AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Google Research AI blog

FEBRUARY 2, 2023

Great machine learning (ML) research requires great systems. In this post, we provide an overview of the numerous advances made across Google this past year in systems for ML that enable us to support the serving and training of complex models while easing the complexity of implementation for end users.

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AWS Machine Learning Blog

APRIL 16, 2024

Customers have benefited from this confidentiality and isolation from AWS operators on all Nitro-based EC2 instances since 2017. By design, there is no mechanism for any Amazon employee to access a Nitro EC2 instance that customers use to run their workloads, or to access data that customers send to a machine learning (ML) accelerator or GPU.

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Data Science Dojo

MARCH 15, 2024

It spun off in 2017 from OpenAI by its ex-research scientists, Peter Chen and Pieter Abbeel. Its robots are powered by a technology called the Covariant Brain, a machine-learning (ML) model to train and improve robots’ functionality in real-world applications.

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Data Science Dojo

JANUARY 18, 2023

AI and ML algorithms in fintech supporting financial transactions such as banking and lending will have an impartial say in which individuals have access to banking in 2023. Moreover, data-driven business models can be implemented in the financial sector as well.

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Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems. Packt, ISBN: 978–1787125933, 2017.

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Data Science 101

APRIL 26, 2019

Gopher Data – Gophers doing data analysis, no schedule events, last blog post was 2017 Gopher Notes – Golang in Jupyter Notebooks Lgo – Interactive programming with Jupyter for Golang Gota – Data frames for Go, “The API is still in flux so use at your own risk.” Thoughts from the Community.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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AWS Machine Learning Blog

JANUARY 6, 2023

The vendors evaluated for this MarketScape offer various software tools needed to support end-to-end machine learning (ML) model development, including data preparation, model building and training, model operation, evaluation, deployment, and monitoring. AI life-cycle tools are essential to productize AI/ML solutions. AWS position.

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Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

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NYU Center for Data Science

AUGUST 29, 2024

He is partly supported by the Apple Scholars in AI/ML PhD fellowship. This work aims to improve the application of ML in healthcare settings. My goal is to develop methods that can bridge the gap between modern ML and real problems in clinical decision-making.” Puli earned his MS in Computer Science from NYU in 2017.

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Towards AI

OCTOBER 19, 2024

Figure 3: A “beeswarm” plot from SHAP to examine the impact of different features on income from census data The Challenge of Modern AI Models While these XAI techniques work well for traditional ML models, modern AI systems like Large Language Models (LLMs) present new challenges. References [1] F. Doshi-Velez and B. 2] […]

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Towards AI

AUGUST 8, 2024

Having worked in the AI/ML field for many years, I vividly recall the early days of GenAI when creating even simple coherent text was a Herculean task. Transformers architecture, introduced back in 2017, revolutionized AI, particularly in language models. Can Mixture of Experts (MoE) Models Push GenAI to the Next Level?

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AWS Machine Learning Blog

MAY 16, 2024

Recommendation model using NCF NCF is an algorithm based on a paper presented at the International World Wide Web Conference in 2017. SageMaker pipeline for training SageMaker Pipelines helps you define the steps required for ML services, such as preprocessing, training, and deployment, using the SDK.

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Defined.ai blog

JANUARY 29, 2023

GANs in Data augmentation and Medical imaging GANs are commonly utilized in data augmentation, which is the process of creating additional data for training other machine learning (ML) models. This technique is useful when data is scarce or costly, and where other ML models require large amounts of data to function effectively.

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ML @ CMU

OCTOBER 7, 2024

NeurIPS 2017. Hessel et al., A Reference-free Evaluation Metric for Image Captioning. EMNLP 2021. Heusel et al., GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Betker et al., Improving Image Generation with Better Captions (DALL-E 3). OpenAI 2023. Esser et al.,

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AWS Machine Learning Blog

MARCH 14, 2024

Beyond hardware, data cleaning and processing, model architecture design, hyperparameter tuning, and training pipeline development demand specialized machine learning (ML) skills. Launched in 2017, Amazon SageMaker is a fully managed service that makes it straightforward to build, train, and deploy ML models.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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DataRobot Blog

APRIL 29, 2022

In 2017, additional regulation targeted much smaller financial institutions in the U.S. The FDIC’s action was announced through a Financial Institution Letter, FIL-22-2017. The Framework for ML Governance. The new regulation greatly reduced the minimum threshold for compliance for banks from $50 billion to $1 billion in assets.

