Towards AI

JUNE 18, 2024

Many of you asked for an electronic version of our new book, so after working out the kinks, we are finally excited to release the electronic version of “Building LLMs for Production.” We’ve heard many feedback from you guys wanting to have both the e-book and book for different occasions. We listened.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying.

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AWS Machine Learning Blog

FEBRUARY 14, 2024

In this post, we discuss how to use the comprehensive capabilities of Amazon Bedrock to perform complex business tasks and improve the customer experience by providing personalization using the data stored in a database like Amazon Redshift. This solution contains two major components. This solution contains two major components.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Building generative AI applications presents significant challenges for organizations: they require specialized ML expertise, complex infrastructure management, and careful orchestration of multiple services. An expert in AI/ML and generative AI, Ameer helps customers unlock the potential of these cutting-edge technologies.

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Flipboard

NOVEMBER 24, 2023

The SnapLogic Intelligent Integration Platform (IIP) enables organizations to realize enterprise-wide automation by connecting their entire ecosystem of applications, databases, big data, machines and devices, APIs, and more with pre-built, intelligent connectors called Snaps.

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The MLOps Blog

JULY 4, 2024

TL;DR Vector databases play a key role in Retrieval-Augmented Generation (RAG) systems. Further, talking to data scientists and ML engineers, I noticed quite a bit of confusion around RAG systems and terminology. Vector Database: A database purpose-built for handling storage and retrieval of vectors.

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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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ODSC - Open Data Science

SEPTEMBER 21, 2023

The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition. Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. First of all, the ultimate goal of your work is to generate value.

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Dataconomy

AUGUST 3, 2023

They’ve long used AI’s little brother Machine Learning (ML) for demand and price management in the airline, hotel, and transport industries. ML and AI are already working to benefit travel companies Online travel platforms and service providers have been using ML for years, even if travelers aren’t aware of this. AI is (merely!)

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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

MARCH 29, 2023

In this post, we discuss how the IEO developed UNDP’s artificial intelligence and machine learning (ML) platform—named Artificial Intelligence for Development Analytics (AIDA)— in collaboration with AWS, UNDP’s Information and Technology Management Team (UNDP ITM), and the United Nations International Computing Centre (UNICC).

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AWS Machine Learning Blog

AUGUST 7, 2024

It works by first retrieving relevant responses from a database, then using those responses as context to feed the generative model to produce a final output. For example, retrieving responses from its database before generating a response could provide more relevant and coherent responses. Scott Fitzgerald.

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AWS Machine Learning Blog

OCTOBER 29, 2024

The CloudFormation template provisions resources such as Amazon Data Firehose delivery streams, AWS Lambda functions, Amazon S3 buckets, and AWS Glue crawlers and databases. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value.

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O'Reilly Media

JUNE 11, 2024

It’s in every book, on-demand course, and video, and will eventually be available across our entire learning platform. Answers enables active learning: interacting with content by asking questions and getting answers, rather than simply ingesting a stream from a book or video. With RAG, adding content is trivial.

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Flipboard

DECEMBER 6, 2023

During the embeddings experiment, the dataset was converted into embeddings, stored in a vector database, and then matched with the embeddings of the question to extract context. The generated query is then run against the database to fetch the relevant context. Based on the initial tests, this method showed great results.

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Mlearning.ai

FEBRUARY 13, 2023

Welcome to the wild, wacky world of databases! But with so many types of databases to choose from, how do you know which one is right for you? Databases are like different tools in a toolbox — each designed for a different need Picture a toolbox filled with different tools, each designed for a specific task. and let’s dive in!

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AWS Machine Learning Blog

APRIL 3, 2024

With Amazon Titan Multimodal Embeddings, you can generate embeddings for your content and store them in a vector database. We use Amazon OpenSearch Serverless as a vector database for storing embeddings generated by the Amazon Titan Multimodal Embeddings model. Rupinder Grewal is a Senior AI/ML Specialist Solutions Architect with AWS.

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Data Science Dojo

APRIL 18, 2024

Large language models are powerful AI-powered language tools trained on massive amounts of text data, like books, articles, and even code. Hence, embeddings take on the role of a translator, making words comprehendible for ML models. Here’s a video series providing a comprehensive exploration of embeddings and vector databases.

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Towards AI

SEPTEMBER 12, 2024

Learn about the most exciting advancements in ML, NLP, and robotics and how they are being scaled for success and growth. Featured Community post from the Discord Danieldanieldaniel1 wrote an article on building a local-first vector database using RxDB and transformers.js. Learn AI Together Community section! AI poll of the week!

