AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

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IBM Journey to AI blog

JULY 17, 2023

offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting. This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models.

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DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

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ODSC - Open Data Science

MARCH 11, 2024

Data Morph: A Cautionary Tale of Summary Statistics Visualization in Bayesian Workflow Using Python or R Harnessing Bayesian Statistics for Business-Centric Data Science Data Engineering and Big Data Join this track to learn the latest techniques and processes to analyze raw data and automate data into mechanical processes and algorithms.

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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. This provides end-to-end support for data engineering and MLOps workflows.

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. However, there are some critical differences between the two companies.

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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

JUNE 5, 2023

In general, it’s a large language model, not altogether that different from language machine learning models we’ve seen in the past that do various natural language processing tasks. One of the most exciting things about language models is you don’t need to hook up a lot of stuff.

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The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

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AWS Machine Learning Blog

JANUARY 26, 2024

Also consider using Amazon Security Lake to automatically centralize security data from AWS environments, SaaS providers, on premises, and cloud sources into a purpose-built data lake stored in your account. Emily Soward is a Data Scientist with AWS Professional Services.

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