AWS Machine Learning Blog

NOVEMBER 7, 2024

Operating such large-scale forecasting requires resilient, reusable, reproducible, and automated machine learning (ML) workflows with fast experimentation and continuous improvements. To improve forecasting accuracy, all involved ML models need to be retrained, and predictions need to be produced weekly, and in some cases daily.

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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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Dataconomy

FEBRUARY 6, 2024

With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector. Why does AI/ML deserve to be the future of the modern world? Let’s understand the crucial role of AI/ML in the tech industry.

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ML @ CMU

MARCH 30, 2023

Our work further motivates novel directions for developing and evaluating tools to support human-ML interactions. Model explanations have been touted as crucial information to facilitate human-ML interactions in many real-world applications where end users make decisions informed by ML predictions.

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Google Research AI blog

FEBRUARY 23, 2023

Commensurate with our mission to demonstrate these societal benefits , Google Research’s programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022.

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Dataversity

DECEMBER 17, 2021

Despite extraordinary advancements in the field, machine learning (ML) and deep learning have seen slow adoption in the enterprise. However, in 2022 AI will evolve to better deliver on its promise. The post Will AI Become the Real Deal in 2022? There will be a new wave of technological advancements that will […].

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Dataconomy

AUGUST 22, 2024

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. On these projects, I mentored numerous ML engineers, fostering a culture of innovation within Petronas. You told us you were implementing these projects in 2020-2022, so it all started amid the Covid-19 times.

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Google Research AI blog

FEBRUARY 21, 2023

With the amazing advances in machine learning (ML) and quantum computing, we now have powerful new tools that enable us to act on our curiosity, collaborate in new ways, and radically accelerate progress toward breakthrough scientific discoveries. You can find other posts in the series here.)

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Dataconomy

DECEMBER 5, 2023

Runway ML, a leading AI video creation platform, has joined forces with Getty Images, the world’s premier visual content provider, to develop innovative AI video models tailored for the entertainment and advertising industries. Let’s talk about Runway ML’s approach.

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Mlearning.ai

AUGUST 11, 2023

ML Implementation — 00 I do not know how I will be proceeding with this project(s) but I plan to document it to some extent. The goal is to utilize ML-Agents with C# and Unity engine to make a couple of ML projects, obviously with visualization. Part 01 of ML Implementation. Until net time. Might take a while to run).

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DataRobot Blog

MARCH 30, 2022

Composable ML. This past summer, DataRobot announced the addition of Composable ML to our AI Cloud platform. However, with the advent of Composable ML, I now have access to built-in sentiment analysis tasks in DataRobot to preprocess the lyrics for me. See DataRobot Composable ML in Action. Request a demo.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Google Research has been at the forefront of this effort, developing many innovations from privacy-safe recommendation systems to scalable solutions for large-scale ML. You can find other posts in the series here.)

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Towards AI

APRIL 4, 2023

Last Updated on April 4, 2023 by Editorial Team Introducing a Python SDK that allows enterprises to effortlessly optimize their ML models for edge devices. With their groundbreaking web-based Studio platform, engineers have been able to collect data, develop and tune ML models, and deploy them to devices. on Tuesday, April 4, 2022

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Eugene Yan

OCTOBER 1, 2022

My three favorite papers, 17 paper summaries, and ML and non-ML lessons.

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Pickl AI

JANUARY 29, 2024

2024 Tech breakdown: Understanding Data Science vs ML vs AI Quoting Eric Schmidt , the former CEO of Google, ‘There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days.’ billion in 2022 to a remarkable USD 484.17 billion by 2029.

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O'Reilly Media

JANUARY 10, 2023

In reality, 2022 has been a fantastic year for AI. GitHub CoPilot also wasn’t new in 2022, but in the last year we’ve heard of more and more programmers who are using ChatGPT to write production code. GPT-3 wasn’t new, of course, but ChatGPT made GPT-3 usable in ways people hadn’t imagined.

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Google Research AI blog

FEBRUARY 14, 2023

In this blog post, we’ll review key learnings and themes from our explorations in 2022. Top Google Research, 2022 & beyond This was the sixth blog post in the “Google Research, 2022 & Beyond” series.

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Google Research AI blog

FEBRUARY 7, 2023

In the last 10 years, AI and ML models have become bigger and more sophisticated — they’re deeper, more complex, with more parameters, and trained on much more data, resulting in some of the most transformative outcomes in the history of machine learning. sub-quadratic with relation to the input sequence length).

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AWS Machine Learning Blog

JANUARY 6, 2023

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. The tools are typically used by data scientists and ML developers from experimentation to production deployment of AI and ML solutions. AWS position.

