O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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AWS Machine Learning Blog

FEBRUARY 14, 2024

We build a personalized generative AI travel itinerary planner as part of this example and demonstrate how we can personalize a travel itinerary for a user based on their booking and user profile data stored in Amazon Redshift. This solution contains two major components. This solution contains two major components.

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AWS AI AI Database 141

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

APRIL 7, 2024

Additionally, the elimination of human loop processes has made it possible for AI/ML to construct training data for data annotation and labeling, which has a major influence on geospatial data. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data.

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AWS Machine Learning Blog

DECEMBER 18, 2024

Training an LLM is a compute-intensive and complex process, which is why Fastweb, as a first step in their AI journey, used AWS generative AI and machine learning (ML) services such as Amazon SageMaker HyperPod. The dataset was stored in an Amazon Simple Storage Service (Amazon S3) bucket, which served as a centralized data repository.

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ODSC - Open Data Science

MAY 24, 2023

Building a Business with a Real-Time Analytics Stack, Streaming ML Without a Data Lake, and Google’s PaLM 2 Building a Pizza Delivery Service with a Real-Time Analytics Stack The best businesses react quickly and with informed decisions. Here’s a use case of how you can use a real-time analytics stack to build a pizza delivery service.

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Data Lakes ML ML Analytics 52

AWS Machine Learning Blog

FEBRUARY 13, 2024

The BigBasket team was running open source, in-house ML algorithms for computer vision object recognition to power AI-enabled checkout at their Fresho (physical) stores. Their objective was to fine-tune an existing computer vision machine learning (ML) model for SKU detection. Log model training metrics.

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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. Outside of work, he enjoys playing lawn tennis and reading books.

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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. How do I develop my body of work?

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AWS Machine Learning Blog

MARCH 13, 2024

JumpStart is the machine learning (ML) hub of SageMaker that provides access to foundation models in addition to built-in algorithms and end-to-end solution templates to help you quickly get started with ML. Read widely: Reading books, articles, and blogs from different genres and subjects exposes you to new words and phrases.

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Machine Learning Machine Learning Algorithm Python 137

O'Reilly Media

SEPTEMBER 19, 2023

A basic, production-ready cluster priced out to the low-six-figures. A company then needed to train up their ops team to manage the cluster, and their analysts to express their ideas in MapReduce. Plus there was all of the infrastructure to push data into the cluster in the first place. Goodbye, Hadoop. And it was good.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. These embeddings are inputs to a customer-facing tier-1 Amazon service.

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Pickl AI

JUNE 7, 2023

Kaggle Bike Sharing Bike-sharing systems is one of the best Data Science project on Github that allows you to book and rent motorbikes/bicycles and return them. The primary goal of the Kaggle competition is creating an ML Model that can predict the total number of bikes rented.

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AWS Machine Learning Blog

APRIL 3, 2023

JumpStart is the machine learning (ML) hub of Amazon SageMaker that offers a one-click access to over 350 built-in algorithms; pre-trained models from TensorFlow, PyTorch, Hugging Face, and MXNet; and pre-built solution templates. This page lists available end-to-end ML solutions, pre-trained models, and example notebooks.

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AWS Machine Learning Blog

SEPTEMBER 16, 2024

The clustered regularly interspaced short palindromic repeat (CRISPR) technology holds the promise to revolutionize gene editing technologies, which is transformative to the way we understand and treat diseases. He got interested in this project after reading the book The Code Breaker.

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Natural Language Processing AWS Deep Learning Deep Learning 126

Mlearning.ai

JANUARY 27, 2023

Words with similar semantic properties, such as “dog” and “puppy,” would be represented in the vector space by vectors that are close to one another, but words with different properties, such as “dog” and “book,” would be represented by vectors that are farther apart.

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AWS Machine Learning Blog

MAY 30, 2024

OpenSearch Service currently has tens of thousands of active customers with hundreds of thousands of clusters under management, processing hundreds of trillions of requests per month. Principal ML Prototyping Solutions Architect at AWS Tamil Jayakumar is a Sr. Principal Enterprise Architect at CBRE Chakra Nagarajan is a Sr.

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Mlearning.ai

JANUARY 17, 2024

Traditional AI can recognize, classify, and cluster, but not generate the data it is trained on. Complex ML problems can only be solved in neural networks with many layers. It just generates (in ML lingo „predicts“) the next token. Stacks of books and scrolls next to him and behind him. Let’s play the comparison game.

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Mlearning.ai

JUNE 8, 2023

machine learning models that learn from almost no training data) Fraud detection/outlier detection Typo detection and all manners of “fuzzy matching” Detecting when ML models go stale (drift) Learning embeddings for your machine learning model An embedding is a mapping from discrete objects, such as words, to vectors of real numbers.

