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. Additionally, we show how to use AWS AI/ML services for analyzing unstructured data.

AWS 166

AWS ML ML Analytics 166

Data Science Dojo

FEBRUARY 23, 2024

GPTs for Data science are the next step towards innovation in various data-related tasks. These are platforms that integrate the field of data analytics with artificial intelligence (AI) and machine learning (ML) solutions. Power BI Wizard It is a popular business intelligence tool that empowers you to explore data.

Data Science 356

Data Science Data Analysis Data Analysis Machine Learning 356

Dataconomy

MAY 2, 2023

The machine learning systems developed by Machine Learning Engineers are crucial components used across various big data jobs in the data processing pipeline. Additionally, Machine Learning Engineers are proficient in implementing AI or ML algorithms. Is ML engineering a stressful job?

ML 110

ML ML Machine Learning Machine Learning 110

Data Science Dojo

OCTOBER 31, 2024

Growth Outlook: Companies like Google DeepMind, NASA’s Jet Propulsion Lab, and IBM Research actively seek research data scientists for their teams, with salaries typically ranging from $120,000 to $180,000. With the continuous growth in AI, demand for remote data science jobs is set to rise.

Data Science 195

Data Science Data Scientist Machine Learning Machine Learning 195

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

ML 98

ML ML Data Silos Data Quality 98

The MLOps Blog

DECEMBER 28, 2022

The onset of the pandemic has triggered a rapid increase in the demand and adoption of ML technology. Building ML team Following the surge in ML use cases that have the potential to transform business, the leaders are making a significant investment in ML collaboration, building teams that can deliver the promise of machine learning.

ML 78

ML ML Data Scientist Machine Learning 78

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 Models 52

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 Models 52

Data Science Dojo

FEBRUARY 6, 2025

Here’s a complete guide to understanding all about LLMs What is Data Annotation? Data annotation is the process of labeling data to make it understandable and usable for machine learning (ML) models. Below are a few reasons that make data annotation a critical component for language models.

AI 211

AI AI ML ML 211

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. HBase is employed to offer real-time key-based access to data.

Data Science 74

Data Science AWS Hadoop Data Scientist 74

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 Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

AWS Machine Learning Blog

FEBRUARY 7, 2024

In the first post of this three-part series, we presented a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. Under Labels – optional , for Labels , choose Create new labels.

ML 134

ML ML AWS Data Profiling 134

Dataconomy

AUGUST 7, 2023

An AI database is not merely a repository of information but a dynamic and specialized system meticulously crafted to cater to the intricate demands of AI and ML applications. Herein lies the crux of the AI database’s significance: it is tailored to meet the intricate requirements that underpin the success of AI and ML endeavors.

Database 168

Database AI ML ML 168

Pickl AI

NOVEMBER 29, 2024

It enhances scalability, experimentation, and reproducibility, allowing ML teams to focus on innovation. This blog highlights the importance of organised, flexible configurations in ML workflows and introduces Hydra. Machine Learning projects evolve rapidly, frequently introducing new data , models, and hyperparameters.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

O'Reilly Media

OCTOBER 19, 2021

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. What does a modern technology stack for streamlined ML processes look like? Why: Data Makes It Different. All ML projects are software projects.

ML 145

ML ML Data Scientist AWS 145

Smart Data Collective

DECEMBER 19, 2021

For example, the Impute library package handles the imputation of missing values, MinMaxScaler scales datasets, or uses Autumunge to prepare table data for machine learning algorithms. Besides, Python allows creating data models, systematizing data sets, and developing web services for proficient data processing.

Python 141

Python Machine Learning Machine Learning Algorithm 141

The MLOps Blog

JUNE 13, 2024

Data scientists often lack focus, time, or knowledge about software engineering principles. As a result, poor code quality and reliance on manual workflows are two of the main issues in ML development processes. You need to think about and improve the data, the model, and the code, which adds layers of complexity.

ML 59

ML ML Data Scientist Azure 59

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

Machine Learning 137

Machine Learning Machine Learning ML ML 137

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

AWS 121

AWS Machine Learning Machine Learning ML 121

Dataconomy

AUGUST 25, 2023

Performance metrics in machine learning can even be used at the early stages of ML model development such as model prediction ( Image credit ) Step 3: Model prediction The model prediction is the pinnacle of a machine learning journey, where the rubber meets the road and the model’s abilities are put to the test.

Algorithm 91

Algorithm ML ML Machine Learning 91

Data Science Dojo

FEBRUARY 23, 2024

GPTs for Data science are the next step towards innovation in various data-related tasks. These are platforms that integrate the field of data analytics with artificial intelligence (AI) and machine learning (ML) solutions. Power BI Wizard It is a popular business intelligence tool that empowers you to explore data.

