AWS Machine Learning Blog

MARCH 14, 2024

Did anyone make an ace at the 2022 Shriners Children’s Open? Yes, Adam Hadwin made a hole-in-one on hole 14 during round 3 of the 2022 Shriners Children’s Open The following explainer video highlights a few examples of interacting with the virtual assistant. Collin Morikawa’s longest drive at the Shriners Childrens Open was 334 yards.

Generative AI 124

Generative AI LLM AI AI 124

ODSC - Open Data Science

JUNE 6, 2023

October 2022). Spark provides this abstraction layer to make it easy for a data engineer to pass this interface to an ML engineer to implement. He previously worked as a product engineer in infrastructure automation. This function makes it easy to define custom aggregation functions in Python.

Machine Learning 90

Machine Learning Neural Network ML Engineer Data Science 90

AWS Machine Learning Blog

JUNE 26, 2023

To address this challenge, AWS introduced Amazon SageMaker Role Manager in December 2022. This addresses a critical obstacle to wider adoption because ML administrators can now automate their tasks programmatically.

ML 83

ML Data Scientist Automation Machine Learning 83

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 Data Scientist 52

AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas.

ML 115

ML Data Scientist Machine Learning Automation 115

AWS Machine Learning Blog

OCTOBER 11, 2024

Generative AI is used in various use cases, such as content creation, personalization, intelligent assistants, questions and answers, summarization, automation, cost-efficiencies, productivity improvement assistants, customization, innovation, and more. The agent returns the LLM response to the chatbot UI or the automated process.

Metadata 101

Metadata Generative AI LLM Data Ingestion 101

AWS Machine Learning Blog

FEBRUARY 29, 2024

billion EUR (in 2022), a workforce of 336,884 employees (including 221,343 employees in Germany), and operations spanning 130 countries. Major benefits include high scalability of the service, in large part due to automation and a self-service model, and an attractive pricing model that’s primarily based on resource consumption.

Data Scientist 122

Data Scientist DevOps AI AI 122

AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas.

ML 52

ML Data Scientist Machine Learning Automation 52

AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker inference , which was made generally available in April 2022, makes it easy for you to deploy ML models into production to make predictions at scale, providing a broad selection of ML infrastructure and model deployment options to help meet all kinds of ML inference needs.

Large Language Models 99

Large Language Models ML Python Machine Learning 99

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So does that mean feature selection is no longer necessary?

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So does that mean feature selection is no longer necessary?

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So does that mean feature selection is no longer necessary?

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

MAY 26, 2023

[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.

ML 52

ML Python Machine Learning Data Scientist 52

Snorkel AI

MAY 26, 2023

[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.

ML 52

ML Python Machine Learning Data Scientist 52

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

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. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

JANUARY 13, 2023

Standardize building and reuse of AI solutions across business functions and AI practitioners’ personas, while ensuring adherence to enterprise best practices: Automate and standardize the repetitive undifferentiated engineering effort. TR automated this deployment using AWS DevOps services like AWS CodePipeline and AWS CodeBuild.

ML 87

ML Data Scientist Machine Learning Metadata 87

Marktechpost

NOVEMBER 14, 2024

Suddenly, engineers could interact with them simply by prompting, without any initial training. Our platform initially focused on fine-tuning models like BERT in 2021-2022, which were considered large at the time. That’s why we provide an end-to-end platform backed by a dedicated team of ML engineers to help you every step of the way.

Neural Network 129

Neural Network Deep Learning Machine Learning Generative AI 129