Flipboard

NOVEMBER 22, 2024

This post is part of an ongoing series about governing the machine learning (ML) lifecycle at scale. The data mesh architecture aims to increase the return on investments in data teams, processes, and technology, ultimately driving business value through innovative analytics and ML projects across the enterprise.

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Marktechpost

MAY 8, 2024

FMI’s container-based approach aids in replicating simulations but requires metadata for broader reproducibility and adaptation. MaRDIFlow’s design principle revolves around treating components as abstract objects defined by their input-output behavior and metadata. If you like our work, you will love our newsletter.

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IBM Journey to AI blog

OCTOBER 4, 2023

Artificial intelligence (AI) is now at the forefront of how enterprises work with data to help reinvent operations, improve customer experiences, and maintain a competitive advantage. The first step for successful AI is access to trusted, governed data to fuel and scale the AI. All of this supports the use of AI.

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AWS Machine Learning Blog

DECEMBER 5, 2023

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

By setting up automated policy enforcement and checks, you can achieve cost optimization across your machine learning (ML) environment. Technical tags – These provide metadata about resources. The AWS reserved prefix aws: tags provide additional metadata tracked by AWS. This helps track spending for cost allocation purposes.

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Unite.AI

AUGUST 28, 2024

The company implements AI to the task of preventing and detecting malware. The term “AI” is broadly used as a panacea to equip organizations in the battle against zero-day threats. ML is unfit for the task. Not all AI is equal. Unlike ML, DL is built on neural networks, enabling it to self-learn and train on raw data.

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IBM Journey to AI blog

MAY 9, 2023

Today is a revolutionary moment for Artificial Intelligence (AI). After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. The answer is that generative AI leverages recent advances in foundation models.

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IBM Journey to AI blog

OCTOBER 16, 2023

Artificial intelligence (AI) adoption is still in its early stages. As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. ” Are foundation models trustworthy?

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IBM Journey to AI blog

FEBRUARY 14, 2024

When thinking of artificial intelligence (AI) use cases, the question might be asked: What won’t AI be able to do? The easy answer is mostly manual labor, although the day might come when much of what is now manual labor will be accomplished by robotic devices controlled by AI. We’re all amazed by what AI can do.

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IBM Journey to AI blog

MAY 20, 2024

Machine learning (ML) has become a critical component of many organizations’ digital transformation strategy. From predicting customer behavior to optimizing business processes, ML algorithms are increasingly being used to make decisions that impact business outcomes.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. Such infrastructure should not only address these issues but also scale according to the demands of AI workloads, thereby enhancing business outcomes. Let’s delve into the database portfolio from IBM available on AWS. 

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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IBM Journey to AI blog

APRIL 13, 2023

If your company is in the early stage of its AI journey or has budget constraints, you may struggle to find a deployment system for your model. Building ML infrastructure and integrating ML models with the larger business are major bottlenecks to AI adoption [1,2,3]. Db2 Warehouse on cloud also supports these ML features.

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IBM Journey to AI blog

FEBRUARY 8, 2024

The European Artificial Intelligence Act, while not yet law, is driving new levels of human oversight and regulatory compliance for artificial intelligence (AI) within the European Union. Similar to GDPR for privacy, the EU AI Act has potential to set the tone for upcoming AI regulations worldwide.

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Flipboard

NOVEMBER 27, 2024

The application needs to search through the catalog and show the metadata information related to all of the data assets that are relevant to the search context. The following diagram illustrates the end-to-end architecture, consisting of the metadata API layer, ingestion pipeline, embedding generation workflow, and frontend UI.

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Metadata Auto-complete Data Discovery ML Engineer 149

Marktechpost

JULY 27, 2024

These methods have the potential to greatly exaggerate published results, deceiving the scientific community and the general public about the actual effectiveness of ML models. Due to the intricacy of ML research, which includes pre-training, post-training, and evaluation stages, there is much potential for QRPs. Check out the Paper.

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IBM Journey to AI blog

MAY 9, 2023

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. While the promise of AI isn’t guaranteed and may not come easy, adoption is no longer a choice. So what is stopping AI adoption today? It is an imperative.

