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.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

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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AI AI Artificial Intelligence Artificial Intelligence 172

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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ML ML Data Scientist AWS 139

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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AI AI Data Engineering Data Engineer 54

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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SQL Database AWS Machine Learning 168

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

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

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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K-nearest Neighbors AWS Machine Learning Machine Learning 115

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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Database Data Models Data Modeling Big Data 52

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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Database Natural Language Processing Predictive Analytics AI 195

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 Database Data Warehouse Analytics 87

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 Machine Learning ML 127

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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Algorithm AI AI Database 101

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.

Data Science 130

Data Science Machine Learning Machine Learning Deep Learning 130

AWS Machine Learning Blog

DECEMBER 23, 2024

An example multi-hop query in finance is Compare the oldest booked Amazon revenue to the most recent. With AWS, you have access to scalable infrastructure and advanced services like Amazon Neptune , a fully managed graph database service. Implementing GraphRAG from scratch usually requires a process similar to the following diagram.

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AWS AI AI Natural Language Processing 119

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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AWS Data Preparation Azure Data Scientist 123

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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AI AI Database ML 105

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?

Database 94

Database AI AI Machine Learning 94

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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AWS ML ML Machine Learning 102

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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AWS AI AI ML 117

O'Reilly Media

OCTOBER 30, 2023

The recent discovery (documented by an exposé in The Atlantic ) that OpenAI, Meta, and others used databases of pirated books, for example, highlights the need for transparency in training data. Given the importance of intellectual property to the modern economy, copyright ought to be an important part of this executive order.

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AI AI Database Algorithm 120

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 Machine Learning Database 122

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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ML ML AWS Data Lakes 117

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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AI AI Database ML 141

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 Database ML ML 106

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 AI AI ML 115

AWS Machine Learning Blog

MARCH 14, 2024

Beyond hardware, data cleaning and processing, model architecture design, hyperparameter tuning, and training pipeline development demand specialized machine learning (ML) skills. Launched in 2017, Amazon SageMaker is a fully managed service that makes it straightforward to build, train, and deploy ML models.

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AWS AI AI ML 120

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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AWS ML ML Database 69

The MLOps Blog

AUGUST 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. Stefan: Yeah. Stefan: Yeah.

ML 52

ML ML Data Scientist Machine Learning 52

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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AI AI Machine Learning Machine Learning 90

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 ML ML Clustering 95

Mlearning.ai

JULY 29, 2023

INTRODUCTION Recently I had the pleasure to explore different options to automate a daily ML inference job, which reads raw data from a database table and write the inference results to another table. This approach is heavily inspired by the book Designing Machine Learning Systems by Chip Huyen , a go-to resource for any ML Engineer.

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Azure Machine Learning Machine Learning ML 52

AWS Machine Learning Blog

NOVEMBER 24, 2023

The platform enables you to create managed agents for complex business tasks without the need for coding, such as booking travel, processing insurance claims, creating ad campaigns, and managing inventory. Product database – The central repository stores vendor products, images, labels, and generated descriptions.

AWS 113

AWS Database AI AI 113

Flipboard

JUNE 13, 2023

The ScholarAI plugin searches academic databases for citations, and returns links. However, the citations I got were valid: real books and articles, with the authors listed correctly. The judge was not pleased. That raises another question: ChatGPT has always been prone to making up citations–but now there’s a plugin for that!

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Database Python AI AI 160

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 AI AI Database 72

AWS Machine Learning Blog

JULY 1, 2024

In this post, we demonstrate how you can build chatbots with QnAIntent that connects to a knowledge base in Amazon Bedrock (powered by Amazon OpenSearch Serverless as a vector database ) and build rich, self-service, conversational experiences for your customers. She helps key customer accounts on their data, generative AI and AI/ML journeys.

AWS 118

AWS AI AI Natural Language Processing 118

AWS Machine Learning Blog

FEBRUARY 19, 2024

Internally, Amazon Bedrock uses embeddings stored in a vector database to augment user query context at runtime and enable a managed RAG architecture solution. The document embeddings are split into chunks and stored as indexes in a vector database. We use the Amazon letters to shareholders dataset to develop this solution.

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AWS Database Machine Learning Machine Learning 109