Decision Trees Data Scientist Data Science 85

IBM Journey to AI blog

OCTOBER 23, 2024

IBM® SPSS Statistics is a leading comprehensive statistical software that provides predictive models and advanced statistical techniques to derive actionable insights from data. For many businesses, research institutions, data scientists, data analyst experts and statisticians, SPSS Statistics is the standard for statistical analysis.

Python 87

Python Data Analysis Data Analysis Data Preparation 87

Towards AI

AUGUST 16, 2023

Figure 4: The ModelOps process [Wikipedia] The Machine Learning Workflow Machine learning requires experimenting with a wide range of datasets, data preparation, and algorithms to build a model that maximizes some target metric(s). There is no standard way to package and deploy models. References [1] J. Damji and M. 19, 2021. [2]

Machine Learning 98

Machine Learning Machine Learning ML ML 98

AWS Machine Learning Blog

FEBRUARY 22, 2023

This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. Boomi’s data science team implemented a Markov chain model that could be applied to common integration sequences, or steps, on their platform, hence the name Step Suggest. These tools integrate via API into Boomi’s core service offering.

AWS 80

AWS ML ML Data Science 80

Dataconomy

MARCH 27, 2023

RPA uses a graphical user interface (GUI) to interact with applications and websites, while ML uses algorithms and statistical models to analyze data. 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

AWS Machine Learning Blog

NOVEMBER 16, 2023

With SageMaker MLOps tools, teams can easily train, test, troubleshoot, deploy, and govern ML models at scale to boost productivity of data scientists and ML engineers while maintaining model performance in production.

ML 106

ML ML AWS AI 106