Marktechpost

JUNE 13, 2024

Introduction to Data Engineering Data Engineering Challenges: Data engineering involves obtaining, organizing, understanding, extracting, and formatting data for analysis, a tedious and time-consuming task. Data scientists often spend up to 80% of their time on data engineering in data science projects.

ETL 127

ETL Machine Learning Data Ingestion Big Data 127

Marktechpost

JULY 5, 2024

Programming for Data Science with Python This course series teaches essential programming skills for data analysis, including SQL fundamentals for querying databases and Unix shell basics. Students also learn Python programming, from fundamentals to data manipulation with NumPy and Pandas, along with version control using Git.

Data Science 61

Data Science Machine Learning Data Analysis Software Engineer 61

Unite.AI

SEPTEMBER 22, 2023

Automation has been a key trend in the past few years and that ranges from the design to building of a data warehouse to loading and maintaining, all of that can be automated. So pretty much what is available to a developer or data scientist who is working with the open source libraries and going through their own data science journey.

Large Language Models 278

Large Language Models Automation Artificial Intelligence Artificial Intelligence 278

ODSC - Open Data Science

OCTOBER 18, 2024

The advent of big data, affordable computing power, and advanced machine learning algorithms has fueled explosive growth in data science across industries. However, research shows that up to 85% of data science projects fail to move beyond proofs of concept to full-scale deployment.

Data Science 52

Data Science ETL Data Scientist Data Quality 52

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. The good news is that there are many skills that data scientists already have that are transferable to data engineering.

Data Science 52

Data Science ETL Data Scientist Machine Learning 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Read more to know.

Data Science 52

Data Science Data Scientist ETL Machine Learning 52

Mlearning.ai

FEBRUARY 9, 2023

Working as a Data Scientist — Expectation versus Reality! 11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. With that context, between Model 1 and Model 2, it may be better to select Model 2.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

Applied Data Science

AUGUST 2, 2021

This post is a bitesize walk-through of the 2021 Executive Guide to Data Science and AI  — a white paper packed with up-to-date advice for any CIO or CDO looking to deliver real value through data. Team Building the right data science team is complex. Download the free, unabridged version here.

Data Science 52

Data Science Data Scientist Machine Learning Automation 52

IBM Journey to AI blog

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

Machine Learning 186

Machine Learning Metadata Automation AI 186

Pickl AI

MARCH 19, 2025

Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Introduction Imagine a world where data is a messy jungle, and we need smart tools to turn it into useful insights.

Big Data 52

Big Data Automation Data Science Python 52

Towards AI

MARCH 21, 2023

Last Updated on March 21, 2023 by Editorial Team Author(s): Data Science meets Cyber Security Originally published on Towards AI. Navigating the World of Data Engineering: A Beginner’s Guide. A GLIMPSE OF DATA ENGINEERING ❤ IMAGE SOURCE: BY AUTHOR Data or data? What are ETL and data pipelines?

ETL 98

ETL Data Scientist Data Science Automation 98

Applied Data Science

MAY 6, 2021

This is part of the Full Stack Data Scientist blog series. Building end-to-end data science solutions means developing data collection, feature engineering, model building and model serving processes. If you’re looking to do more with your data, please get in touch via our website.

Data Scientist 52

Data Scientist Automation Python Data Science 52

AWS Machine Learning Blog

MARCH 1, 2023

In addition to the challenge of defining the features for the ML model, it’s critical to automate the feature generation process so that we can get ML features from the raw data for ML inference and model retraining. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

Automation 101

Automation ETL Data Drift ML 101

AWS Machine Learning Blog

JANUARY 10, 2024

An ML model registered by a data scientist needs an approver to review and approve before it is used for an inference pipeline and in the next environment level (test, UAT, or production). When data scientists develop a model, they register it to the SageMaker Model Registry with the model status of PendingManualApproval.

ML 127

ML Machine Learning Data Scientist ETL 127

ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach. You can connect with her on Linkedin.

Big Data 98

Big Data Metadata ETL Data Science 98

The MLOps Blog

SEPTEMBER 7, 2023

Data Scientists and ML Engineers typically write lots and lots of code. From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc.

Software Engineer 52

Software Engineer Machine Learning ETL Data Scientist 52

Marktechpost

JULY 17, 2023

The company’s H20 Driverless AI streamlines AI development and predictive analytics for professionals and citizen data scientists through open source and customized recipes. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98

AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio.

Data Drift 110

Data Drift BERT Data Scientist Python 110

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

Generative AI 127

Generative AI Prompt Engineering Prompt Engineer ML 127

ODSC - Open Data Science

APRIL 4, 2024

There are many factors, but here, we’d like to hone in on the activities that a data science team engages in. Data Science & AI News ODSC’s AI Weekly Recap: Week of March 29th This week’s AI Weekly Recap is all about BrainBox’s new ARIA AI, The UN’s resolution on AI, and Amazon’s $4 billion investment in Anthropic.

