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.

ML 105

ML ML Engineer Machine Learning Data Scientist 105

Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

AWS Machine Learning Blog

MARCH 30, 2023

He enables customers in the area of spectator sports to innovate and capitalize on their data, delivering high-quality user and fan experiences through machine learning and data science. His skills and areas of expertise include application development, data science, machine learning, and big data.

Machine Learning 72

Machine Learning Data Scientist Metadata Data Science 72

AWS Machine Learning Blog

MARCH 30, 2023

He enables customers in the area of spectator sports to innovate and capitalize on their data, delivering high-quality user and fan experiences through machine learning and data science. His skills and areas of expertise include application development, data science, machine learning, and big data.

Machine Learning 70

Machine Learning Data Scientist ML Data Science 70

AWS Machine Learning Blog

OCTOBER 20, 2023

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

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ML Data Scientist ML Engineer Data Science 101

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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Data Drift Auto-complete ML Automation 99

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. JuMa is now available to all data scientists, ML engineers, and data analysts at BMW Group.

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ML DevOps Data Scientist ML Engineer 95

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

AWS Machine Learning Blog

FEBRUARY 28, 2024

medium instance with the Python 3 (Data Science) kernel. About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. Outside of his work as a Data and ML engineer at Amazon, Sanjeeb Panda is an avid foodie and music enthusiast. text_content=False, json_lines=False).load()

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Metadata LLM Generative AI NLP 111

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

DataRobot Blog

SEPTEMBER 13, 2022

What Do Data Scientists Do? Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. What data scientists do is directly tied to an organization’s AI maturity level.

Data Scientist 52

Data Scientist Automation ML Data Ingestion 52

AWS Machine Learning Blog

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. Rajesh Ramchander is a Senior Data & ML Engineer in Professional Services at AWS.

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IDP Explainability Big Data ML 70

AWS Machine Learning Blog

AUGUST 31, 2023

Architecture overview The architecture is implemented as follows: Data Science Account – Data Scientists conduct their experiments in SageMaker Studio and build an MLOps setup to deploy models to staging/production environments using SageMaker Projects. Ram Vittal is a Principal ML Solutions Architect at AWS.

ML 91

ML Data Scientist Machine Learning Automation 91

Flipboard

MARCH 7, 2023

There are also limited options for ad hoc script customization by users, such as data scientists or ML engineers, due to permissions of the user profile execution role. Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.” Choose Open Launcher.

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Python Automation Data Scientist ML 93

AWS Machine Learning Blog

AUGUST 31, 2023

Architecture overview The architecture is implemented as follows: Data Science Account – Data Scientists conduct their experiments in SageMaker Studio and build an MLOps setup to deploy models to staging/production environments using SageMaker Projects. Ram Vittal is a Principal ML Solutions Architect at AWS.

ML 52

ML Data Scientist Machine Learning Automation 52

AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

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Automation Python Machine Learning ML 92

AWS Machine Learning Blog

APRIL 19, 2023

The resulting training dataset from the processing job can be saved directly as a CSV for model training, or it can be bulk ingested into an offline feature group that can be used for other models and by other data science teams to address a wide variety of other use cases.

ML 77

ML Data Scientist Big Data Machine Learning 77

Snorkel AI

MAY 24, 2023

The Future of Data-centric AI virtual conference will bring together a star-studded lineup of expert speakers from across the machine learning, artificial intelligence, and data science field. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Patil served as the first U.S.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

Snorkel AI

MAY 24, 2023

The Future of Data-centric AI virtual conference will bring together a star-studded lineup of expert speakers from across the machine learning, artificial intelligence, and data science field. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. Patil served as the first U.S.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52