Smart Data Collective

SEPTEMBER 8, 2021

A lot of Open-Source ETL tools house a graphical interface for executing and designing Data Pipelines. It can be used to manipulate, store, and analyze data of any structure. It generates Java code for the Data Pipelines instead of running Pipeline configurations through an ETL Engine. Conclusion.

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The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier. What is an ETL data pipeline in ML? Let’s look at the importance of ETL pipelines in detail.

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ETL Data Pipeline ML ML 59

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 Engineering Data Engineering 52

The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. An integrated model factory to develop, deploy, and monitor models in one place using your preferred tools and languages.

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Machine Learning Machine Learning ML ML 134

The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. How to understand your users (data scientists, ML engineers, etc.).

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

ODSC - Open Data Science

FEBRUARY 17, 2023

Knowing how spaCy works means little if you don’t know how to apply core NLP skills like transformers, classification, linguistics, question answering, sentiment analysis, topic modeling, machine translation, speech recognition, named entity recognition, and others. The chart below shows what’s hot right now.

Data Science 111

Data Science Deep Learning Deep Learning Natural Language Processing 111

Data Science Dojo

JULY 3, 2024

Data science bootcamps are intensive short-term educational programs designed to equip individuals with the skills needed to enter or advance in the field of data science. They cover a wide range of topics, ranging from Python, R, and statistics to machine learning and data visualization.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

APRIL 21, 2025

Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks. Both fields are interdependent for effective data-driven decision-making What is Big Data?

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Big Data Big Data Data Science Machine Learning 52

O'Reilly Media

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. Learning new skills and improving old ones were the most common reasons for training, though hireability and job security were also factors. Women were more likely than men to have advanced degrees, particularly PhDs.

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AI AI Azure AWS 145

ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. Scikit-learn also earns a top spot thanks to its success with predictive analytics and general machine learning.

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Data Science Data Scientist Computer Science Computer Science 98

IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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Big Data Big Data ML ML 106

Pickl AI

MAY 15, 2024

As a Data Analyst, you’ve honed your skills in data wrangling, analysis, and communication. But the allure of tackling large-scale projects, building robust models for complex problems, and orchestrating data pipelines might be pushing you to transition into Data Science architecture.

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Data Analyst Data Scientist Data Science Machine Learning 52

DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Towards AI

APRIL 4, 2023

We sketch out ideas in notebooks, build data pipelines and training scripts, and integrate with a vibrant ecosystem of Python tools. About Edge Impulse Edge Impulse offers the latest in machine learning tooling, enabling all enterprises to build smarter edge products. The Edge Impulse SDK is designed to be one of them.

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ML ML Python Machine Learning 98

ODSC - Open Data Science

DECEMBER 5, 2023

We had bigger sessions on getting started with machine learning or SQL, up to advanced topics in NLP, and of course, plenty related to large language models and generative AI. Top Sessions With sessions both online and in-person in San Francisco, there was something for everyone.

Data Science 98

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 98

Women in Big Data

NOVEMBER 27, 2024

Integrating seamlessly with other Google Cloud services, BigQuery is a powerful solution for organizations seeking efficient and cost-effective large-scale data analysis. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

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.

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Data Engineer Data Engineering Data Engineering Data Engineering 52

Data Science Blog

FEBRUARY 4, 2023

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible.

Cloud Data 147

Cloud Data Data Science Business Intelligence Business Intelligence 147

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

The MLOps Blog

FEBRUARY 5, 2023

We will also get familiar with tools that can help record this data and further analyse it. In the later part of this article, we will discuss its importance and how we can use machine learning for streaming data analysis with the help of a hands-on example. What is streaming data?

Machine Learning 72

Machine Learning Machine Learning Data Pipeline Apache Kafka 72

DataRobot Blog

OCTOBER 3, 2017

Many announcements at Strata centered on product integrations, with vendors closing the loop and turning tools into solutions, most notably: A Paxata-HDInsight solution demo, where Paxata showcased the general availability of its Adaptive Information Platform for Microsoft Azure. 3) Data professionals come in all shapes and forms.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. MLOps tools play a pivotal role in every stage of the machine learning lifecycle. What is MLOps?

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

JANUARY 23, 2023

Data versioning control is an important concept in machine learning, as it allows for the tracking and management of changes to data over time. As data is the foundation of any machine learning project, it is essential to have a system in place for tracking and managing changes to data over time.

ML 52

ML ML Data Lakes Machine Learning 52

The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. However, in scenarios where dataset versioning solutions are leveraged, there can still be various challenges experienced by ML/AI/Data teams.

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ML ML Machine Learning Machine Learning 59

AWS Machine Learning Blog

DECEMBER 20, 2023

A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker. Outside of work, he enjoys playing tennis, cooking, and spending time with family.

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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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

JANUARY 31, 2024

We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices. Python has long been the favorite programming language of data scientists.

Machine Learning 52

Machine Learning Machine Learning Python Data Scientist 52

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 Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Mlearning.ai

JULY 11, 2023

Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. The process or lifecycle of machine learning and deep learning tends to follow a similar pattern in most companies. Another crucial aspect to consider is MLOps (Machine Learning Operations) activities.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

phData

OCTOBER 11, 2024

The financial services industry is at the forefront of the data transformation era, leveraging data, analytics, and machine learning to optimize a wide range of functions. From credit card processing and insurance underwriting to retail banking, data is reshaping the way these organizations operate.

AI 52

AI AI Data Pipeline ML 52

phData

DECEMBER 4, 2023

The software you might use OAuth with includes: Tableau Power BI Sigma Computing If so, you will need an OAuth provider like Okta, Microsoft Azure AD, Ping Identity PingFederate, or a Custom OAuth 2.0 When to use SCIM vs phData's Provision Tool SCIM manages users and groups with Azure Active Directory or Okta. authorization server.

Database 98

Database Clustering SQL Data Pipeline 98

Pickl AI

JUNE 7, 2024

IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Key Features: Graphical Framework: Allows users to design data pipelines with ease using a graphical user interface. Read Further: Azure Data Engineer Jobs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. First, private cloud infrastructure providers like Amazon (AWS), Microsoft (Azure), and Google (GCP) began by offering more cost-effective and elastic resources for fast access to infrastructure.

Data Quality 52

Data Quality Cloud Data Data Pipeline Data Observability 52

DataRobot

SEPTEMBER 14, 2021

DataRobot AI Cloud is the only platform on the market that offers straight through code, straight through automation, or any combination of these approaches in a unified environment that continuously learns. In true multi-cloud fashion, model training can be done in one cloud environment while model deployment can be done in another.

AI 52

AI AI Data Pipeline Data Preparation 52

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

ODSC - Open Data Science

JANUARY 7, 2025

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. As MLOps become more relevant to ML demand for strong software architecture skills will increase aswell.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Pickl AI

OCTOBER 17, 2024

Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability. By automating key tasks, organisations can enhance efficiency and accuracy, ultimately improving the quality of their data pipelines.

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ETL Data Warehouse Data Quality Data Governance 52