Data Science Dojo

SEPTEMBER 10, 2024

LLM companies are businesses that specialize in developing and deploying Large Language Models (LLMs) and advanced machine learning (ML) models. It also excels at creating concise, relevant, and customizable summaries of text and documents. What are LLM Companies?

Machine Learning 369

Machine Learning Machine Learning Natural Language Processing ML 369

Flipboard

NOVEMBER 17, 2023

Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

AWS 94

AWS Data Governance Data Silos SQL 94

AWS Machine Learning Blog

SEPTEMBER 27, 2024

Processing these images and scanned documents is not a cost- or time-efficient task for humans, and requires highly performant infrastructure that can reduce the time to value. Solution overview Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning (ML) models.

AWS 126

AWS Data Lakes ML ML 126

AWS Machine Learning Blog

MARCH 1, 2023

Amazon Comprehend is a managed AI service that uses natural language processing (NLP) with ready-made intelligence to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.

Data Lakes 97

Data Lakes AWS ML ML 97

AWS Machine Learning Blog

MARCH 1, 2023

This combination of great models and continuous adaptation is what will lead to a successful machine learning (ML) strategy. Solution overview Amazon Comprehend is a fully managed service that uses natural language processing (NLP) to extract insights about the content of documents. Each flywheel has its own dedicated data lake.

Data Lakes 98

Data Lakes AWS ML ML 98

AWS Machine Learning Blog

DECEMBER 3, 2024

Although generative AI is fueling transformative innovations, enterprises may still experience sharply divided data silos when it comes to enterprise knowledge, in particular between unstructured content (such as PDFs, Word documents, and HTML pages), and structured data (real-time data and reports stored in databases or data lakes).

AWS 109

AWS Database Data Silos Data Lakes 109

AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

ML 69

ML ML Data Scientist AWS 69

AWS Machine Learning Blog

JANUARY 29, 2025

Intelligent document processing , translation and summarization, flexible and insightful responses for customer support agents, personalized marketing content, and image and code generation are a few use cases using generative AI that organizations are rolling out in production.

AWS 107

AWS AI AI Database 107

How to Learn Machine Learning

MAY 2, 2025

Azure Machine Learning is Microsoft’s enterprise-grade service that provides a comprehensive environment for data scientists and ML engineers to build, train, deploy, and manage machine learning models at scale. You can explore its capabilities through the official Azure ML Studio documentation. Awesome, right?

Azure 52

Azure Machine Learning Machine Learning Data Science 52

AWS Machine Learning Blog

NOVEMBER 22, 2023

The IDP Well-Architected Lens is intended for all AWS customers who use AWS to run intelligent document processing (IDP) solutions and are searching for guidance on how to build secure, efficient, and reliable IDP solutions on AWS. This post focuses on the Operational Excellence pillar of the IDP solution.

AWS 126

AWS ML ML Machine Learning 126

AWS Machine Learning Blog

FEBRUARY 12, 2025

Machine learning (ML) engineers must make trade-offs and prioritize the most important factors for their specific use case and business requirements. Retriever quality For better retrieval performance, the way the data is stored in the vector store has a big impact. For example, your input document might include tables within the PDF.

AWS 106

AWS Machine Learning Machine Learning AI 106

AWS Machine Learning Blog

MAY 30, 2024

To serve their customers, Vitech maintains a repository of information that includes product documentation (user guides, standard operating procedures, runbooks), which is currently scattered across multiple internal platforms (for example, Confluence sites and SharePoint folders).

AI 135

AI AI AWS Database 135

AWS Machine Learning Blog

AUGUST 2, 2024

Generative AI empowers organizations to combine their data with the power of machine learning (ML) algorithms to generate human-like content, streamline processes, and unlock innovation. The Product Stewardship department is responsible for managing a large collection of regulatory compliance documents.

AWS 133

AWS Machine Learning Machine Learning Database 133

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

FEBRUARY 21, 2025

The agent knowledge base stores Amazon Bedrock service documentation, while the cache knowledge base contains curated and verified question-answer pairs. For this example, you will ingest Amazon Bedrock documentation in the form of the User Guide PDF into the Amazon Bedrock knowledge base. This will be the primary dataset.

AWS 131

AWS Natural Language Processing Machine Learning Machine Learning 131

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

AI 132

AI AI ML ML 132

Flipboard

FEBRUARY 19, 2025

The retrieval component uses Amazon Kendra as the intelligent search service, offering natural language processing (NLP) capabilities, machine learning (ML) powered relevance ranking, and support for multiple data sources and formats.

AWS 81

AWS Cloud Computing Natural Language Processing Data Lakes 81

Data Science 101

DECEMBER 16, 2019

It now also supports PDF documents. Azure Data Factory Preserves Metadata during File Copy When performing a File copy between Amazon S3, Azure Blob, and Azure Data Lake Gen 2, the metadata will be copied as well. Data Labeling in Azure ML Studio. Not a huge update but still a nice feature.

