Data Science 101

JANUARY 6, 2020

For example, Azure Arc now allows you to run Azure products on a kubernetes container running anywhere (even in Amazon Web Services or Google Cloud) and AWS Outposts runs AWS on-premise. Will AutoML replace data scientists? I think we are going to see more interoperability between the major cloud providers.

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Data Science 101

NOVEMBER 7, 2019

Azure Synapse. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Synapse allows one to use SQL to query petabytes of data, both relational and non-relational, with amazing speed. R Support for Azure Machine Learning. Azure Quantum.

Data Science 106

Data Science Azure SQL Machine Learning 106

AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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ML ML Azure AWS 120

Data Science 101

JANUARY 10, 2020

Google Releases a tool for Automated Exploratory Data Analysis Exploring data is one of the first activities a data scientist performs after getting access to the data. This command-line tool helps to determine the properties and quality of the data as well the predictive power. Courses & Learning.

Data Science 78

Data Science Power BI Cloud Data Exploratory Data Analysis 78

Pickl AI

APRIL 6, 2023

Accordingly, one of the most demanding roles is that of Azure Data Engineer Jobs that you might be interested in. The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. How to Become an Azure Data Engineer?

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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.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Amazon SageMaker Studio offers a comprehensive set of capabilities for machine learning (ML) practitioners and data scientists. The AI platform team’s key objective is to ensure seamless access to Workbench services and SageMaker Studio for all Deutsche Bahn teams and projects, with a primary focus on data scientists and ML engineers.

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Pickl AI

MAY 17, 2023

There are various online Data Science courses that you can consider in Data Science after 10th that includes Data Science course for teenagers. So, how to become a Data Scientist after 10th? Learn working with Big Data: In order to become Data Scientist, working with large datasets is a given.

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Data Scientist Data Science Data Wrangling SQL 52

phData

MAY 16, 2023

Azure Active Directory (AD) is a popular identity and access management service provided by Microsoft which works well as a Single Sign On (SSO) for the Snowflake Data Cloud. In this blog post, we will guide you through the steps of connecting Azure AD SCIM to Snowflake and provide some tips and tricks for ease of implementation.

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Azure Data Engineer Data Engineering Data Engineering 52

Pickl AI

DECEMBER 25, 2024

Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. Together, these tools enable Data Scientists to tackle a broad spectrum of challenges. Typical Applications in Industries Data Science finds applications across industries. Data Scientists require a robust technical foundation.

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Data Science Analytics Analytics Data Scientist 52

ODSC - Open Data Science

FEBRUARY 2, 2023

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Data Science Of course, a data scientist should know data science!

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O'Reilly Media

OCTOBER 19, 2021

ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses. Today, a number of cloud-based, auto-scaling systems are easily available, such as AWS Batch. Data Science Layers. Software Development Layers. Software Architecture.

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ML ML Data Scientist AWS 145

O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. The top certification was for AWS (3.9%

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AI AI Azure AWS 145

Data Science 101

OCTOBER 24, 2020

This is a great talk for data scientists and managers of technology teams. If you do data science in 2020 or beyond, there is a good chance the cloud will be involved.

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Data Science Data Scientist AI AI 136

phData

OCTOBER 2, 2023

The examples used during the learning process are commonly referred to as training data. In a process known as feature engineering, data scientists apply transformations to raw data to create features suitable for ML models to consume. Data platform abstraction. Example dataset that could be used for an ML model.

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Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

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ODSC - Open Data Science

JANUARY 7, 2025

Heres what we noticed from analyzing this data, highlighting whats remained the same over the years, and what additions help make the modern data scientist in2025. Data Science Of course, a data scientist should know data science! Joking aside, this does infer particular skills.

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Data Scientist Data Science Machine Learning Machine Learning 40

Data Science Connect

JULY 26, 2023

Companies like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are leveraging their extensive cloud infrastructure to create edge computing solutions. By deploying a network of edge locations closer to end-users and data sources, cloud providers ensure low-latency data processing for their customers.

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Flipboard

DECEMBER 13, 2024

The explosion of data creation and utilization, paired with the increasing need for rapid decision-making, has intensified competition and unlocked opportunities within the industry. AWS has been at the forefront of domain adaptation, creating a framework to allow creating powerful, specialized AI models.

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DrivenData Labs

MAY 21, 2024

Some projects manage this folder like the data folder and sync it to a canonical store (e.g., AWS S3) separately from source code. Data storage ¶ V1 was designed to encourage data scientists to (1) separate their data from their codebase and (2) store their data on the cloud.

