Tag: ai/ml

Practical machine learning (ML) is a trial and error process. ML practitioners compare different performance metrics by running ML experiments till you find the best model with a given set of parameters. Because of the experimental nature of ML, there are many reasons for tracking ML experiments and making them reproducible including debugging and compliance.  …

Read more “How you can automate ML experiment tracking with Vertex AI Experiments autologging”

Posted by Piotr Padlewski and Josip Djolonga, Software Engineers, Google Research Large Language Models (LLMs) like PaLM or GPT-3 showed that scaling transformers to hundreds of billions of parameters improves performance and unlocks emergent abilities. The biggest dense models for image understanding, however, have reached only 4 billion parameters, despite research indicating that promising multimodal …

Read more “Scaling vision transformers to 22 billion parameters”

This post was co-written with Tony Momenpour and Drew Clark from KYTC. Government departments and businesses operate contact centers to connect with their communities, enabling citizens and customers to call to make appointments, request services, and sometimes just ask a question. When there are more calls than agents can answer, callers get placed on hold …

Read more “Reduce call hold time and improve customer experience with self-service virtual agents using Amazon Connect and Amazon Lex”

AI has never been more front and center for both consumers and development teams. I have hosted AI conversations, and one of the top comments I get is how to get started in AI without a ton of resources or ML developers on hand. The first place I send people is the treasure trove of …

Read more “Got your eye on AI? Try these 6 interactive tutorials”

There is such a thing as too much data — when it becomes too overwhelming to properly figure out how to use it. Keeping track and making sense of what data your organization has, where it came from, who owns it, and how they can use it, alongside a myriad of other questions, can be a daunting …

Read more “Data Cataloging: Bringing order to chaos”

Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. To realize this potential, however, organizations need ML solutions to be reliable with ML solution development that is …

Read more “Data-centric ML benchmarking: Announcing DataPerf’s 2023 challenges”

Bounding box annotation is a time-consuming and tedious task that requires annotators to create annotations that tightly fit an object’s boundaries. Bounding box annotation tasks, for example, require annotators to ensure that all edges of an annotated object are enclosed in the annotation. In practice, creating annotations that are precise and well-aligned to object edges …

Read more “Snapper provides machine learning-assisted labeling for pixel-perfect image object detection”

Scientific Research with Palantir: A Review of Health Research in 2022 Editor’s note: At Palantir, we support our partners in health and life sciences by providing them with the technology they need to achieve their most critical missions. In recent years, our partners across hospital systems, life science companies, and Federal agencies have expanded their use …

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This is a guest post by Neslihan Erdogan, Global Industrial IT Manager at HAYAT HOLDING. With the ongoing digitization of the manufacturing processes and Industry 4.0, there is enormous potential to use machine learning (ML) for quality prediction. Process manufacturing is a production method that uses formulas or recipes to produce goods by combining ingredients …

Read more “HAYAT HOLDING uses Amazon SageMaker to increase product quality and optimize manufacturing output, saving $300,000 annually”