Posts tagged Data Quality
Free Machine Learning Workshop - Build Higher Quality Models with Higher Quality Data
Learn how to instantly resolve data errors using Galileo. Galileo Machine Learning Data Quality Intelligence enables ML Practitioners to resolve data errors.
Webinar - Unpacking The State of Data Quality in Machine Learning
Unpack the findings of our State of Machine Learning Data Quality Report. We have surveyed 500 experienced data professionals to learn what types of data they work with, what data errors they encounter, and what technologies they use.
ImageNet Data Errors Discovered Instantly using Galileo
Using Galileo you can surface labeling errors and model errors on the most popular dataset in computer vision. Explore the various error type and simple visualization tools to find troublesome data points.
5 Principles You Need To Know About Continuous ML Data Intelligence
Build better models, faster, with better data. We will dive into what is ML data intelligence, and it's 5 principles you can use today.
LLMOps: Insights from Industry Leaders on the Evolving GenAI Stack
Explore insights from industry leaders on the evolving GenAI stack at Galileo's GenAI Productionize conference. Learn how enterprises are adopting LLMOps, optimizing costs, fine-tuning models, and improving data quality to harness the power of generative AI. Discover key trends and strategies for integrating GenAI into your organization.
Introducing Data Error Potential (DEP) - New Powerful Metric for Quantifying Data Difficulty for a Model
The Data Error Potential (DEP) is a 0 to 1 score that provides a tool to very quickly sort and bubble up data that is most difficult and worthwhile to explore when digging into your model’s errors. Since DEP is task agnostic, it provides a strong metric to guide exploration of model failure modes.
LabelStudio + Galileo: Fix your ML data quality 10x faster
Galileo integrates deeply with Label Studio to help data scientists debug and fix their training data 10x faster.
Building High-Quality Models Using High Quality Data at Scale
One neglected aspect of building high-quality models is that it depends on one crucial entity: high quality data. Good quality data in ML is the most significant impediment to seamless ML adoption across the enterprise.
🔠What is NER And Why It’s Hard to Get Right
In this post, we discuss the Named Entity Recognition (NER) task, why it is an important component of various NLP pipelines, and why it is particularly challenging to improve NER models.
NLP: Huggingface Transformers NER, understanding BERT with Galileo
HuggingFace has proved to be one of the leading hubs for NLP-based models and datasets powering so many applications today. But in the case of NER, as with any other NLP task, the quality of your data can impact how well (or poorly) your models perform during training and post-production.
Mastering Data: Generate Synthetic Data for RAG in Just $10
Learn to create and filter synthetic data with ChainPoll for building evaluation and training dataset
Enough Strategy, Let's Build: How to Productionize GenAI
At GenAI Productionize 2024, expert practitioners shared their own experiences and mistakes to offer tools and techniques for deploying GenAI at enterprise scale. Read key takeaways from the session on how to productionize generative AI.
đź” Improving Your ML Datasets With Galileo, Part 1
We used Galileo on the popular Newsgroups dataset to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available publicly for use.
Being 'Data-Centric' is the Future of Machine Learning
Machine Learning is advancing quickly but what is changing? Learn what the state of ML is today, what being data-centric means, and what the future of ML is turning into.
Fixing Your ML Data Blindspots
When working on machine learning (ML) projects, the challenges are usually centered around datasets in both the pre-training and post-training phases, with their own respective issues that need to be addressed. Learn about different ML data blind spots and how to address these issues.
đź” Improving Your ML Datasets, Part 2: NER
We used Galileo on the popular MIT dataset with a NER task, to find data errors fast, fix them, get meaningful gains within minutes, and made the fixed dataset available for use.
4 Types of ML Data Errors You Can Fix Right Now ⚡️
In this article, Galileo founding engineer Nikita Demir discusses common data errors that NLP teams run into, and how Galileo helps fix these errors in minutes, with a few lines of code.
How to Scale your ML Team’s Impact
Putting a high-quality Machine Learning (ML) model into production can take weeks, months, or even quarters. Learn how ML teams are now working to solve these bottlenecks.
How We Scaled Data Quality at Galileo
At Galileo, we had a simple goal: enable machine learning engineers to easily and quickly surface critical issues in their datasets. This data-centric approach to model development made sense to us but came with a unique challenge that other model-centric ML tools did not face: data is big. Really big. While other tracking tools were logging *meta-*data information such as hyper-parameters, weights, and biases, we were logging embedding and probability vectors, sometimes in the near thousand-dimension space, and sometimes many per input.
Introducing ML Data Intelligence For Unstructured Data
“The data I work with is always clean, error free, with no hidden biases” said no one that has ever worked on training and productionizing ML models. Learn what ML data Intelligence is and how Galileo can help with your unstructured data.
“ML Data” : The past, present and future
Data is critical for ML. But it wasn't always this way. Learn about how focusing on ML Data quality came to become the central figure for the best ML teams today.