Flipboard

MAY 30, 2025

For the preceding techniques, the foundation should provide scalable infrastructure for data storage and training, a mechanism to orchestrate tuning and training pipelines, a model registry to centrally register and govern the model, and infrastructure to host the model.

AWS 141

AWS AI AI Database 141

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. SageMaker Data Wrangler supports fine-grained data access control with Lake Formation and Amazon Athena connections.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. An integrated model factory to develop, deploy, and monitor models in one place using your preferred tools and languages.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

MAY 8, 2023

Article on Azure ML by Bethany Jepchumba and Josh Ndemenge of Microsoft In this article, I will cover how you can train a model using Notebooks in Azure Machine Learning Studio. At the end of this article, you will learn how to use Pytorch pretrained DenseNet 201 model to classify different animals into 48 distinct categories.

Azure 52

Azure ML ML Data Modeling 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL.

Data Science 84

Data Science AWS Hadoop Data Scientist 84

ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

Azure 52

Azure ML ML Data Modeling 52

DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. 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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. Dan Volk is a Data Scientist at the AWS Generative AI Innovation Center.

AWS 126

AWS ML ML Machine Learning 126

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Flipboard

OCTOBER 25, 2023

Unstructured data is information that doesn’t conform to a predefined schema or isn’t organized according to a preset data model. Text, images, audio, and videos are common examples of unstructured data. The steps of the workflow are as follows: Integrated AI services extract data from the unstructured data.

AWS 167

AWS ML ML Analytics 167

ODSC - Open Data Science

MARCH 23, 2023

Each month, ODSC has a few insightful webinars that touch on a range of issues that are important in the data science world, from use cases of machine learning models, to new techniques/frameworks, and more. This is due to how data lakes can become too large and complex. Watch on-demand here.

Data Science 52

Data Science Data Lakes Machine Learning Machine Learning 52

AWS Machine Learning Blog

MAY 31, 2024

He has been helping the customers over the last 20 years in building the enterprise data strategies, advising customers on Generative AI, cloud implementations, migrations, reference architecture creation, data modeling best practices, data lake/warehouses architectures.

AWS 134

AWS Machine Learning Machine Learning Database 134

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

Tableau

MAY 11, 2022

Data fabrics are gaining momentum as the data management design for today’s challenging data ecosystems. At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location.

Tableau 98

Tableau Data Warehouse Database Data Analyst 98

AWS Machine Learning Blog

JUNE 22, 2023

Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers. With predictive maintenance, L&W can get advanced warning of machine breakdowns and proactively dispatch a service team to inspect the issue.

AWS 115

AWS ML ML Machine Learning 115

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. Integrating seamlessly with other Google Cloud services, BigQuery is a powerful solution for organizations seeking efficient and cost-effective large-scale data analysis.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

O'Reilly Media

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. However, the concept is quite abstract. Compute.

ML 145

ML ML Data Scientist AWS 145

ODSC - Open Data Science

OCTOBER 15, 2023

Institute of Analytics The Institute of Analytics is a non-profit organization that provides data science and analytics courses, workshops, certifications, research, and development. The courses and workshops cover a wide range of topics, from basic data science concepts to advanced machine learning techniques.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

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). It can be used with both on-premise and multi-cloud environments.

AI 88

AI AI Data Warehouse ML 88

ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. Data Lakes: These store raw, unprocessed data in its original format.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

phData

SEPTEMBER 28, 2023

Attach a Common Data Model Folder (preview) When you create a Dataflow from a CDM folder, you can establish a connection to a table authored in the Common Data Model (CDM) format by another application. Dataflows provide centralized data and reduce the load on data sources.

Power BI 78

Power BI Data Preparation Machine Learning Machine Learning 78

Precisely

OCTOBER 16, 2023

Big data analytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. The cloud is especially well-suited to large-scale storage and big data analytics, due in part to its capacity to handle intensive computing requirements at scale.

Analytics 72

Analytics Analytics Big Data Analytics Big Data Analytics 72

phData

SEPTEMBER 26, 2023

Sources The sources involved could influence or determine the options available for the data ingestion tool(s). These could include other databases, data lakes, SaaS applications (e.g. Data flows from the current data platform to the destination. Learn more about how a data model is chosen!

SQL 52

SQL Database ETL Data Modeling 52

ODSC - Open Data Science

OCTOBER 9, 2024

These tools use machine learning models trained on vast amounts of code to assist developers in writing cleaner, more efficient code. Tools like Testim and Applitools leverage machine learning to improve both unit testing and UI testing. How you might ask? What should you be looking for?

Apache Kafka 40

Apache Kafka AI AI Machine Learning 40

Alation

FEBRUARY 13, 2020

Just as you need data about finances for effective financial management, you need data about data (metadata) for effective data management. You can’t manage data without metadata. Figure 1 shows a logical data model that represents typical metadata content of a data catalog.

Data Lakes 52

Data Lakes Data Governance Data Science Data Analyst 52

phData

MAY 13, 2024

Built for integration, scalability, governance, and industry-leading security, Snowflake optimizes how you can leverage your organization’s data, providing the following benefits: Built to Be a Source of Truth Snowflake is built to simplify data integration wherever it lives and whatever form it takes.

Database 52

Database Analytics Analytics Machine Learning 52

DagsHub

NOVEMBER 11, 2024

Social media conversations, comments, customer reviews, and image data are unstructured in nature and hold valuable insights, many of which are still being uncovered through advanced techniques like Natural Language Processing (NLP) and machine learning. What is Unstructured Data?

AI 52

AI AI Data Lakes Database 52

ODSC - Open Data Science

NOVEMBER 18, 2024

Reinforcement Learning with Human Feedback Luis Serrano, PhD | Author of Grokking Machine Learning and Creator of Serrano Academy In this session, you’ll explore the widely used LLM fine-tuning method of Reinforcement Learning with Human Feedback (RLHF).

Deep Learning 40

Deep Learning Deep Learning Database Data Science 40

Pickl AI

JUNE 11, 2024

Self-Service Analytics User-friendly interfaces and self-service analytics tools empower business users to explore data independently without relying on IT departments. This might involve data validation rules, data cleansing procedures, and ongoing monitoring to maintain data integrity.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

Alation

JULY 19, 2022

Across the country, data scientists have an unemployment rate of 2% and command an average salary of nearly $100,000. As they attempt to put machine learning models into production, data science teams encounter many of the same hurdles that plagued data analytics teams in years past: Finding trusted, valuable data is time-consuming.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Pickl AI

APRIL 6, 2023

Understand the fundamentals of data engineering: To become an Azure Data Engineer, you must first understand the concepts and principles of data engineering. Knowledge of data modeling, warehousing, integration, pipelines, and transformation is required. Data Warehousing concepts and knowledge should be strong.

Azure 52

Azure Data Engineering Data Engineering Data Engineer 52

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage.

Data Pipeline 59

Data Pipeline ETL Data Quality SQL 59

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

The MLOps Blog

MAY 9, 2023

They run scripts manually to preprocess their training data, rerun the deployment scripts, manually tune their models, and spend their working hours keeping previously developed models up to date. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

ML 98

ML ML Machine Learning Machine Learning 98