Flipboard

SEPTEMBER 25, 2023

We borrow proven techniques from the latest in NLP (natural language processing) academia to build evaluation tooling that any software engineer can use. Devs shouldn’t be neck-deep in evaluation pipelines just to test their software, so we solve that complexity for them. What’s your secret sauce?

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Rajesh Nedunuri is a Senior Data Engineer within the Amazon Worldwide Returns and ReCommerce Data Services team. He specializes in designing, building, and optimizing large-scale data solutions.

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Smart Data Collective

FEBRUARY 3, 2022

It uses machine learning and natural language processing technology to improve data matching. The reusability feature will help in data management and analytics, further maintaining the data pipeline. With the help of explainability, businesses can easily understand what the output would be.

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Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. First, let’s explore the key attributes of each role: The Data Scientist Data scientists have a wealth of practical expertise building AI systems for a range of applications. The Data Engineer Not everyone working on a data science project is a data scientist.

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Data Science Dojo

APRIL 16, 2024

Natural language processing (NLP) engineer Potential pay range – US$164,000 to 267,000/yr As the name suggests, these professionals specialize in building systems for processing human language, like large language models (LLMs).

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AWS Machine Learning Blog

OCTOBER 24, 2024

His area of research is all things natural language (like NLP, NLU, NLG). His research publications are on natural language processing, personalization, and reinforcement learning. We use Amazon Bedrock Agents with two knowledge bases for this assistant.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines.

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AWS Machine Learning Blog

DECEMBER 18, 2024

All data generation and processing steps were run in parallel directly on the SageMaker HyperPod cluster nodes, using a unique working environment and highlighting the clusters versatility for various tasks beyond just training models. Andrea Policarpi is a Data Scientist at BIP xTech, collaborating with Fastwebs AI CoE.

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AWS Machine Learning Blog

MARCH 14, 2024

Amazon Kendra uses natural language processing (NLP) to understand user queries and find the most relevant documents. For our final structured and unstructured data pipeline, we observe Anthropic’s Claude 2 on Amazon Bedrock generated better overall results for our final data pipeline.

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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. Solution overview SageMaker algorithms have fixed input and output data formats.

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Data Science Dojo

JULY 3, 2024

Data Engineering : Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing. Artificial Intelligence : Concepts of AI include neural networks, natural language processing (NLP), and reinforcement learning.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

In this post, Reveal experts showcase how they used Amazon Comprehend in their document processing pipeline to detect and redact individual pieces of PII. Amazon Comprehend is a fully managed and continuously trained natural language processing (NLP) service that can extract insight about the content of a document or text.

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The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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ODSC - Open Data Science

OCTOBER 15, 2023

Elementl / Dagster Labs Elementl and Dagster Labs are both companies that provide platforms for building and managing data pipelines. Elementl’s platform is designed for data engineers, while Dagster Labs’ platform is designed for data scientists. However, there are some critical differences between the two companies.

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IBM Journey to AI blog

AUGUST 12, 2024

Primary activities AIOps relies on big data-driven analytics , ML algorithms and other AI-driven techniques to continuously track and analyze ITOps data. The process includes activities such as anomaly detection, event correlation, predictive analytics, automated root cause analysis and natural language processing (NLP).

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

An optional CloudFormation stack to deploy a data pipeline to enable a conversation analytics dashboard. Choose an option for allowing unredacted logs for the Lambda function in the data pipeline. This allows you to control which IAM principals are allowed to decrypt the data and view it. For testing, choose yes.

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Precisely

AUGUST 29, 2024

Key Takeaways Data quality ensures your data is accurate, complete, reliable, and up to date – powering AI conclusions that reduce costs and increase revenue and compliance. Data observability continuously monitors data pipelines and alerts you to errors and anomalies.

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AWS Machine Learning Blog

SEPTEMBER 28, 2023

He builds machine learning pipelines and recommendation systems for product recommendations on the Detail Page. Maria Masood specializes in building data pipelines and data visualizations at AWS Commerce Platform. Outside of work, he enjoys game development and rock climbing.

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IBM Journey to AI blog

OCTOBER 16, 2023

Foundation models: The power of curated datasets Foundation models , also known as “transformers,” are modern, large-scale AI models trained on large amounts of raw, unlabeled data. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

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AWS Machine Learning Blog

MARCH 27, 2025

Generative AI supports key use cases such as content creation, summarization, code generation, creative applications, data augmentation, natural language processing, scientific research, and many others. Amazon Bedrock is well-suited for this data augmentation exercise to generate high-quality ground truth data.

