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. Making data engineering more systematic through principles and tools will be key to making AI algorithms work.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

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

The MLOps Blog

DECEMBER 28, 2022

Data scientists frame the business problem and the objective into a statistical solution and start with the very first step of data exploration. 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

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. Model Training: Running computations to learn from the data.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

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 119

AWS AI AI Database 119

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. ArangoDB is designed to be scalable, reliable, and easy to use.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

Alation

AUGUST 3, 2021

It brings together business users, data scientists , data analysts, IT, and application developers to fulfill the business need for insights. DataOps then works to continuously improve and adjust data models, visualizations, reports, and dashboards to achieve business goals. Using DataOps to Empower Users.

DataOps 59

DataOps Data Pipeline Data Analyst Analytics 59

DagsHub

DECEMBER 11, 2023

DagsHub DagsHub is a centralized Github-based platform that allows Machine Learning and Data Science teams to build, manage and collaborate on their projects. In addition to versioning code, teams can also version data, models, experiments and more. It does not support the ‘dvc repro’ command to reproduce its data pipeline.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Iguazio

JANUARY 22, 2024

Production App - Build resilient and modular production pipelines with automation, scale, testing, observability, versioning, security, risk handling, etc. Monitoring - Monitor all resources, data, model and application metrics to ensure performance. This helps cleanse the data.

Data Pipeline 52

Data Pipeline ML ML Data Warehouse 52

DataSeries

AUGUST 15, 2024

Data Engineering Career: Unleashing The True Potential of Data Problem-Solving Skills Data Engineers are required to possess strong analytical and problem-solving skills to navigate complex data challenges. Understanding these fundamentals is essential for effective problem-solving in data engineering.

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

ODSC - Open Data Science

FEBRUARY 8, 2024

data sources or simpler data models) of the data products we want to build? Answering these questions allows data scientists to develop useful data products that start out simple and can be improved and made more complex over time until the long-term vision is achieved. What are the dependencies (e.g.

Data Science 52

Data Science AI AI Data Scientist 52

Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. LLMOps is MLOps for LLMs.

ML 52

ML ML Data Scientist AI 52

ODSC - Open Data Science

APRIL 26, 2023

Though just about every industry imaginable utilizes the skills of a data-focused professional, each has its own challenges, needs, and desired outcomes. This is why you’ll often find that there are jobs in AI specific to an industry, or desired outcome when it comes to data. So, what are you waiting for?

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DagsHub

DECEMBER 5, 2023

It helps data scientists keep track of their experiments, reproduce their results, and collaborate with others effectively. Experiment tracking tools enable us to log experiment metadata, such as hyperparameters, dataset/code versions, and model performance metrics. This is where ML experiment tracking comes into play!

Machine Learning 52

Machine Learning Machine Learning ML ML 52

DagsHub

APRIL 21, 2024

This collaboration of ML and operations teams is what you call MLOps and focuses on streamlining the process of deploying the ML models to production, along with maintaining and monitoring them. Model Training Frameworks This stage involves the process of creating and optimizing the predictive models with labeled and unlabeled data.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

Pickl AI

JULY 8, 2024

Features and Capabilities of Time Series Databases TSDBs offer a rich set of functionalities that empower developers and data scientists to effectively manage and analyse time series data. Here are some key features: High-performance Write and Read Operations TSDBs are optimised for rapid data ingestion and retrieval.

Database 52

Database Data Pipeline Machine Learning Machine Learning 52

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. billion is lost by Fortune 500 companies because of broken data pipelines and communications.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

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. billion is lost by Fortune 500 companies because of broken data pipelines and communications.

Machine Learning 52

Machine Learning Machine Learning ML ML 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. Therefore, you’ll be empowered to truncate and reprocess data if bugs are detected and provide an excellent raw data source for data scientists.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

JULY 18, 2023

Enter dbt dbt provides SQL-centric transformations for your data modeling and transformations, which is efficient for scrubbing and transforming your data while being an easy skill set to hire for and develop within your teams. It should also enable easy sharing of insights across the organization.

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 Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

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. They include: 1 Data (or input) pipeline. 2 Model (or training) pipeline.

ML 98

ML ML Machine Learning Machine Learning 98

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 105

Data Scientist EDA AI Exploratory Data Analysis 105

Iguazio

MARCH 20, 2025

This includes responsible AI, Gartners concept of AI TRiSM (Trust, Risk and Security in AI Models) and Sovereign AI. AI engineering - AI is being democratized for developers and engineers, expanding beyond the limited pool of data scientists. AI Agents and multi-agent systems.

AI 59

AI AI Data Pipeline Data Scientist 59