Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly. A data scientist team orders a new JuMa workspace in BMW’s Catalog.

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Iguazio

DECEMBER 14, 2023

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. The book contains a full chapter dedicated to generative AI. Key Takeaways 1.

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PyImageSearch

APRIL 24, 2023

Project Structure Accelerating DNNs Using TorchDynamo TorchDynamo vs. TorchScript vs. FX Tracing Data-Dependent Control Flow Non-PyTorch Libraries TorchDynamo FX Graphs Summary Citation Information What’s Behind PyTorch 2.0? data-dependent flows or non-PyTorch libraries). Figure 2: Overview of PrimTorch (source: PyTorch 2.0 ).

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Pickl AI

OCTOBER 18, 2023

In my pursuit to further explore this topic, I decided to bring together the new trends in sports analytics. Technologies like AR/VR, Big Data analytics, biometrics, video-based sensing, and 2D/3D imaging are actively used in video analysis and motion tracking. I was wondering how AI and analytics can help in modifying sports.

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The MLOps Blog

JANUARY 11, 2023

Overall, implementing MLOps in an IoT edge company is important because IoT systems often involve relatively large volumes of data, high levels of complexity, and real-time decision-making. This can enable the company to leverage the data generated by its IoT edge devices to drive business decisions and gain a competitive advantage.

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Snorkel AI

APRIL 16, 2024

Paroma Varma, co-founder at Snorkel AI, and Ali Arsenjani, director of AI/ML partner engineering at Google, discussed the role of data in the development and implementation of LLMs. Customization is crucial, and injecting unique organizational data (their ‘secret sauce’) into these models can help tailor them for specific use cases.

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Snorkel AI

APRIL 16, 2024

Paroma Varma, co-founder at Snorkel AI, and Ali Arsenjani, director of AI/ML partner engineering at Google, discussed the role of data in the development and implementation of LLMs. Customization is crucial, and injecting unique organizational data (their ‘secret sauce’) into these models can help tailor them for specific use cases.

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Mlearning.ai

JANUARY 26, 2023

Prepared by: Carson Ross (OpenGeoHub) , Tom Hengl (OpenGeoHub) , Leandro Parente (OpenGeoHub) , Vasile Crăciunescu (TerraSigna) Data Cubes are highly organised data infrastructures enabling users to run new analyses and generate insights into processes, patterns and trends.

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IBM Journey to AI blog

DECEMBER 19, 2023

This post expands on the topic and provides a maturity model and building blocks that help enterprises accelerate their software supply chain lifecycle in the ever-evolving landscape of enterprise cloud-native software development. This results in trust, efficiency and accuracy in reviews.

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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. Is more data always better? Maybe I’ll start us off here Robert?

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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. Is more data always better? Maybe I’ll start us off here Robert?

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IBM Journey to AI blog

AUGUST 11, 2023

The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. How the models are stored.

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Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. Amazon SNS is fully managed pub/sub service for A2A and A2P messaging.

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Explosion

JANUARY 29, 2023

In 2021, we released thinc-apple-ops. In order to modularize content for reuse, The Guardian’s data science team developed a spaCy-Prodigy NER workflow for quote extraction. We talked with The Guardian’s lead data scientist Anna Vissens about the project for a fascinating blog post. Convolution GPU 96,291 121,623 +26.3%

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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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AWS Machine Learning Blog

NOVEMBER 30, 2023

In addition to offering them choice and—as they expect from us—breadth and depth of capabilities across all layers, customers also tell us they appreciate our data-first approach, and trust that we’ve built everything from the ground up with enterprise-grade security and privacy. Customers must acquire large amounts of data and prepare it.

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AWS Machine Learning Blog

MAY 22, 2023

The power of LLMs comes from their capacity to learn and generalize from extensive and diverse training data. Models can also be trained in a supervised fashion using labeled data to accomplish a set of tasks (for example, is this movie review positive, negative, or neutral). In addition to questions with answers, SQuAD2.0

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The MLOps Blog

JUNE 5, 2023

Every episode is focused on one specific ML topic, and during this one, we talked to David Hershey about GPT-3 and the feature of MLOps. David : Unusual is a venture fund, and my current focus is on our Machine Learning and Data Infrastructure Investments. You don’t need to worry about inference as much or versioning and data.

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