Data Science 52

Data Science ETL Big Data Machine Learning 52

ODSC - Open Data Science

APRIL 3, 2023

As the sibling of data science, data analytics is still a hot field that garners significant interest. Companies have plenty of data at their disposal and are looking for people who can make sense of it and make deductions quickly and efficiently.

Data Science 52

Data Science Big Data ETL Deep Learning 52

Marktechpost

AUGUST 27, 2024

Data Analytics Basics for Everyone This IBM course introduces learners to the components of a modern data ecosystem, the roles of Data Analysts, Data Scientists, and Data Engineers, and the tasks they perform, such as data gathering, wrangling, mining, analysis, and communication.

Data Analysis 57

Data Analysis Python Data Scientist Big Data 57

ODSC - Open Data Science

FEBRUARY 6, 2025

Explore the must-attend sessions and cutting-edge tracks designed to equip AI practitioners, data scientists, and engineers with the latest advancements in AI and machine learning. Register by Friday for 50%off! Register by Friday for 50%off! We discuss the open-source Guardrails AI and how you can use it to safeguard your AIapps.

Prompt Engineer 52

Prompt Engineer Prompt Engineering ETL AI 52

AWS Machine Learning Blog

MARCH 5, 2025

About the authors Samantha Stuart is a Data Scientist with AWS Professional Services, and has delivered for customers across generative AI, MLOps, and ETL engagements. He is specialized in the design and implementation of big data and analytical applications on the AWS platform.

Generative AI 77

Generative AI LLM AI AI 77

Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources.

ETL 52

ETL Metadata Business Intelligence Data Analysis 52

Pickl AI

NOVEMBER 4, 2024

Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information. Additionally, Data Engineers implement quality checks, monitor performance, and optimise systems to handle large volumes of data efficiently.

Data Quality 52

Data Quality ETL Data Integration Machine Learning 52

AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them.

Data Scientist 118

Data Scientist Generative AI Machine Learning ML 118

AWS Machine Learning Blog

AUGUST 21, 2023

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. To demonstrate fine-grained data access permissions, we consider the following two users: David, a data scientist on the marketing team.

Data Scientist 97

Data Scientist Machine Learning ML Big Data 97

TransOrg Analytics

AUGUST 9, 2024

Business Applications: Big Data Analytics : Supporting advanced analytics, machine learning, and artificial intelligence applications. Data Archival : Storing historical data that might be needed for future analysis. Data Exploration : Allowing data scientists to explore and experiment with large datasets.

Machine Learning 52

Machine Learning ETL Big Data Data Quality 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

It truly is an all-in-one data lake solution. HPCC Systems and Spark also differ in that they work with distinct parts of the big data pipeline. Spark is more focused on data science, ingestion, and ETL, while HPCC Systems focuses on ETL and data delivery and governance.

Big Data 52

Big Data ETL Data Science Data Ingestion 52

ODSC - Open Data Science

OCTOBER 9, 2024

Confirmed sessions related to software engineering include: Building Data Contracts with Open-Source Tools Chronon — Open Source Data Platform for AI/ML Creating APIs That Data Scientists Will Love with FastAPI, SQLAlchemy, and Pydantic Using APIs in Data Science Without Breaking Anything Don’t Go Over the Deep End: Building an Effective OSS Management (..)

Software Engineer 40

Software Engineer Software Development DevOps Machine Learning 40

The MLOps Blog

OCTOBER 20, 2023

Jupyter notebooks have been one of the most controversial tools in the data science community. Nevertheless, many data scientists will agree that they can be really valuable – if used well. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis.

Data Scientist 52

Data Scientist Python Explainability ETL 52

The MLOps Blog

DECEMBER 7, 2022

If you want to get data scientists, engineers, architects, stakeholders, third-party consultants, and a whole myriad of other actors on board, you have to build two things: 1 Bridges between stakeholders and members from all over an organization—from marketing to sales to engineering—working with data on different theoretical and practical levels.

ML 52

ML ETL Data Scientist Automation 52

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. A lot of you who are already in the data science field must be familiar with BigQuery and its advantages.

Data Science 52

Data Science Big Data ETL Automation 52

AWS Machine Learning Blog

SEPTEMBER 6, 2024

By following these guidelines, data scientists can quantify the user experience delivered by their generative AI pipelines and communicate meaning to business stakeholders, facilitating ready comparisons across different architectures, such as Retrieval Augmented Generation (RAG) pipelines, off-the-shelf or fine-tuned LLMs, or agentic solutions.

Generative AI 92

Generative AI LLM AI AI 92

The MLOps Blog

JANUARY 23, 2023

When it comes to data complexity, it is for sure that in machine learning, we are dealing with much more complex data. First of all, machine learning engineers and data scientists often use data from different data vendors. Some data sets are being corrected by data entry specialists and manual inspectors.

ML 52

ML Machine Learning Metadata Data Scientist 52