Cloud Data 81

Cloud Data Data Science Azure Natural Language Processing 81

DagsHub

DECEMBER 11, 2023

DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. DVC can be used for versioning data and models, to track experiments and compare any data, code, parameters models and graphical plots of performance. DVC can efficiently handle large files and machine learning models.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Iguazio

JUNE 25, 2023

Evaluating ML model performance is essential for ensuring the reliability, quality, accuracy and effectiveness of your ML models. In this blog post, we dive into all aspects of ML model performance: which metrics to use to measure performance, best practices that can help and where MLOps fits in. Why Evaluate Model Performance?

ML 52

ML ML Clustering Cross Validation 52

ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

AWS Machine Learning Blog

DECEMBER 18, 2023

In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution. MongoDB Atlas MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud.

Clustering 128

Clustering AWS Database ML 128

Alation

FEBRUARY 1, 2022

Menninger states that modern data governance programs can provide a more significant ROI at a much faster pace. Sheer volume of data makes automation with Artificial Intelligence & Machine Learning (AI & ML) an imperative. But to improve and automate complex processes, AI & ML are key. Curious to learn more?

Data Lakes 52

Data Lakes Data Governance ML ML 52

AWS Machine Learning Blog

JULY 29, 2024

SageMaker endpoints can be registered with Salesforce Data Cloud to activate predictions in Salesforce. To learn more about inference with Claude 3, refer to Anthropic Claude models in the Amazon Bedrock documentation. He focuses on building AI/ML features for CRM, and is the product owner for the Bring Your Own LLM feature.

AWS 130

AWS AI AI ML 130

AWS Machine Learning Blog

FEBRUARY 28, 2024

Third, despite the larger adoption of centralized analytics solutions like data lakes and warehouses, complexity rises with different table names and other metadata that is required to create the SQL for the desired sources. About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon.

SQL 143

SQL AWS Database ML 143

AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. Data comes from disparate sources in a number of formats.

AWS 103

AWS Machine Learning Machine Learning Analytics 103

IBM Journey to AI blog

APRIL 9, 2024

This allows the Masters to scale analytics and AI wherever their data resides, through open formats and integration with existing databases and tools. “Hole distances and pin positions vary from round to round and year to year; these factors are important as we stage the data.” Lastly, watsonx.data pulls from live feeds.

AI 100

AI AI Machine Learning Machine Learning 100

Towards AI

FEBRUARY 13, 2023

How to build a chatbot that answers questions about documentation and cites its sources The tutorial was initially hosted via a live stream on our Learn AI Discord. Three 5-minute reads/videos to keep you learning 1.How These considerations include cost, complexity, expertise, time to value, and competitive advantage.

AI 98

AI AI Data Warehouse Data Lakes 98

IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. An AI governance framework ensures the ethical, responsible and transparent use of AI and machine learning (ML). Capture and document model metadata for report generation.

AI 88

AI AI Data Warehouse ML 88

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging. This article will discuss managing unstructured data for AI and ML projects. What is Unstructured Data?

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

AWS Machine Learning Blog

AUGUST 2, 2023

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra supports a variety of document formats , such as Microsoft Word, PDF, and text from various data sources. Amazon Kendra supports a variety of document formats , such as Microsoft Word, PDF, and text from various data sources.

AWS 98

AWS AI AI Machine Learning 98

phData

AUGUST 20, 2024

However, applying version control to machine learning (ML) pipelines comes with unique challenges. From data prep and model training to validation and deployment, each step is intricate and interconnected, demanding a robust system to manage it all. Some of the components of an ML pipeline and the common tools used for version control.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

Alation

AUGUST 4, 2022

And where data was available, the ability to access and interpret it proved problematic. Big data can grow too big fast. Left unchecked, data lakes became data swamps. Some data lake implementations required expensive ‘cleansing pumps’ to make them navigable again.

Big Data 95

Big Data Big Data Apache Kafka Data Lakes 95

AWS Machine Learning Blog

JUNE 25, 2024

Although these traditional machine learning (ML) approaches might perform decently in terms of accuracy, there are several significant advantages to adopting generative AI approaches. The following table compares the generative approach (generative AI) with the discriminative approach (traditional ML) across multiple aspects.

AWS 132

AWS Natural Language Processing Machine Learning Machine Learning 132

AWS Machine Learning Blog

AUGUST 26, 2024

Use case overview Using generative AI, we built Account Summaries by seamlessly integrating both structured and unstructured data from diverse sources. This includes sales collateral, customer engagements, external web data, machine learning (ML) insights, and more.

AWS 133

AWS AI AI K-nearest Neighbors 133