TheSequence

JULY 5, 2023

Uber runs one of the most sophisticated data and machine learning(ML) infrastructures in the planet. It’s Michelangelo platform has been used as the reference architecture for many MLOps platforms over the last few years. Uber innvoations in ML and data span across all categories of the stack.

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The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals.

ML 52

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement.

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The MLOps Blog

MARCH 15, 2023

Data pipeline stages But before delving deeper into the technical aspects of these tools, let’s quickly understand the core components of a data pipeline succinctly captured in the image below: Data pipeline stages | Source: Author What does a good data pipeline look like?

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Heartbeat

DECEMBER 7, 2023

What is Data Mesh? Data Mesh is a new data set that enables units or cross-functional teams to decentralize and manage their data domains while collaborating to maintain data quality and consistency across the organization — architecture and governance approach. We can call fabric texture or actual fabric.

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ODSC - Open Data Science

OCTOBER 20, 2023

You’ll use MLRun, Langchain, and Milvus for this exercise and cover topics like the integration of AI/ML applications, leveraging Python SDKs, as well as building, testing, and tuning your work. In this session, we’ll demonstrate how you can fine-tune a Gen AI model, build a Gen AI application, and deploy it in 20 minutes.

Data Science 52

Data Science NLP Machine Learning Data Analysis 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions. Catch the sessions you missed!

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Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions. Catch the sessions you missed!

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

TheSequence

SEPTEMBER 29, 2024

🔎 ML Research AlphaProteo Google DeepMind published a paper introducing AlphaProteo, a new family of model for protein design. million to improve the data quality problem for building models. Data platform Airbyte can now create connectors directly from the API documentation.

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Marktechpost

JULY 17, 2023

To educate self-driving cars on how to avoid killing people, the business concentrates on some of the most challenging use cases for its synthetic data platform. Its most recent development, made in partnership with the Toyota Research Institute, teaches autonomous systems about object permanence using synthetic data.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

IBM Journey to AI blog

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. 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.

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Pickl AI

JUNE 11, 2024

Self-Service Analytics User-friendly interfaces and self-service analytics tools empower business users to explore data independently without relying on IT departments. Best Practices for Maximizing Data Warehouse Functionality A data warehouse, brimming with historical data, holds immense potential for unlocking valuable insights.

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IBM Journey to AI blog

AUGUST 4, 2023

It’s often described as a way to simply increase data access, but the transition is about far more than that. When effectively implemented, a data democracy simplifies the data stack, eliminates data gatekeepers, and makes the company’s comprehensive data platform easily accessible by different teams via a user-friendly dashboard.

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Pickl AI

DECEMBER 25, 2024

Key Takeaways Business Analytics targets historical insights; Data Science excels in prediction and automation. Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. With added skills, professionals can shift between Business Analytics and Data Science.

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