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

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

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Read more to know.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 138

Tableau Analytics Analytics Machine Learning 138

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

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 Engineering Data Engineer 52

ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance.

Machine Learning 52

Machine Learning Machine Learning AI AI 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. This ensures that the data is accurate, consistent, and reliable.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. Data modeling, data cleanup, etc. Click on the link above to learn more! What is Fivetran?

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 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).

AI 88

AI AI Data Warehouse ML 88

phData

NOVEMBER 2, 2023

Managing data pipelines efficiently is paramount for any organization. The Snowflake Data Cloud has introduced a groundbreaking feature that promises to simplify and supercharge this process: Snowflake Dynamic Tables. If you’re looking to leverage the full power of the Snowflake Data Cloud, let phData be your guide.

Data Pipeline 52

Data Pipeline SQL Data Warehouse Data Engineering 52

The MLOps Blog

DECEMBER 28, 2022

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. Team composition The team comprises domain experts, data engineers, data scientists, and ML engineers.

ML 78

ML ML Data Scientist Machine Learning 78

ODSC - Open Data Science

APRIL 26, 2023

For example, a data scientist would be a good fit for a team that is in charge of handling large swaths of data and creating actionable insights from them. In another industry what matters is being able to predict behaviors in the medium and short terms, and this is where a machine learning engineer might come to play.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

DataRobot Blog

SEPTEMBER 13, 2022

The future of business depends on artificial intelligence and machine learning. According to IDC , 83% of CEOs want their organizations to be more data-driven. Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do?

Data Scientist 52

Data Scientist ML ML AI 52

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

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

Iguazio

FEBRUARY 8, 2024

LLMOps (Large Language Model Operations), is a specialized domain within the broader field of machine learning operations (MLOps). LLMOps focuses specifically on the operational aspects of large language models (LLMs). Data Pipeline - Manages and processes various data sources. What is LLMOps?

ML 52

ML ML Data Scientist AI 52

Pickl AI

JULY 8, 2024

Security is Paramount Implement robust security measures to protect sensitive time series data. Integration with Data Pipelines and Analytics TSDBs often work in tandem with other data tools to create a comprehensive data ecosystem for analysis and insights generation.

Database 52

Database Data Pipeline Analytics Analytics 52

phData

OCTOBER 17, 2024

By leveraging version control, testing, and documentation features, dbt Core enables teams to ensure data quality and consistency across their pipelines while integrating seamlessly with modern data warehouses. Aside from migrations, Data Source is also great for data quality checks and can generate data pipelines.

AI 52

AI AI SQL Data Quality 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

Pickl AI

JUNE 7, 2024

IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Key Features: Graphical Framework: Allows users to design data pipelines with ease using a graphical user interface. Read Further: Azure Data Engineer Jobs.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. One might say that tabular data modeling is the original data-centric AI!

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

ODSC - Open Data Science

FEBRUARY 8, 2024

How can we build up toward our vision in terms of solvable data problems and specific data products? data sources or simpler data models) of the data products we want to build? Data science — The discipline within the organization that makes data products. What are we working towards?

Data Science 52

Data Science AI AI Data Scientist 52

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

phData

JULY 18, 2023

Advanced Analytics: Snowflake’s platform is purposefully engineered to cater to the demands of machine learning and AI-driven data science applications in a cost-effective manner. Efficient Sharing: Sigma provides several easy but secure ways to share data and analytics internally and externally.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

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

Flipboard

MAY 30, 2025

Data quality is ownership of the consuming applications or data producers. Governance The two key areas of governance are model and data: Model governance Monitor model for performance, robustness, and fairness. Model versions should be managed centrally in a model registry.

AWS 138

AWS AI AI Database 138

Towards AI

JANUARY 22, 2025

Simply put, focusing solely on data analysis, coding or modeling will no longer cuts it for most corporate jobs. These two languages cover most data science workflows. Additionally, languages like DAX can be helpful for specific use cases involving data models and dashboards.

Data Scientist 105

Data Scientist EDA AI Exploratory Data Analysis 105

Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Below are 20 essential tools every data engineer should know.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52