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Towards AI

AUGUST 7, 2024

Last Updated on August 8, 2024 by Editorial Team Author(s): Gift Ojeabulu Originally published on Towards AI. Why VS Code might be better for many data scientists and ML engineers than Jupyter Notebook. Essential VS Code Extensions for Data Scientists and ML Engineers.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. There are a few different ways in which authentication across AWS accounts can be achieved when data in the SaaS platform is accessed from SageMaker and when the ML model is invoked from the SaaS platform.

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Unite.AI

SEPTEMBER 14, 2023

AI and Blockchain have emerged as two of the most groundbreaking technical innovations in recent times. Artificial Intelligence (AI) : Enables machines and computers to emulate human thinking and decision-making processes.

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AWS Machine Learning Blog

AUGUST 22, 2024

Building a deployment pipeline for generative artificial intelligence (AI) applications at scale is a formidable challenge because of the complexities and unique requirements of these systems. Generative AI models are constantly evolving, with new versions and updates released frequently.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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AWS Machine Learning Blog

NOVEMBER 26, 2023

Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users.

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AWS Machine Learning Blog

MAY 30, 2024

Generative artificial intelligence (AI) has unlocked fresh opportunities for these use cases. In this post, we introduce the Media Analysis and Policy Evaluation solution, which uses AWS AI and generative AI services to provide a framework to streamline video extraction and evaluation processes.

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Marktechpost

JULY 24, 2024

An improvement in AutoML for dealing with complicated data modalities, including tabular-text, text-vision, and vision-text-tabular configurations, the proposed method simplifies and guarantees the efficiency and adaptability of multimodal ML pipelines. The Sequential Model-Based Optimization (SMBO) approach uses it as a search space.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. In the context of Amazon Bedrock , observability and evaluation become even more crucial.

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AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks.

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Metadata ML Software Engineer Machine Learning 120

Unite.AI

JULY 31, 2023

In the past decade, Artificial Intelligence (AI) and Machine Learning (ML) have seen tremendous progress. Modern AI and ML models can seamlessly and accurately recognize objects in images or video files. The SEER framework allows developers to train large & complex ML models on random data with no supervision, i.e

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AWS Machine Learning Blog

APRIL 19, 2024

Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug molecules faster and more effectively.

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Marktechpost

MAY 30, 2024

Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. Its AI courses provide valuable knowledge and hands-on experience, helping learners build and optimize AI models, understand advanced AI concepts, and apply AI solutions to real-world problems.

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Marktechpost

AUGUST 24, 2024

RAG has gained significant attention because it addresses the need for more precise, context-aware outputs in AI-driven systems. The key innovations in this methodology include generating metadata and synthetic Question and Answer (QA) pairs for each document and introducing the concept of a Meta Knowledge Summary (MK Summary).

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Marktechpost

DECEMBER 2, 2024

The development of machine learning (ML) models for scientific applications has long been hindered by the lack of suitable datasets that capture the complexity and diversity of physical systems. The data is available with a PyTorch interface, allowing for seamless integration into existing ML pipelines.

Machine Learning 116

Machine Learning Metadata ML Large Language Models 116

AWS Machine Learning Blog

JUNE 25, 2024

This solution simplifies the integration of advanced monitoring tools such as Prometheus and Grafana, enabling you to set up and manage your machine learning (ML) workflows with AWS AI Chips. By deploying the Neuron Monitor DaemonSet across EKS nodes, developers can collect and analyze performance metrics from ML workload pods.

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AWS Machine Learning Blog

MAY 2, 2024

Amazon Personalize makes it straightforward to personalize your website, app, emails, and more, using the same machine learning (ML) technology used by Amazon, without requiring ML expertise. If you use Amazon Personalize with generative AI, you can also feed the metadata into prompts. Choose Create solution.

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Metadata Software Engineer Large Language Models Machine Learning 128

Unite.AI

JUNE 11, 2024

It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. ai, IBM Watson AI, Microsoft AzureML, and a lot more. Next, the LightAutoML inner datasets contain CV iterators and metadata that implement validation schemes for the datasets.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Today, generative AI can enable people without SQL knowledge. This generative AI task is called text-to-SQL, which generates SQL queries from natural language processing (NLP) and converts text into semantically correct SQL.

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Metadata LLM Generative AI NLP 138

IBM Journey to AI blog

SEPTEMBER 5, 2024

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. To maximize the value of their AI initiatives, organizations must maintain data integrity throughout its lifecycle.

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