AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

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AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. cdk.json – Contains metadata, and feature flags.

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Google Research AI blog

JUNE 14, 2023

Marcos Seefelder, Software Engineer, and Daniel Duckworth, Research Software Engineer, Google Research When choosing a venue, we often find ourselves with questions like the following: Does this restaurant have the right vibe for a date? Is there good outdoor seating? Are there enough screens to watch the game?

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AWS Machine Learning Blog

NOVEMBER 8, 2023

The MONAI AI models and applications can be hosted on Amazon SageMaker , which is a fully managed service to deploy machine learning (ML) models at scale. AHI provides API access to ImageSet metadata and ImageFrames. Metadata contains all DICOM attributes in a JSON document. In the customized inference.py

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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AWS Machine Learning Blog

MARCH 2, 2023

When end-users ask natural language questions, Amazon Kendra uses machine learning (ML) algorithms to understand the context and return the most relevant answers. The Amazon Kendra’s S3 connector supports indexing documents and their associated metadata stored in an S3 bucket. Leave Metadata files prefix folder location blank.

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AWS Machine Learning Blog

APRIL 7, 2023

As one of the most prominent use cases to date, machine learning (ML) at the edge has allowed enterprises to deploy ML models closer to their end-customers to reduce latency and increase responsiveness of their applications. Even ground and aerial robotics can use ML to unlock safer, more autonomous operations. Instances[*].

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The MLOps Blog

OCTOBER 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. It was two or three times more.

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The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded two successful software services companies. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies.

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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. Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 52

Bugra Akyildiz

JUNE 11, 2023

Google built a new system called DIDACT (​​Dynamic Integrated Developer ACTivity), that trains a LLM for all of the software development activities. Libraries This repository includes datasets written by language models, used in their paper on "Discovering Language Model Behaviors with Model-Written Evaluations."

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AWS Machine Learning Blog

DECEMBER 6, 2023

The search precision can also be improved with metadata filtering. To overcome these limitations, we propose a solution that combines RAG with metadata and entity extraction, SQL querying, and LLM agents, as described in the following sections. But how can we implement and integrate this approach to an LLM-based conversational AI?

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image.

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AWS Machine Learning Blog

APRIL 11, 2024

Traditional rule engines or ML-based classification can classify the documents, but often reach a limit on types of document formats and support for the dynamic addition of a new classes of document. An S3 prefix or S3 object metadata can be used to classify gallery images.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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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. and Pandas or Apache Spark DataFrames.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLOps aims to automate and operationalize ML models, enabling smoother transitions to production and deployment.

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The MLOps Blog

FEBRUARY 27, 2023

This article was originally an episode of the MLOps Live , an interactive Q&A session where ML practitioners answer questions from other ML practitioners. Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects. Then we are there to help.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. Instead, you can focus on the higher value-added effort of training jobs at scale in a shorter amount of time and iterating on your ML models faster.

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AWS Machine Learning Blog

APRIL 23, 2024

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals. Nitin Eusebius is a Sr.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

In the rapidly evolving world of AI and machine learning (ML), foundation models (FMs) have shown tremendous potential for driving innovation and unlocking new use cases. This table will hold the endpoint, metadata, and configuration parameters for the model.

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AWS Machine Learning Blog

OCTOBER 25, 2023

Using SageMaker geospatial capabilities, you can efficiently transform or enrich large-scale geospatial datasets, accelerate model building with pre-trained machine learning (ML) models, and explore model predictions and geospatial data on an interactive map using 3D accelerated graphics and built-in visualization tools.

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Metadata Machine Learning ML Software Engineer 86

TensorFlow

NOVEMBER 29, 2023

Posted by Marat Dukhan and Frank Barchard, Software Engineers CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. The metadata can be added during model conversion using the _experimental_supported_accumulation_type attribute of the tf.lite.TargetSpec object: converter.

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Metadata Neural Network Software Engineer Computer Vision 105

AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. You can find Pranav on LinkedIn.

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AWS Machine Learning Blog

APRIL 4, 2024

In this post, we describe how Nielsen Sports modernized a system running thousands of different machine learning (ML) models in production by using Amazon SageMaker multi-model endpoints (MMEs) and reduced operational and financial cost by 75%. We can now research and deploy a new ML model in under 7 days, instead of over 1 month previously.

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Google Research AI blog

AUGUST 15, 2023

Effective recommendations that present students with relevant reading material helps keep students reading, and this is where machine learning (ML) can help. ML has been widely used in building recommender systems for various types of digital content, ranging from videos to books to e-commerce items.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. Then we’re going to talk about adapting foundation models for the enterprise and how that affects the ML lifecycle, and what we need to potentially add to the lifecycle.

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Snorkel AI

MARCH 2, 2023

I am Ali Arsanjani, and I lead partner engineering for Google Cloud, specializing in the area of AI-ML, and I’m very happy to be here today with everyone. Then we’re going to talk about adapting foundation models for the enterprise and how that affects the ML lifecycle, and what we need to potentially add to the lifecycle.

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Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AWS Machine Learning Blog

AUGUST 4, 2023

Amazon Kendra is a highly accurate and simple-to-use intelligent search service powered by machine learning (ML). In addition, the ML-powered intelligent search can accurately find information from unstructured documents with natural language narrative content, for which keyword search is not very effective.

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Metadata Software Engineer ML Machine Learning 76

AWS Machine Learning Blog

MAY 8, 2023

With Amazon SageMaker , you can manage the whole end-to-end machine learning (ML) lifecycle. It offers many native capabilities to help manage ML workflows aspects, such as experiment tracking, and model governance via the model registry. He holds a PhD in Telecommunication Engineering and has experience in software engineering.

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AWS Machine Learning Blog

APRIL 26, 2024

In previous posts, we covered new capabilities like hybrid search support , metadata filtering to improve retrieval accuracy , and how Knowledge Bases for Amazon Bedrock manages the end-to-end RAG workflow. He frequently speaks at AI/ML conferences, events, and meetups around the world.

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Data Ingestion Generative AI Python Software Engineer 101

AWS Machine Learning Blog

MARCH 19, 2024

In the evolving landscape of manufacturing, the transformative power of AI and machine learning (ML) is evident, driving a digital revolution that streamlines operations and boosts productivity. Reformat the Python script, input question, and CSV metadata into a string. AI/ML Solutions Architect at Amazon Web Services.

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AWS Machine Learning Blog

JULY 20, 2023

In recent years, the company has invested heavily in the machine learning (ML) sector by developing strong in-house know-how that has enabled them to realize very ambitious projects such as automatic monitoring of its 2.3 The following is a high-level architecture of the ML pipeline with its main steps. GW of installed capacity.

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The MLOps Blog

MARCH 21, 2023

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. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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