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An integrated experience for all your data and AI with Amazon SageMaker Unified Studio (preview)

Flipboard

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. For Project name , enter a name (for example, demo).

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snowflake Snowpark: cloud SQL and Python ML pipelines

Snorkel AI

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Final ODSC East 2023 Schedule Released! Here’s How You Can Spend Your Week

ODSC - Open Data Science

Mini-Bootcamp and VIP Pass holders will have access to four live virtual sessions on data science fundamentals. Confirmed sessions include: An Introduction to Data Wrangling with SQL with Sheamus McGovern, Software Architect, Data Engineer, and AI expert Programming with Data: Python and Pandas with Daniel Gerlanc, Sr.

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State of Machine Learning Survey Results Part Two

ODSC - Open Data Science

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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State of Machine Learning Survey Results Part One

ODSC - Open Data Science

Big data analytics is evergreen, and as more companies use big data it only makes sense that practitioners are interested in analyzing data in-house. Lastly, data engineering is popular as the engineering side of AI is needed to make the most out of data, such as collection, cleaning, extracting, and so on.