AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. And although generative AI has appeared in previous events, this year we’re taking it to the next level. And although our track focuses on generative AI, many other tracks have related sessions.

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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. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. The next step is to build ML models using features selected from one or multiple feature groups.

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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.

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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.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. SageMaker Data Wrangler supports fine-grained data access control with Lake Formation and Amazon Athena connections.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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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. and Pandas or Apache Spark DataFrames.

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IBM Journey to AI blog

MAY 9, 2023

Today is a revolutionary moment for Artificial Intelligence (AI). After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. The answer is that generative AI leverages recent advances in foundation models.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Data preparation, train and tune, deploy and monitor. Hope you can all hear me well.

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Snorkel AI

MARCH 2, 2023

Ali Arsanjani, director of cloud partner engineering at Google Cloud , presented a talk entitled “Challenges and Ethics of DLM and LLM Adoption in the Enterprise” at Snorkel AI’s recent Foundation Model Virtual Summit. Data preparation, train and tune, deploy and monitor. Hope you can all hear me well.

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Alation

SEPTEMBER 23, 2021

There are many other features and functions , but to verify that a data catalog functions as platform, look for these five features: Intelligence – A data catalog should leverage AI/ML-driven pattern detection, including popularity, pattern matching, and provenance/impact analysis. Key Features of a Data Catalog.

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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. If all goes well, of course ?

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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The MLOps Blog

OCTOBER 20, 2023

Nevertheless, many data scientists will agree that they can be really valuable – if used well. And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. in a pandas DataFrame) but in the company’s data warehouse (e.g.,

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