Data Analysis, Data Drift and Data Scientist - Artificial Intelligence Zone

Data Scientists in the Age of AI Agents and AutoML

Towards AI

JANUARY 22, 2025

Uncomfortable reality: In the era of large language models (LLMs) and AutoML, traditional skills like Python scripting, SQL, and building predictive models are no longer enough for data scientist to remain competitive in the market. Coding skills remain important, but the real value of data scientists today is shifting.

Data Scientist

Data Scientist Data Drift Data Science Data Analysis

Monitoring Machine Learning Models in Production

Heartbeat

JUNE 12, 2023

The primary goal of model monitoring is to ensure that the model remains effective and reliable in making predictions or decisions, even as the data or environment in which it operates evolves. Data drift refers to a change in the input data distribution that the model receives.

Machine Learning

Machine Learning Data Drift Explainability Data Quality

How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

MARCH 1, 2023

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

Automation

Automation ETL Data Drift ML

Webinars

How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

MORE WEBINARS

Accelerate AI-Driven Decisions with DataRobot Dedicated Managed AI Cloud and Google Cloud

DataRobot Blog

JANUARY 12, 2023

By outsourcing the day-to-day management of the data science platform to the team who created the product, AI builders can see results quicker and meet market demands faster, and IT leaders can maintain rigorous security and data isolation requirements.

Data Drift

Data Drift Data Science AI AI

5 Takeaways from the 2022 Gartner® Data & Analytics Summit, Orlando, Florida

DataRobot Blog

SEPTEMBER 6, 2022

With AI projects in pockets across the business, data scientists and business leaders must align to inject artificial intelligence into an organization. At the 2022 Gartner Data and Analytics Summit, data leaders learned the latest insights and trends. Data Analysis Must Include Business Value.

Data Scientist

Data Scientist Data Science Machine Learning Data Drift

Machine Learning Operations (MLOPs) with Azure Machine Learning

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning

Machine Learning Data Drift Data Science Data Scientist

Monitoring Your Time Series Model in Comet

Heartbeat

MARCH 21, 2023

There are several techniques used for model monitoring with time series data, including: Data Drift Detection: This involves monitoring the distribution of the input data over time to detect any changes that may impact the model’s performance. You can learn more about Comet here.

Machine Learning

Machine Learning Data Drift Data Scientist Data Analysis

Better Forecasting with AI-Powered Time Series Modeling

DataRobot Blog

DECEMBER 15, 2022

If your dataset is not in time order (time consistency is required for accurate Time Series projects), DataRobot can fix those gaps using the DataRobot Data Prep tool , a no-code tool that will get your data ready for Time Series forecasting. Prepare your data for Time Series Forecasting. Perform exploratory data analysis.

Machine Learning

Machine Learning AI AI Data Drift

Managing Dataset Versions in Long-Term ML Projects

The MLOps Blog

MARCH 20, 2023

However, dataset version management can be a pain for maturing ML teams, mainly due to the following: 1 Managing large data volumes without utilizing data management platforms. 2 Ensuring and maintaining high-quality data. 3 Incorporating additional data sources. 4 The time-consuming process of labeling new data points.

ML

ML Data Drift Machine Learning Algorithm

Building ML Platform in Retail and eCommerce

The MLOps Blog

MAY 31, 2023

So, a better database architecture would be to maintain multiple tables where one of the tables maintains the past 3 months history with session-level details, whereas other tables may contain weekly aggregated click, ATC, and order data. Keeping track of which data was used to run an experiment sometimes becomes painful for a Data Scientist.

ML

ML Algorithm Data Drift Machine Learning

Data Science Tutorial using Python

Viso.ai

MAY 21, 2024

Common data visualization techniques display the exploratory data by bar charts, pie charts, histograms, line graphs, etc. By visualization, you can identify anomalies in your data and have a better representation of your data content. Thus, it is capable of producing quality plots and charts.

Data Science

Data Science Python Neural Network Machine Learning

AI in Time Series Forecasting

Pickl AI

DECEMBER 16, 2024

Making Data Stationary: Many forecasting models assume stationarity. If the data is non-stationary, apply transformations like differencing or logarithmic scaling to stabilize its statistical properties. Exploratory Data Analysis (EDA): Conduct EDA to identify trends, seasonal patterns, and correlations within the dataset.

Machine Learning

Machine Learning AI AI Neural Network

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning

Machine Learning Data Scientist Data Science ML

Capital One’s data-centric solutions to banking business challenges

Snorkel AI

MAY 12, 2023

My name is Erin Babinski and I’m a data scientist at Capital One, and I’m speaking today with my colleagues Bayan and Kishore. We’re here to talk to you all about data-centric AI. Publishing standards for data and governance of that data is either missing or very widely far from an ideal.

Machine Learning

Machine Learning Data Scientist Data Science ML

How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

AWS Machine Learning Blog

SEPTEMBER 21, 2023

This workflow will be foundational to our unstructured data-based machine learning applications as it will enable us to minimize human labeling effort, deliver strong model performance quickly, and adapt to data drift.” – Jon Nelson, Senior Manager of Data Science and Machine Learning at United Airlines.

Auto-complete

Auto-complete Machine Learning Computer Vision ML

How to Build an End-To-End ML Pipeline

The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. It checks the data for quality issues and detects outliers and anomalies. Pipelines can be scheduled to carry out CI, CD, or CT.

ML

ML Machine Learning Metadata Data Science

Creating An Information Edge With Conversational Access To Data

Topbots

JUNE 29, 2023

Figure 1: Representation of the Text2SQL flow As our world is getting more global and dynamic, businesses are more and more dependent on data for making informed, objective and timely decisions. However, as of now, unleashing the full potential of organisational data is often a privilege of a handful of data scientists and analysts.

Algorithm

Algorithm Auto-complete Data Scientist Auto-classification

Artificial Intelligence Zone

Data Scientists in the Age of AI Agents and AutoML

Monitoring Machine Learning Models in Production

Webinars

Trending Sources

How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

Webinars

Accelerate AI-Driven Decisions with DataRobot Dedicated Managed AI Cloud and Google Cloud

5 Takeaways from the 2022 Gartner® Data & Analytics Summit, Orlando, Florida

Machine Learning Operations (MLOPs) with Azure Machine Learning

Monitoring Your Time Series Model in Comet

Better Forecasting with AI-Powered Time Series Modeling

Managing Dataset Versions in Long-Term ML Projects

Building ML Platform in Retail and eCommerce

Data Science Tutorial using Python

AI in Time Series Forecasting

Capital One’s data-centric solutions to banking business challenges

Capital One’s data-centric solutions to banking business challenges

How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

How to Build an End-To-End ML Pipeline

Creating An Information Edge With Conversational Access To Data

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