IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Data Science Blog

MARCH 3, 2023

Übrigens nicht mehr so stark bei den Data Scientists, auch wenn richtig gute Mitarbeiter ebenfalls rar gesät sind, den größten Bedarf haben Unternehmen eher bei den Data Engineers. Das sind die Kollegen, die die Data Warehouses oder Data Lakes aufbauen und pflegen.

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The MLOps Blog

JANUARY 23, 2023

But, it is not rare that data engineers and database administrators process, control, and store terabytes of data in projects that are not related to machine learning. When it comes to data complexity, it is for sure that in machine learning, we are dealing with much more complex data. DVC Git LFS neptune.ai

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ODSC - Open Data Science

MARCH 30, 2023

5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists Between its ability to perform data analysis and ease-of-use, here are 5 reasons why SQL is still ideal for new data scientists to get into the field. But there’s a problem: how on earth do you hook up that GPU?

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. What is data engineering?

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ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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ODSC - Open Data Science

JANUARY 30, 2024

Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.

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ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform.

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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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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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AWS Machine Learning Blog

DECEMBER 7, 2023

Fatma Nur Dumlupınar Keşir received her Bachelor’s Degree from Industrial Engineering Department at Boğaziçi University. She then joined Getir in 2022 as a data scientist and has worked on Recommendation Engine projects, Mathematical Programming for Workforce Planning.

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Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Data and governance foundations – This function uses a data mesh architecture for setting up and operating the data lake, central feature store, and data governance foundations to enable fine-grained data access. This framework considers multiple personas and services to govern the ML lifecycle at scale.

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Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. However, there are some critical differences between the two companies.

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Alation

FEBRUARY 13, 2020

Expansion in our business model is driven by the number of users of the data catalog, which means that our average customer is virally successful relative to their initial investment. The Alation Data Catalog is taking years of data lake and self-service analytics investments and driving them from investments to insights.

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ODSC - Open Data Science

APRIL 3, 2023

This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing. While there’s a need for analyzing smaller datasets on your laptop, expanding into TB+ datasets requires a whole new set of skills and data analytics frameworks.

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Use Multiple Data Models With on-premise data warehouses, storing multiple copies of data can be too expensive.

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Alation

JULY 19, 2022

These days, data scientists are in high demand. Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. For these reasons, finding and evaluating data is often time-consuming. How Data Catalogs Help Data Scientists Ask Better Questions.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly. JuMa automatically provisions a new AWS account for the workspace.

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Alation

JULY 7, 2022

To answer these questions we need to look at how data roles within the job market have evolved, and how academic programs have changed to meet new workforce demands. In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. The data scientist.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai Check out the Kubeflow documentation.

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AWS Machine Learning Blog

MAY 15, 2023

His team is responsible for designing, implementing, and maintaining end-to-end machine learning algorithms and data-driven solutions for Getir. Mutlu Polatcan is a Staff Data Engineer at Getir, specializing in designing and building cloud-native data platforms. He loves combining open-source projects with cloud services.

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AWS Machine Learning Blog

DECEMBER 18, 2023

By using these capabilities, businesses can efficiently store, manage, and analyze time-series data, enabling data-driven decisions and gaining a competitive edge. Prior joining AWS, as a Data/Solution Architect he implemented many projects in Big Data domain, including several data lakes in Hadoop ecosystem.

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Alation

JUNE 21, 2022

The audience grew to include data scientists (who were even more scarce and expensive) and their supporting resources (e.g., After that came data governance , privacy, and compliance staff. Power business users and other non-purely-analytic data citizens came after that. Data engineers want to catalog data pipelines.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. And one of the biggest challenges that we see is taking an idea, an experiment, or an ML experiment that data scientists might be running in their notebooks and putting that into production.

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IBM Journey to AI blog

JULY 17, 2023

Within watsonx.ai, users can take advantage of open-source frameworks like PyTorch, TensorFlow and scikit-learn alongside IBM’s entire machine learning and data science toolkit and its ecosystem tools for code-based and visual data science capabilities.

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The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. This allows data scientists to keep their focus on the creation of models or their continuous improvement.

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ODSC - Open Data Science

APRIL 24, 2023

To cluster the data we have to calculate distances between IPs — The number of all possible IP pairs is very large, and we had to solve the scale problem. Data Processing and Clustering Our data is stored in a Data Lake and we used PrestoDB as a query engine.

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IBM Journey to AI blog

MARCH 14, 2024

With newfound support for open formats such as Parquet and Apache Iceberg, Netezza enables data engineers, data scientists and data analysts to share data and run complex workloads without duplicating or performing additional ETL.

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Alation

MARCH 29, 2021

With more data than ever before, the ability to find the right data has become harder than ever. Yet businesses need to find data to make data-driven decisions. However, data engineers, data scientists, data stewards, and chief data officers face the challenge of finding data easily.

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Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. By keeping the data in cloud storage instead of native BigQuery tables, you can reduce your storage costs while maintaining the ability to query the data.

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Flipboard

DECEMBER 18, 2023

Sessions ANT203 | What’s new in Amazon Redshift Watch this session to learn about the newest innovations within Amazon Redshift—the petabyte-scale AWS Cloud data warehousing solution. Easily build and train machine learning models using SQL within Amazon Redshift to generate predictive analytics and propel data-driven decision-making.

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The MLOps Blog

MARCH 21, 2023

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. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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