Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. To pave the way for the growth of AI, BMW Group needed to make a leap regarding scalability and elasticity while reducing operational overhead, software licensing, and hardware management.

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IBM Journey to AI blog

MAY 9, 2023

Today is a revolutionary moment for Artificial Intelligence (AI). Suddenly, everybody is talking about generative AI: sometimes with excitement, other times with anxiety. The answer is that generative AI leverages recent advances in foundation models. AI is already driving results for business.

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AWS Machine Learning Blog

AUGUST 21, 2023

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

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IBM Journey to AI blog

DECEMBER 7, 2023

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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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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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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AWS Machine Learning Blog

MARCH 1, 2023

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored.

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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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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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AWS Machine Learning Blog

AUGUST 4, 2023

This post is co-authored by Daryl Martis, Director of Product, Salesforce Einstein AI. This is the second post in a series discussing the integration of Salesforce Data Cloud and Amazon SageMaker. Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler.

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Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Virtualization and discovery.

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Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Virtualization and discovery.

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Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Sherry Ding is a Senior AI/ML Specialist Solutions Architect.

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Pickl AI

FEBRUARY 21, 2023

The data locations may come from the data warehouse or data lake with structured and unstructured data. The Data Scientist’s responsibility is to move the data to a data lake or warehouse for the different data mining processes. are the various data mining tools.

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AWS Machine Learning Blog

MAY 31, 2024

This means that individuals can ask companies to erase their personal data from their systems and from the systems of any third parties with whom the data was shared. If the data needs to be purged immediately from the service account, you can contact the AWS team to do so.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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ODSC - Open Data Science

JUNE 12, 2023

No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows. Building business-focussed semantic layers in the cloud (the Power BI Service) with data modeling capabilities, such as managing relationships, creating measures, defining incremental refresh, and creating and managing RLS.

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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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Precisely

JULY 18, 2024

Key Takeaways Data Fabric is a modern data architecture that facilitates seamless data access, sharing, and management across an organization. Data management recommendations and data products emerge dynamically from the fabric through automation, activation, and AI/ML analysis of metadata.

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The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Might be useful Unlike manual, homegrown, or open-source solutions, neptune.ai

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AWS Machine Learning Blog

JUNE 22, 2023

In LnW Connect, an encryption process was designed to provide a secure and reliable mechanism for the data to be brought into an AWS data lake for predictive modeling. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

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Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

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Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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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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Pickl AI

OCTOBER 10, 2024

It supports batch and real-time data processing, making it a preferred choice for large enterprises with complex data workflows. Informatica’s AI-powered automation helps streamline data pipelines and improve operational efficiency.

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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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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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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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AWS Machine Learning Blog

JANUARY 26, 2024

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.

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The MLOps Blog

MAY 9, 2023

An ML platform is an environment that standardizes the technology stack for your ML/AI team and provides tools, libraries, and infrastructure for developing, deploying, and operationalizing machine learning applications. Model/training pipeline This pipeline trains one or more models on the training data with preset hyperparameters.

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AWS Machine Learning Blog

AUGUST 15, 2024

Importing data from the SageMaker Data Wrangler flow allows you to interact with a sample of the data before scaling the data preparation flow to the full dataset. This improves time and performance because you don’t need to work with the entirety of the data during preparation.

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DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. DataRobot Data Prep. The post 3 Major Trends at Strata New York 2017 appeared first on DataRobot AI Cloud.

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