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Data Science Python Data Scientist Data Warehouse 233

IBM Data Science in Practice

FEBRUARY 21, 2023

Photo by Jeroen den Otter on Unsplash Who should read this article: Machine and Deep Learning Engineers, Solution Architects, Data Scientist, AI Enthusiast, AI Founders What is covered in this article? But it’s interoperable on any cloud like Azure, AWS or GCP. Continuous training is the solution.

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Machine Learning Machine Learning AI AI 130

Towards AI

JANUARY 3, 2025

Whether youre an AI enthusiast, a researcher, or a data scientist, understanding the hardware requirements for LLMs is crucial for optimizing performance and cost-effectiveness. Recommended Cloud Setup: Use cloud providers like AWS, Azure, or Google Cloud for access to A100/H100 GPUs. Google Cloud: A2 Mega GPU instances.

AWS 105

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ODSC - Open Data Science

MAY 5, 2023

As such, here are a few data engineering and data science cloud options to make your life easier. Microsoft Azure As one of the most popular data science cloud options, Microsoft Azure is designed for AI. Azure is also compatible with its massive library of other services as well.

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Data Science Data Engineering Data Engineering Data Engineering 52

Dataconomy

SEPTEMBER 4, 2023

Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations. Use ETL (Extract, Transform, Load) processes or data integration tools to streamline data ingestion. Cloud platforms like AWS, Azure, and Google Cloud offer scalable resources that can be provisioned on-demand.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Smart Data Collective

AUGUST 2, 2022

Working with Grape Up, the automotive industry can leverage the most popular cloud services providers: AWS, Azure, Kubernetes, Google Cloud, Alibaba, and OpenStack. Such data can be leveraged to help the organization make data-driven decisions, build a competitive advantage using real-time insights, and create new revenue streams.

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AI AI Machine Learning Machine Learning 141

DagsHub

JULY 25, 2024

Companies like PayPal , Wells Fargo , and MarketAxess leverage H2O.ai's machine learning capabilities to drive data science initiatives. is suitable for enterprises and data scientists looking to accelerate their machine-learning workflows with automated tools and scalable solutions. Guidance for Use H2O.ai Documentation H2O.ai

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Deep Learning Deep Learning Machine Learning Machine Learning 52

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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Mlearning.ai

MAY 23, 2023

Answering one of the most common questions I get asked as a Senior Data Scientist — What skills and educational background are necessary to become a data scientist? Photo by Eunice Lituañas on Unsplash To become a data scientist, a combination of technical skills and educational background is typically required.

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Data Science Data Scientist Machine Learning Machine Learning 52

Towards AI

MARCH 3, 2024

From Code to Cloud: Building CI/CD Pipelines for Containerized Apps Photo by Simon Kadula on Unsplash Introduction U+1F516 Imagine yourself as a Data Scientist, leaning in over your keyboard, sculpting Python scripts that decode the mysteries hidden within your dataset. The workflow is like: Triggered after successful CI.

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Data Science Python Algorithm Azure 116

The MLOps Blog

JULY 18, 2024

Ricard’s goal was to streamline this process to make it faster and more cost-effective while easing the workload for data scientists. Experimentation platform: This platform builds on Metaflow for orchestration and data sharing, and uses Comet for ML experiment tracking.

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ML ML Data Scientist Azure 64

phData

NOVEMBER 8, 2024

Each snapshot has a separate manifest file that keeps track of the data files associated with that snapshot and hence can be restored/queries whenever needed. Versioning also ensures a safer experimentation environment, where data scientists can test new models or hypotheses on historical data snapshots without impacting live data.

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Mlearning.ai

MARCH 15, 2023

Data professionals are in high demand all over the globe due to the rise in big data. The roles of data scientists and data analysts cannot be over-emphasized as they are needed to support decision-making. This article will serve as an ultimate guide to choosing between Data Science and Data Analytics.

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Data Science Analytics Analytics Data Analyst 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. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

AUGUST 12, 2024

It helps companies streamline and automate the end-to-end ML lifecycle, which includes data collection, model creation (built on data sources from the software development lifecycle), model deployment, model orchestration, health monitoring and data governance processes.

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Big Data Big Data ML ML 106

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 Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

JANUARY 13, 2025

The top 10 AI jobs include Machine Learning Engineer, Data Scientist, and AI Research Scientist. Essential skills for these roles encompass programming, machine learning knowledge, data management, and soft skills like communication and problem-solving. Key Skills Experience with cloud platforms (AWS, Azure).

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AI AI Machine Learning Machine Learning 52

Becoming Human

MAY 15, 2023

Note : Now, Start joining Data Science communities on social media platforms. These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey.

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