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.

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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.

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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.

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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.

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Data Analysis Python ETL Big Data 59

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?

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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.

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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.

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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.

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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.

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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.

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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.

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ODSC - Open Data Science

APRIL 6, 2023

For budding data scientists and data analysts, there are mountains of information about why you should learn R over Python and the other way around. Though both are great to learn, what gets left out of the conversation is a simple yet powerful programming language that everyone in the data science world can agree on, SQL.

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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.

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Pickl AI

AUGUST 9, 2024

Data Integration Tools Technologies such as Apache NiFi and Talend help in the seamless integration of data from various sources into a unified system for analysis. Understanding ETL (Extract, Transform, Load) processes is vital for students. Understanding how to assess model performance is crucial for data scientists.

Big Data 52

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Pickl AI

APRIL 26, 2024

Data Warehousing and ETL Processes What is a data warehouse, and why is it important? A data warehouse is a centralised repository that consolidates data from various sources for reporting and analysis. It is essential to provide a unified data view and enable business intelligence and analytics.

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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.

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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.

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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.

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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.

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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.

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ETL Metadata Business Intelligence Data Analysis 52

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.

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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.

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ML Data Scientist Software Engineer Machine Learning 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.

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Data Mining Machine Learning Data Scientist Artificial Intelligence 98

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.

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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.

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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.

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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.

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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.

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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

Heartbeat

NOVEMBER 6, 2023

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You also learned how to build an Extract Transform Load (ETL) pipeline and discovered the automation capabilities of Apache Airflow for ETL pipelines.

ETL 52

ETL Python Metadata Deep Learning 52

Pickl AI

NOVEMBER 14, 2023

Whether you’re working on Data Analysis, Machine Learning, or any other data-related task, having a well-organized Importing Data in Python Cheat Sheet for importing data in Python is invaluable. So, let me present to you an Importing Data in Python Cheat Sheet which will make your life easier.

Python 52

Python Data Analysis Data Science Data Scientist 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

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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.

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Data Scientist Machine Learning ML Big Data 89

Artificial Corner

JUNE 6, 2023

Spark offered a more versatile programming model, supporting not only MapReduce-like batch processing but also real-time stream processing and interactive data queries. Its ability to efficiently handle iterative algorithms and machine learning tasks made it a popular choice for data scientists and engineers. Morgan Kaufmann.

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

Unite.AI

MAY 21, 2024

Based on our survey of senior decision-makers as well as data scientists and analysts across the U.S., The same report mentions major barriers to AI adoption, including data science gaps and latency in implementation. How does Exasol address these challenges for its clients?

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IBM Journey to AI blog

SEPTEMBER 25, 2023

Uber chose Presto for the flexibility it provides with compute separated from data storage. As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries.

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Automation ETL Data Scientist Data Platform 265

Unite.AI

MARCH 29, 2024

One of the most significant milestones in Mercado Livre's data democratization journey was the introduction of machine learning tools designed for both data scientists and business users. The impact of empowering non-technical users with low-code AI tools has been significant for Mercado Livre.

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Machine Learning ETL AI Tools AI 147