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Applied Data Science

OCTOBER 25, 2021

Solid theoretical background in statistics and machine learning, experience with state-of-the-art deep learning algorithms, expert command of tools for data pre-processing, database management and visualisation, creativity and story-telling abilities, communication and team-building skills, familiarity with the industry.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Knowledge Bases is completely serverless, so you don’t need to manage any infrastructure, and when using Knowledge Bases, you’re only charged for the models, vector databases and storage you use. In the Vector database section, select Quick create a new vector store , which manages the process of setting up a vector store. Choose Next.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Current security recommendations in this area involve defining IAM controls, multiple ways to implement detective controls on databases, strengthening infrastructure security surrounding your data via network flow control, and data protection through encryption and tokenization. His interests include serverless architectures and AI/ML.

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Flipboard

MARCH 22, 2023

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Data scientist experience In this section, we cover how data scientists can connect to Snowflake as a data source in Data Wrangler and prepare data for ML. Choose Open Studio.

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IBM Journey to AI blog

APRIL 8, 2024

The new Amazon Relational Database Service (Amazon RDS) for Db2 offering allows customers to migrate their existing, self-managed Db2 databases to the cloud and accelerate strategic modernization initiatives. Are there any constraints on the number of databases that can be hosted on an instance?   Scalability 5. . Amazon RDS

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AWS Machine Learning Blog

APRIL 18, 2023

Amazon Monitron is an end-to-end condition monitoring solution that enables you to start monitoring equipment health with the aid of machine learning (ML) in minutes, so you can implement predictive maintenance and reduce unplanned downtime. For the detailed Amazon Monitron installation guide, refer to Getting started with Amazon Monitron.

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Mlearning.ai

DECEMBER 21, 2023

ML models are mathematical models and therefore require numerical data. Vectors are typically stored in Vector Databases which are best suited for searching. APIs File Directories Databases And many more The first step is to extract the information present in these source locations. What is a Vector Database?

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AWS Machine Learning Blog

JUNE 12, 2024

Photo by Zbynek Burival on Unsplash Time series forecasting is a specific machine learning (ML) discipline that enables organizations to make informed planning decisions. Amazon has a long heritage of using time series forecasting, dating back to the early days of having to meet mail-order book demand.

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AWS Machine Learning Blog

NOVEMBER 17, 2023

Agents extend FMs to run complex business tasks—from booking travel and processing insurance claims to creating ad campaigns and managing inventory—all without writing any code. You can perform a variety of tasks, including sending email notifications, writing to databases, and triggering application APIs in the Lambda functions.

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AWS Machine Learning Blog

JUNE 18, 2024

IDP is an industry term used for describing the mechanism for processing and extracting information out of structured, semi-structured, and unstructured documents using AI and machine learning (ML). The transformed data is then tailored to match the formats required by their downstream databases.

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AWS Machine Learning Blog

JULY 10, 2024

During the last 18 months, we’ve launched more than twice as many machine learning (ML) and generative AI features into general availability than the other major cloud providers combined. Customers can co-locate vector data with operational data, reducing the overhead of managing another database.

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O'Reilly Media

JUNE 18, 2024

Books, music, and movies, for example, bear copyright notices and are registered with the copyright office (which confers additional rights to damages in the event of infringement). researchers said, is high-quality information, such as published books and articles, which have been carefully written and edited by professionals.”

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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. Split data into train, validation, and test sets.

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Towards AI

FEBRUARY 22, 2024

All resources listed here are free, except some online courses and books, which are recommended for a better understanding. If you’re passionate about ML and interested in collaborative learning, connect in the thread! It is intended for anyone with a small programming and machine learning background.

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AWS Machine Learning Blog

MARCH 1, 2024

Context is retrieved from the vector database based on the user query. For example, consider the following query: What is the cost of the book " " on ? In this query for a book name and website name, a keyword search will give better results, because we want the cost of the specific book.

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AWS Machine Learning Blog

AUGUST 14, 2023

The final retrieval augmentation workflow covers the following high-level steps: The user query is used for a retriever component, which does a vector search, to retrieve the most relevant context from our database. In recent years, ML techniques have become increasingly popular to enhance search.

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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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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. Neuron plugs into popular ML frameworks like PyTorch and TensorFlow, and support for JAX is coming early next year. Customers like Adobe, Deutsche Telekom, and Leonardo.ai

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