Machine Learning 52

Machine Learning Machine Learning Clustering Database 52

Pickl AI

NOVEMBER 4, 2024

From structured online courses to insightful books and tutorials and engaging YouTube channels and podcasts, a wealth of content guides you on your journey. Books and Tutorials Books and tutorials are valuable resources for in-depth, self-paced learning. It offers simple and efficient tools for data mining and Data Analysis.

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Data Science Python Machine Learning Machine Learning 52

Pickl AI

OCTOBER 20, 2024

We will focus on Python programming, Machine Learning (ML), Deep Learning, and hands-on projects and stay updated with the latest trends. Books : “Automate the Boring Stuff with Python” is excellent for those who prefer self-paced learning. Deep Learning is a subset of ML. Recommended ML Courses for Beginners Pickl.AI

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Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 52

Mlearning.ai

DECEMBER 21, 2023

ML models are mathematical models and therefore require numerical data. Nature of Content —  Consider whether you are working with lengthy documents, such as articles or books, or shorter content like tweets or instant messages. Splitting: This step involves splitting documents into smaller manageable chunks.

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Mlearning.ai

JUNE 4, 2023

Citing the original description: This is the classification based E-commerce text dataset for 4 categories — “Electronics”, “Household”, “Books” and “Clothing & Accessories”, which almost cover 80% of any E-commerce website. […] The dataset has been scraped from Indian e-commerce platform. Thus, let’s download it and explore it!

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Algorithm ML ML Deep Learning 52

Mlearning.ai

JULY 25, 2023

Case Study Book in Progress! Below is a link to the book outline, Data Science Observations in a Chaotic World , feel free to let me know what you think! From a modeling and coding perspective, preparing case studies may seem time consuming and boring, but it is important to know how to convey results in a clear and concise manner.

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Data Science Clustering Analytics Analytics 52

Explosion

JUNE 18, 2021

How implement models ML fundamentals training and evaluation improve accuracy use library APIs Python and DevOps What when to use ML decide what models and components to train understand what application will use outputs for find best trade-offs select resources and libraries The “how” is everything that helps you execute the plan.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Clustering 52

Pickl AI

MAY 31, 2023

These are a few online tutorials, instructions, and books available that can help you with comprehending these basic concepts. After that, move towards unsupervised learning methods like clustering and dimensionality reduction. It includes regression, classification, clustering, decision trees, and more.

Data Science 52

Data Science Python Data Scientist Machine Learning 52

Snorkel AI

AUGUST 9, 2023

ML practitioners are increasingly coming to appreciate that while foundation models like LLMs provide a fantastic foundation for AI applications, best results are achieved with additional data-centric development. Built-in tools for EDA (filtering, sorting, clustering, tagging, etc.) Book a demo today.

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AI AI EDA Data Scientist 52

Heartbeat

NOVEMBER 6, 2023

Overview of Airflow Architecture (Image from Data Pipelines from Apache Airflow Book) Given that you now understand the core concept behind Airflow and the components that make up Apache Airflow, the next step is a practical hands-on. The celery flower is used for managing the celery cluster, which is not needed for a local executor.

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AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner.

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AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). This technique is achieved through the use of ML algorithms that enable the understanding of the meaning and context of data (semantic relationships) and the learning of complex relationships and patterns within the data (syntactic relationships).

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AWS Machine Learning Blog

AUGUST 15, 2024

With over 50 connectors, an intuitive Chat for data prep interface, and petabyte support, SageMaker Canvas provides a scalable, low-code/no-code (LCNC) ML solution for handling real-world, enterprise use cases. Afterward, you need to manage complex clusters to process and train your ML models over these large-scale datasets.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

AWS innovates to offer the most advanced infrastructure for ML. For ML specifically, we started with AWS Inferentia, our purpose-built inference chip. Several years ago, we realized that to keep pushing the envelope on price performance we would need to innovate all the way down to the silicon, and we began investing in our own chips.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

This capability allows for the seamless addition of SageMaker HyperPod managed compute to EKS clusters, using automated node and job resiliency features for foundation model (FM) development. FMs are typically trained on large-scale compute clusters with hundreds or thousands of accelerators.

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Clustering AWS ML ML 92

Flipboard

DECEMBER 6, 2023

The model then uses a clustering algorithm to group the sentences into clusters. The sentences that are closest to the center of each cluster are selected to form the summary. Implementation includes the following steps: The first step is to break down the large document, such as a book, into smaller sections, or chunks.

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Flipboard

DECEMBER 3, 2024

As a global leader in agriculture, Syngenta has led the charge in using data science and machine learning (ML) to elevate customer experiences with an unwavering commitment to innovation. He’s the author of the bestselling book “Interpretable Machine Learning with Python,” and the upcoming book “DIY AI.”

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning.

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Database AWS ETL SQL 158

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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