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Dataconomy

AUGUST 22, 2024

We developed a custom data pipeline to handle the immense volume of visual data, resulting in significant cost savings and reduced human exposure to hazardous environments. This unprecedented project in Malaysia required creating robust models to identify defects in diverse industrial equipment under varying conditions.

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ODSC - Open Data Science

MAY 9, 2024

From state-of-the-art language models to innovative AI-driven applications, to new open-source models hoping to take away GPT’s crown, let’s take a tour of some of the most notable AI tools and top LLMs that are working to shape how 2024 concludes, and how AI will shape the future.

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phData

APRIL 5, 2024

Data pipelines can be set up in Snowflake using stages , streams, and tasks to automate the continuous process of uploading documents, extracting information, and loading them into destination tables. Large Language Models Snowflake Document AI is powered by a first-party Large Language Model (LLM).

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phData

MAY 28, 2024

Cortex offers a collection of ready-to-use models for common use cases, with capabilities broken into two categories: Cortex LLM functions provide Generative AI capabilities for natural language processing, including completion (prompting) , translation, summarization, sentiment analysis , and vector embeddings.

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AWS Machine Learning Blog

APRIL 19, 2023

It has intuitive helpers and utilities for modalities like computer vision, natural language processing, audio, time series, and tabular data. About the authors Fred Wu is a Senior Data Engineer at Sportradar, where he leads infrastructure, DevOps, and data engineering efforts for various NBA and NFL products.

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AWS Machine Learning Blog

OCTOBER 7, 2024

By using the natural language processing and generation capabilities of generative AI, the chat assistant can understand user queries, retrieve relevant information from various data sources, and provide tailored, contextual responses. Mohamed Mohamud is a Partner Solutions Architect with a focus on Data Analytics.

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IBM Journey to AI blog

JULY 17, 2023

This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models. Encoder-decoder and decoder-only large language models are available in the Prompt Lab today. To bridge the tuning gap, watsonx.ai

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phData

JULY 18, 2024

A startup food manufacturer was utilizing social media data to track trends and find niche markets for developing new products. The marketing team spent weeks analyzing spreadsheets of TikTok and Twitter data. The Results The manufacturer quickly released two new product lines to capitalize on growing food trends.

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DataRobot Blog

SEPTEMBER 13, 2022

Once an organization has identified its AI use cases , data scientists informally explore methodologies and solutions relevant to the business’s needs in the hunt for proofs of concept. These might include—but are not limited to—deep learning, image recognition and natural language processing.

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AWS Machine Learning Blog

DECEMBER 6, 2023

Overview of RAG RAG solutions are inspired by representation learning and semantic search ideas that have been gradually adopted in ranking problems (for example, recommendation and search) and natural language processing (NLP) tasks since 2010. Inside the frontend/streamlit-ui folder, run bash run-streamlit-ui.sh.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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ODSC - Open Data Science

DECEMBER 17, 2024

Cutting-Edge Topics for EveryInterest Unlike many other AI bootcamps, ODSC East is designed to cover a wide range of trending topics in data science, ensuring theres something for everyone. We also place a heavy emphasis on the biggest topics in AI like LLMs, RAG, AI agents, and other things defining todays AI landscape.

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IBM Journey to AI blog

DECEMBER 16, 2022

Data professionals such as data engineers, data scientists, data analysts and data stewards benefit from these self-service data catalog tools that allow for self-service analytics, data discovery, and metadata management.

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Data Quality Data Governance Data Wrangling Data Scientist 52

ODSC - Open Data Science

JANUARY 7, 2025

Natural Language Processing (NLP) has emerged as a dominant area, with tasks like sentiment analysis, machine translation, and chatbot development leading the way. Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus.

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AWS Machine Learning Blog

AUGUST 12, 2024

This is achieved by using the pipeline to transfer data from a Splunk index into an S3 bucket, where it will be cataloged. The approach is shown in the following diagram. In this example, we use the service to classify whether a patient is likely to be admitted to a hospital over the next 30 days based on the combined dataset.

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phData

JUNE 7, 2023

DL is particularly effective in processing large amounts of unstructured data, such as images, audio, and text. Natural Language Processing (NLP) : NLP is a branch of AI that deals with the interaction between computers and human languages.

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IBM Journey to AI blog

AUGUST 11, 2023

How to use ML to automate the refining process into a cyclical ML process. MLOps vs. AIOps AIOps , or artificial intelligence for IT operations, uses AI capabilities, such as natural language processing and ML models, to automate and streamline operational workflows. How MLOps will be used within the organization.

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