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Towards AI

APRIL 1, 2023

One of the core ideas behind ChatGPT dates back to a research paper from 2017. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. Last Updated on April 1, 2023 by Editorial Team Author(s): Jesus Rodriguez Originally published on Towards AI.

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Iguazio

DECEMBER 5, 2023

Successfully training AI and ML models relies not only on large quantities of data, but also on the quality of their annotations. Human annotation helps advance ML and AI model training and evaluation. As such, human annotation is an important step in building successful AI and ML systems. Get the dataset here.

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Towards AI

APRIL 19, 2024

Unlearn.AI 👉Industry domain: AI, Health Tech, Generative AI 👉Location: 2 offices 👉Year founded: 2017 👉Programming languages deployed: CircleCI, GraphQL, Jupyter, NoSQL, Node.js, Java, C++, AWS, CSS, Pandas 👉Benefits: Hybrid work model, vision insurance, virtual coaching services, promotions within the organization, (..)

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Google Research AI blog

MAY 18, 2023

Posted by Lucas Dixon and Michael Terry, co-leads, PAIR, Google Research PAIR (People + AI Research) first launched in 2017 with the belief that “AI can go much further — and be more useful to all of us — if we build systems with people in mind at the start of the process.”

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. The following are a few example RGB images and their labels.

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Mlearning.ai

MAY 15, 2023

describe() count 9994 mean 2017-04-30 05:17:08.056834048 min 2015-01-03 00:00:00 25% 2016-05-23 00:00:00 50% 2017-06-26 00:00:00 75% 2018-05-14 00:00:00 max 2018-12-30 00:00:00 Name: Order Date, dtype: object Average sales per year df['year'] = df['Order Date'].apply(lambda Yearly average sales. Convert it into a graph.

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Towards AI

SEPTEMBER 5, 2023

The WeatherBench 2 dataset aims to enhance ML research in weather forecasting. If modern artificial intelligence were to have a founding document, it would be Google’s 2017 research paper, “Attention Is All You Need.” Five 5-minute reads/videos to keep you learning Transformers Revolutionized AI. What Will Replace Them?

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AWS Machine Learning Blog

MARCH 30, 2023

Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. mkdir $data_dir !cd

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Iguazio

SEPTEMBER 20, 2023

20 Newsgroups A dataset containing roughly 20,000 newsgroup documents spanning a variety of topics, for text classification, text clustering and similar ML applications. million articles from 20,000 news sources across a seven day period in 2017 and 2018. Long-Form Content 14. The newsgroups are: comp.graphics, comp.os.ms-windows.misc,

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Heartbeat

FEBRUARY 6, 2023

When you’re working on an enterprise scale, managing your ML models can be tricky. arXiv preprint arXiv:1701.06659 (2017). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Towards AI

JULY 21, 2023

declassified Blast from the past: Check out this old (2017) blog post from Google introducing transformer models. Fravor, an F/A-18 fighter pilot who engaged a UFO back in 2004 off the coast of Southern California, known colloquially as the “Nimitz incident”. ?

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Towards AI

JULY 21, 2023

The last known comms from 3301 came in April 2017 via Pastebin post. It uses the 2 model architecture: sparse search via Elasticsearch and then a ranker ML model. While most of their puzzles were eventually solved, the very last one, the Liber Primus, is still (mostly) encrypted. Sign Up , it unlocks many cool features!

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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. ML models are however statistical in nature, which theoretically means that their average performance may be very different from the one during a specific training run.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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ML Review

MARCH 31, 2019

In 2017, another group demonstrated that it’s possible for these adversarial examples to generalize to the real world by showing that when printed out, an adversarially constructed image will continue to fool neural networks under different lighting and orientations: Source: Adversarial Examples in the Physical World. Sharif et al.

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Heartbeat

NOVEMBER 8, 2023

Comet, a cloud-based platform for managing machine learning experiments, was developed in 2017 by a team of data scientists and machine learning experts. It offers a range of features that make it easier for users to track and compare different models and ML experiments, such as experiment tracking and model production monitoring.

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AWS Machine Learning Blog

MARCH 21, 2023

The tool makes it easy to access geospatial data sources, run purpose-built processing operations, apply pre-trained ML models, and use built-in visualization tools faster and at scale. against the baseline period (Q3 2017), affecting a total area of 250.21

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