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Recent Posts
A Metrics-First Approach to LLM Evaluation
Learn about different types of LLM evaluation metrics needed for generative applications
5 Techniques for Detecting LLM Hallucinations
A survey of hallucination detection techniques
Deep Dive Into LLM Hallucinations Across Generative Tasks
The creation of human-like text with Natural Language Generation (NLG) has improved recently because of advancements in Transformer-based language models. This has made the text produced by NLG helpful for creating summaries, generating dialogue, or transforming data into text. However, there is a problem: these deep learning systems sometimes make up or "hallucinate" text that was not intended, which can lead to worse performance and disappoint users in real-world situations.
Pinecone + Galileo = get the right context for your prompts
Galileo LLM Studio enables Pineonce users to identify and visualize the right context to add powered by evaluation metrics such as the hallucination score, so you can power your LLM apps with the right context while engineering your prompts, or for your LLMs in production
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
“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.
🔭 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.
🔭 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.
🔭 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.
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.
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