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.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

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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AWS Machine Learning Machine Learning ML 119

AWS Machine Learning Blog

AUGUST 12, 2024

Data is presented to the personas that need access using a unified interface. For example, it can be used to answer questions such as “If patients have a propensity to have their wearables turned off and there is no clinical telemetry data available, can the likelihood that they are hospitalized still be accurately predicted?”

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ML ML AWS AI 121

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.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineer 52

IBM Journey to AI blog

AUGUST 12, 2024

Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks. Primary activities AIOps relies on big data-driven analytics , ML algorithms and other AI-driven techniques to continuously track and analyze ITOps data.

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Big Data Big Data ML ML 106

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. For example, neptune.ai

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Machine Learning Machine Learning ML ML 134

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.

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Machine Learning Machine Learning Data Pipeline AI 52

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.

AI 88

AI AI Data Warehouse ML 88

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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phData

JULY 18, 2024

Many data engineering consulting companies could also answer these questions for you, or maybe you think you have the talent on your team to do it in-house. Many data engineering consulting companies could also answer these questions for you, or maybe you think you have the talent on your team to do it in-house.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Data Engineering 52

IBM Journey to AI blog

JULY 17, 2023

offers a Prompt Lab, where users can interact with different prompts using prompt engineering on generative AI models for both zero-shot prompting and few-shot prompting. This allows users to accomplish different Natural Language Processing (NLP) functional tasks and take advantage of IBM vetted pre-trained open-source foundation models.

AI 74

AI AI Machine Learning Machine Learning 74

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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

IBM Journey to AI blog

DECEMBER 16, 2022

It seamlessly integrates with IBM’s data integration, data observability, and data virtualization products as well as with other IBM technologies that analysts and data scientists use to create business intelligence reports, conduct analyses and build AI models.

Data Quality 52

Data Quality Data Governance Data Scientist Data Wrangling 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. Similar to previous years, SQL is still the second most popular skill, as its used for many backend processes and core skills in computer science and programming.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data.

AI 59

AI AI Machine Learning Machine Learning 59

IBM Journey to AI blog

AUGUST 11, 2023

How to use ML to automate the refining process into a cyclical ML process. Data science—Data scientists can use MLOps not only for efficiency, but also for greater oversight of processes and better governance to facilitate regulatory compliance. How MLOps will be used within the organization.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

Alation

MARCH 31, 2022

And with interfaces potentially ranging from SQL editors to natural language processing, self-service for all may require a chameleon or suite of solutions to suit the individual. Regardless, all variations require the foundational data to be: Discoverable. The interface adapts to the unique needs of the persona.

Business Intelligence 52

Business Intelligence Business Intelligence Data Warehouse Data Scientist 52

Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Algorithm Development: Crafting algorithms to solve complex business problems and optimise processes. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs.

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Azure Data Scientist Data Science Machine Learning 52

DagsHub

JANUARY 14, 2025

The model achieved better performance with 45TB of deduplicated data vs 100TB raw data, thus reducing training costs significantly Vector Space Theory: This approach identifies near-duplicate texts based on the assumption that similar texts will lie close in their multidimensional vector space.

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Machine Learning Machine Learning Clustering Algorithm 52

The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

AWS Machine Learning Blog

MARCH 15, 2024

He has worked with multiple federal agencies to advance their data and AI goals. Nitin’s other focus areas include natural language processing (NLP), data pipelines, and generative AI.

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AWS ML ML Data Silos 128

Kaggle

JULY 29, 2020

David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

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ETL Data Scientist Data Science Machine Learning 71

The MLOps Blog

AUGUST 11, 2023

This section delves into the common stages in most ML pipelines, regardless of industry or business function. 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52