Data Science 195

Data Science Data Analysis Data Analysis Machine Learning 195

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

ML 117

ML ML AWS Machine Learning 117

Data Science Dojo

FEBRUARY 23, 2024

Power BI Wizard It is a popular business intelligence tool that empowers you to explore data. The data exploration allows you to create reports, use DAX formulas for data manipulation, and suggest best practices for data modeling. The learning assistance provides deeper insights and improved accuracy.

Data Science 195

Data Science Data Analysis Data Analysis Data Analyst 195

AWS Machine Learning Blog

NOVEMBER 18, 2024

The key reasons that influenced this decision were: Managed service – Amazon Bedrock is a fully serverless offering that offers a choice of industry leading FMs without provisioning infrastructure, procuring GPUs around the clock, or configuring ML frameworks.

AI 108

AI AI Database AWS 108

DataRobot

JUNE 23, 2021

As they strive to improve models, data scientists continually try new approaches to refine their predictions. To help data scientists experiment faster, DataRobot has added Composable ML to automated machine learning. Composable ML then lets you add new types of feature engineering or build entirely new models.

ML 52

ML ML Data Scientist Machine Learning 52

Iguazio

DECEMBER 18, 2023

For data science practitioners, productization is key, just like any other AI or ML technology. However, it's important to contextualize generative AI within the broader landscape of AI and ML technologies. By doing so, you can ensure quality and production-ready models. Here’s to a successful 2024!

ML 64

ML ML AI AI 64

ODSC - Open Data Science

OCTOBER 21, 2024

Fortunately, there are many tools for ML evaluation and frameworks designed to support responsible AI development and evaluation. But let’s first take a look at some of the tools for ML evaluation that are popular for responsible AI. It includes methods for addressing fairness issues by adjusting training data, models, or outputs.

ML 52

ML ML Machine Learning Machine Learning 52

Mlearning.ai

DECEMBER 21, 2023

Explore ML architectural patterns in Azure for classic and evolving needs – streaming data, model monitoring, and multiple models pipeline Continue reading on MLearning.ai »

Azure 52

Azure ML ML Data Models 52

AWS Machine Learning Blog

JULY 29, 2024

Amazon Forecast is a fully managed service that uses statistical and machine learning (ML) algorithms to deliver highly accurate time series forecasts. With SageMaker Canvas, you get faster model building , cost-effective predictions, advanced features such as a model leaderboard and algorithm selection, and enhanced transparency.

ML 113

ML ML Algorithm AWS 113

The MLOps Blog

OCTOBER 8, 2024

In the machine learning (ML) and artificial intelligence (AI) domain, managing, tracking, and visualizing model training processes is a significant challenge due to the scale and complexity of managed data, models, and resources.

ML 52

ML ML Machine Learning Machine Learning 52

Flipboard

JULY 24, 2023

Researchers from many universities build open-source projects which contribute to the development of the Data Science domain. It is also called the second brain as it can store data that is not arranged according to a present data model or schema and, therefore, cannot be stored in a traditional relational database or RDBMS.

Natural Language Processing 122

Natural Language Processing Artificial Intelligence Artificial Intelligence Data Science 122

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

NOVEMBER 22, 2023

The IDP Well-Architected Custom Lens follows the AWS Well-Architected Framework, reviewing the solution with six pillars with the granularity of a specific AI or machine learning (ML) use case, and providing the guidance to tackle common challenges. Model monitoring The performance of ML models is monitored for degradation over time.

AWS 119

AWS ML ML Machine Learning 119

DagsHub

AUGUST 29, 2024

Source: Author Introduction Machine learning (ML) models, like other software, are constantly changing and evolving. Version control systems (VCS) play a key role in this area by offering a structured method to track changes made to models and handle versions of data and code used in these ML projects.

ML 52

ML ML Python Machine Learning 52

Tableau

DECEMBER 1, 2021

April 2018), which focused on users who do understand joins and curating federated data sources. May 2020) shifted sheets to a multiple-table data model, where the sheet’s fields allow the computer to write much more efficient queries to the data sources. Visual encoding is key to explaining ML models to humans.

Tableau 145

Tableau ML ML Database 145

Women in Big Data

APRIL 4, 2025

During the Keynote talk Responsible AI @ Kumo AI , Hema Raghavan (Kumo AI Co-Founder & Head of Engineering) showcased platform solutions that make machine learning on relational data simple, performant, and scalable.

Big Data 52

Big Data Big Data AI AI 52

AWS Machine Learning Blog

APRIL 25, 2024

Hugging Face is a popular open source hub for machine learning (ML) models. He has helped launch and scale the AI/ML powered Amazon SageMaker service and has implemented several proofs of concept using Amazon AI services. client("s3") o = urlparse(s3_file, allow_fragments=False) bucket = o.netloc key = o.path.lstrip("/") s3.download_file(bucket,

AWS 122

AWS ML ML Python 122