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Google Research AI blog

FEBRUARY 28, 2023

Adherence to such public health programs is a prevalent challenge, so researchers from Google Research and the Indian Institute of Technology, Madras worked with ARMMAN to design an ML system that alerts healthcare providers about participants at risk of dropping out of the health information program. certainty when used correctly.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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Smart Data Collective

MAY 22, 2022

Unlike ML models trained on datasets, every device gets the level of protection it needs. Are you trying to improve your ransomware defense in 2022? The post How AI Can Supercharge Your Ransomware Defense In 2022? For example, process A may be permitted on one device and blocked on another. It all depends on the risk score.

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Towards AI

JULY 19, 2023

How to Maximize ML Project Success with Efficient Scoping? | In Aug 2022, Forbes stated that somewhere between 60–80% of AI projects are failing, according to different sources. What can you recommend to him as an ML Engineer? Like all other fields, scoping ML projects have many community recommended outlines.

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Towards AI

NOVEMBER 16, 2024

7659–7679, 2022.[16] He, “Global convergence and variance reduction for a class of nonconvex-nonconcave minimax problems,”Advances in Neural Information Processing Systems, vol. 1153–1165, 2020.[15] Lessard, and R. Grosse, “Near-optimal local convergence of alternating gradient descent-ascent for minimaxoptimization,” pp. Breguel, M.

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ODSC - Open Data Science

MAY 30, 2023

Be sure to check out his talk, “ ML Applications in Asset Allocation and Portfolio Management ,” there! The year 2022 presented two significant turnarounds for tech: the first one is the immediate public visibility of generative AI due to ChatGPT. Editor’s note: Peter Schwendner, PhD is a speaker for ODSC Europe this June.

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NYU Center for Data Science

MARCH 15, 2023

To make the opportunity more accessible, the recorded lectures from the previous iteration of the school held January 10–14, 2022, are now publicly available on YouTube! The NYU AI School grew from a 3-day workshop that took place in October 2019, with the first week-long event launched in February 2021.

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Smart Data Collective

JANUARY 3, 2022

Many of us will have already started thinking about our resolutions for 2022, and it’s incredibly similar when it comes to the beautiful world of web design. So, what are we expecting with AI-driven web design in 2022? Often an area of a site that gets forgotten about, 2022 will be the year of the footer. Funky Designs.

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PyImageSearch

DECEMBER 11, 2023

Kicking Off with a Keynote The second day of the Google Machine Learning Community Summit began with an inspiring keynote session by Soonson Kwon, the ML Community Lead at Google. The focus of his presentation was clear and forward-thinking: Accelerate AI/ML research and application.

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AWS Machine Learning Blog

MAY 8, 2024

In 2022, Dialog Axiata made significant progress in their digital transformation efforts, with AWS playing a key role in this journey. This strategic use of AWS services delivers efficiency and scalability of their operations, as well as the implementation of advanced AI/ML applications.

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AWS Machine Learning Blog

OCTOBER 26, 2023

Intuitivo, a pioneer in retail innovation, is revolutionizing shopping with its cloud-based AI and machine learning (AI/ML) transactional processing system. Our AI/ML research team focuses on identifying the best computer vision (CV) models for our system. This significantly reduces training time and cost for product planogram models.

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Dataversity

JANUARY 18, 2022

Artificial intelligence (AI) and machine learning (ML) are two critical technologies that are shaping our future, how we work, and how businesses persevere through future black swan events. The post Why 2022 Will Be the Year of AI, Machine Learning, and Cloud Technology appeared first on DATAVERSITY.

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Towards AI

FEBRUARY 13, 2023

In Aug 2022, Forbes stated that somewhere between 60–80% of AI projects are failing, according to different sources. What can you recommend to him as an ML Engineer? We can also keep giving hundreds of other suggestions to him, both belonging to ML and not belonging to ML. They do all this before a project begins.

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Dataconomy

DECEMBER 13, 2021

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information they can pull from the massive amounts of data generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and.

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Google Research AI blog

JANUARY 18, 2023

Please keep your eye on this space and look for the title “Google Research, 2022 & Beyond” for more articles in the series. With this post, I am kicking off a series in which researchers across Google will highlight some exciting progress we've made in 2022 and present our vision for 2023 and beyond.

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Dataversity

JANUARY 13, 2022

The post The Future of Data Management: Five Predictions for 2022 appeared first on DATAVERSITY. To get the most out of data, it must be consolidated from many systems to unlock valuable business insights and feed applications such as machine learning […].

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Google Research AI blog

MAY 15, 2023

How do we effectively annotate skin tone for use in inclusive machine learning (ML)? 2022 ALL RIGHTS RESERVED. The images are not to be used to train ML models. What does global consensus of skin tone look like? Images by TONL. Copyright TONL.CO Used with permission.

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KDnuggets

JANUARY 17, 2022

In this article, Eric Siegel summarizes the recent KDnuggets poll results and argues that the pervasive failure of ML projects comes from a lack of prudent leadership. He also argues that MLops is not the fundamental missing ingredient – instead, an effective ML leadership practice must be the dog that wags the model-integration tail.

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