Data Science Dojo

FEBRUARY 20, 2023

Spark is well suited to applications that involve large volumes of data, real-time computing, model optimization, and deployment. Read about Apache Zeppelin: Magnum Opus of MLOps in detail AWS SageMaker AWS SageMaker is an AI service that allows developers to build, train and manage AI models.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS).

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

ODSC - Open Data Science

FEBRUARY 2, 2023

Here’s what we found for both skills and platforms that are in demand for data scientist jobs. Data Science Skills and Competencies Aside from knowing particular frameworks and languages, there are various topics and competencies that any data scientist should know. Joking aside, this does infer particular skills.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

AWS Machine Learning Blog

OCTOBER 18, 2023

This post details how Purina used Amazon Rekognition Custom Labels , AWS Step Functions , and other AWS Services to create an ML model that detects the pet breed from an uploaded image and then uses the prediction to auto-populate the pet attributes. AWS CodeBuild is a fully managed continuous integration service in the cloud.

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AWS Machine Learning Blog

MARCH 1, 2023

In this post, we share how Kakao Games and the Amazon Machine Learning Solutions Lab teamed up to build a scalable and reliable LTV prediction solution by using AWS data and ML services such as AWS Glue and Amazon SageMaker. The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account.

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ODSC - Open Data Science

FEBRUARY 17, 2023

The chart below shows 20 in-demand skills that encompass both NLP fundamentals and broader data science expertise. In a change from last year, there’s also a higher demand for those with data analysis skills as well. Having mastery of these two will prove that you know data science and in turn, NLP.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

Flipboard

NOVEMBER 24, 2023

In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges. For example, teams using these platforms missed an easy migration of their AI/ML prototypes to the industrialization of the solution running on AWS.

ML 153

ML ML AWS AI 153

phData

AUGUST 6, 2024

As today’s world keeps progressing towards data-driven decisions, organizations must have quality data created from efficient and effective data pipelines. For customers in Snowflake, Snowpark is a powerful tool for building these effective and scalable data pipelines.

Data Pipeline 52

Data Pipeline Python Data Engineer Data Engineering 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Read more to know.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM). Their task is to construct and oversee efficient data pipelines.

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ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

ML 124

ML ML AWS Python 124

Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

Data Engineering 130

Data Engineering Data Engineer Data Engineering Data Engineering 130

AWS Machine Learning Blog

DECEMBER 20, 2023

Whether logs are coming from Amazon Web Services (AWS), other cloud providers, on-premises, or edge devices, customers need to centralize and standardize security data. After the security log data is stored in Amazon Security Lake, the question becomes how to analyze it. Subscribe an AWS Lambda function to the SQS queue.

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Towards AI

OCTOBER 4, 2023

Photo by Markus Winkler on Unsplash This story explains how to create and orchestrate machine learning pipelines with AWS Step Functions and deploy them using Infrastructure as Code. This article is for data and ML Ops engineers who would want to deploy and update ML pipelines using CloudFormation templates.

Machine Learning 59

Machine Learning Machine Learning AWS ML 59

AWS Machine Learning Blog

DECEMBER 15, 2023

In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics. Without creating and maintaining data pipelines, you will be able to power ML models with your unstructured data stored in Amazon DocumentDB.

Machine Learning 106

Machine Learning Machine Learning AWS ML 106

Mlearning.ai

JULY 11, 2023

Before embarking on a data science transition, it’s crucial to be aware of these key factors. Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. In the data science industry, effective communication and collaboration play a crucial role.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

AWS Machine Learning Blog

SEPTEMBER 28, 2023

This process significantly benefits from the MLOps features of SageMaker, which streamline the data science workflow by harnessing the powerful cloud infrastructure of AWS. The following diagram illustrates the inference pipeline. Click here to open the AWS console and follow along.

AWS 108

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AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. Access to Amazon Bedrock FMs isn’t granted by default.

SQL 113

SQL Data Lakes Data Analyst AWS 113

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.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

AUGUST 22, 2024

This makes managing and deploying these updates across a large-scale deployment pipeline while providing consistency and minimizing downtime a significant undertaking. Generative AI applications require continuous ingestion, preprocessing, and formatting of vast amounts of data from various sources.

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ML ML AI AI 116

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%

AI 145

AI AI Azure AWS 145

AWS Machine Learning Blog

APRIL 12, 2023

In this post, we describe how AWS Partner Airis Solutions used Amazon Lookout for Equipment , AWS Internet of Things (IoT) services, and CloudRail sensor technologies to provide a state-of-the-art solution to address these challenges. It’s an easy way to run analytics on IoT data to gain accurate insights.

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AWS ML ML Machine Learning 96

IBM Journey to AI blog

AUGUST 12, 2024

Here, we’ll discuss the key differences between AIOps and MLOps and how they each help teams and businesses address different IT and data science challenges. AIOPs enables ITOPs personnel to implement predictive alert handling, strengthen data security and support DevOps processes.

Big Data 106

Big Data Big Data ML ML 106

ODSC - Open Data Science

FEBRUARY 6, 2024

Apache Kafka For data engineers dealing with real-time data, Apache Kafka is a game-changer. This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

MARCH 15, 2023

Build a Stocks Price Prediction App powered by Snowflake, AWS, Python and Streamlit — Part 2 of 3 A comprehensive guide to develop machine learning applications from start to finish. Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application.

Python 52

Python AWS Exploratory Data Analysis Machine Learning 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.

AI 74

AI AI Machine Learning Machine Learning 74

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Let’s unlock the power of ETL Tools for seamless data handling.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

ODSC - Open Data Science

JANUARY 7, 2025

The data science job market is rapidly evolving, reflecting shifts in technology and business needs. 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. Joking aside, this does infer particular skills.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team.

ML 52

ML ML DataOps Data Scientist 52

Pickl AI

OCTOBER 10, 2024

It supports batch and real-time data processing, making it a preferred choice for large enterprises with complex data workflows. Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency. AWS Glue AWS Glue is a fully managed ETL service provided by Amazon Web Services.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. First, the data lake is fed from a number of data sources.

ML 52

ML ML DataOps Data Scientist 52

Dataconomy

MAY 26, 2017

Data processing today is done in form of pipelines which include various steps like aggregation, sanitization, filtering and finally generating insights by applying various statistical models. Amazon Kinesis is a platform to build pipelines for streaming data at the scale of terabytes per hour.

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Apache Kafka Data Analysis Data Analysis Big Data 156

Pickl AI

OCTOBER 15, 2024

It supports both batch and real-time data processing , making it highly versatile. Its ability to integrate with cloud platforms like AWS and Azure makes it an excellent choice for businesses moving to the cloud. Apache Nifi Apache Nifi is an open-source ETL tool that automates data flow between systems.

ETL 52

ETL Azure AWS Data Governance 52

Pickl AI

DECEMBER 25, 2024

Cloud-based solutions, such as AWS SageMaker or Google Cloud AI Platform, can be employed to access scalable computing power. Efficient data pipelines and distributed computing frameworks are essential to address these scalability issues effectively.

Natural Language Processing 52

Natural Language Processing Machine Learning Machine Learning AI 52