AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. We add this data to Snowflake as a new table.

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Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

AWS recently released Amazon SageMaker geospatial capabilities to provide you with satellite imagery and geospatial state-of-the-art machine learning (ML) models, reducing barriers for these types of use cases. For more information, refer to Preview: Use Amazon SageMaker to Build, Train, and Deploy ML Models Using Geospatial Data.

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The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition. 2 Ensuring and maintaining high-quality data.

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IBM Data Science in Practice

MARCH 8, 2023

The United States published a Blueprint for the AI Bill of Rights. The growth of the AI and Machine Learning (ML) industry has continued to grow at a rapid rate over recent years. Source: A Chat with Andrew on MLOps: From Model-centric to Data-centric AI So how does this data-centric approach fit in with Machine Learning? — Features

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AWS Machine Learning Blog

FEBRUARY 1, 2024

Data is the foundation to capturing the maximum value from AI technology and solving business problems quickly. To unlock the potential of generative AI technologies, however, there’s a key prerequisite: your data needs to be appropriately prepared.

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Iguazio

FEBRUARY 20, 2024

Gen AI is quickly reshaping industries, and the pace of innovation is incredible to witness. While building gen AI application pilots is fairly straightforward, scaling them to production-ready, customer-facing implementations is a novel challenge for enterprises, and especially for the financial services sector.

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AWS Machine Learning Blog

DECEMBER 5, 2023

This post is written in collaboration with Balaji Chandrasekaran, Jennifer Cwagenberg and Andrew Sansom and Eiman Ebrahimi from Protopia AI. In their implementation of generative AI technology, enterprises have real concerns about data exposure and ownership of confidential information that may be sent to LLMs.

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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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phData

APRIL 5, 2024

With Document AI from the Snowflake Data Cloud , organizations can utilize the power of LLMs to automate the process of converting unstructured documents into organized tables with ease! In this blog, we’ll cover what the Document AI tool is, what use cases it solves, and how to integrate it with document processing pipelines.

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AWS Machine Learning Blog

APRIL 19, 2023

Business requirements We are the US squad of the Sportradar AI department. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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Towards AI

OCTOBER 4, 2023

Mike Shakhomirov Originally published on Towards AI. Advanced-Data Engineering and ML Ops with Infrastructure as Code This member-only story is on us. This article is for data and ML Ops engineers who would want to deploy and update ML pipelines using CloudFormation templates.

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Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

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Snorkel AI

JUNE 9, 2023

Snorkel AI wrapped the second day of our The Future of Data-Centric AI virtual conference by showcasing how Snorkel’s data-centric platform has enabled customers to succeed, taking a deep look at Snorkel Flow’s capabilities, and announcing two new solutions.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Companies are finding NLP to be one of the best applications of AI regardless of industry.

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IBM Journey to AI blog

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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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. Their task is to construct and oversee efficient data pipelines.

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Heartbeat

JANUARY 5, 2024

Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data Applications and Data Pipelines This article will provide an overview of LangChain, the problems it addresses, its use cases, and some of its limitations. Python : Great for including AI in Python-based software or data pipelines.

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O'Reilly Media

SEPTEMBER 15, 2021

In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The average salary for data and AI professionals who responded to the survey was $146,000. The results are biased by the survey’s recipients (subscribers to O’Reilly’s Data & AI Newsletter ).

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DagsHub

SEPTEMBER 10, 2023

Join us for a month-long celebration of open-source contributions to Machine Learning and AI projects. Gain hands-on experience building datasets, models, pipelines, and more! Intermediate Data Pipeline : Build data pipelines using DVC for automation and versioning of Open Source Machine Learning projects.

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IBM Journey to AI blog

AUGUST 23, 2023

The recent success of artificial intelligence based large language models has pushed the market to think more ambitiously about how AI could transform many enterprise processes. However, consumers and regulators have also become increasingly concerned with the safety of both their data and the AI models themselves.

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Ocean Protocol

JANUARY 17, 2024

challenges - Conclusion Summary With Predictoor, you can run AI-powered prediction bots or trading bots on crypto price feeds to earn $. The repo provides starting-point predictoor bots, which gather historical CEX price data and build AI/ML models. The pdr-backend v0.2 flows - Challenges in v0.1

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Precisely

APRIL 11, 2024

The project’s first phase focused on leveraging data replication offered by Precisely to enable near real-time replication of midrange data to AWS with support for heterogeneous source and target databases.

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Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring.

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ODSC - Open Data Science

AUGUST 11, 2023

There’s a lot to consider when learning about data science, such as what topics are relevant, format, and so on. To help you build AI better, we’ve created a list of the top ten virtual talks from ODSC West this year so you can learn a variety of topics and from speakers of different backgrounds. Watch all sessions here.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. As MLOps become more relevant to ML demand for strong software architecture skills will increase as well.

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

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Alation

APRIL 14, 2023

And there’s a growing consensus that dumping all data into the cloud data warehouse is too expensive to be sustainable – though presenters expressed doubt that this may be the case for just small use cases. So, how can a data catalog support the critical project of building data pipelines?

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Iguazio

NOVEMBER 16, 2023

An MLOps platform enables streamlining and automating the entire ML lifecycle, from model development and training to deployment and monitoring. In this blog post, we explore what is required from you for each option and provide tools that will help you make the right choice for your organization to scale your ML and AI activities.

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Mlearning.ai

MAY 25, 2023

Such kind of linguistic associations are a result of quite complex neurological computations that happens in our mind, as we have been trained by our parents, teachers and ourselves from experiences and infact genetic data too BUT the ML models would start from scratch with no pre-built understanding of word meaning.

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Iguazio

JANUARY 16, 2024

Sense is a talent engagement company whose platform improves the recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals.

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Precisely

JUNE 2, 2023

Innovators in the industry understand that leading-edge technologies such as AI and machine learning will be a deciding factor in the quest for competitive advantage when moving to the cloud. The right data integration technology can vastly simplify things. Those statements echo what most insurance executives already know.

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AWS Machine Learning Blog

FEBRUARY 1, 2024

Resilience plays a pivotal role in the development of any workload, and generative AI workloads are no different. There are unique considerations when engineering generative AI workloads through a resilience lens. In this post, we discuss the different stacks of a generative AI workload and what those considerations should be.

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Iguazio

JANUARY 16, 2024

Sense is a talent engagement platform that improves recruitment processes with automation, AI and personalization. Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals.

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