How to make annotation painless ? Intoduction The need in data annotation increases as the use of supervised machine learning increases. Only humans can give the groudtruth label in order to be able to train model. As an annotator, the job of annotating is very ungrateful, repetitive and sometimes complex. As an annotation project manager, … Continue reading How to make annotation painless ?
How to manage your machine learning pipeline with MLflow Here at Kili we're always exited to improve our processes, thats why we're starting to work with MLflow. In this article we share with you some insights in how it works and how you can set up you machine learning pipeline with it and we show … Continue reading How to manage your machine learning pipeline with MLflow
Improving data annotation with Superpixels To understand the need for superpixels in segmentation we must first understand what is image segmentation. Image segmentation consists in detecting specific regions in an image. In concrete terms, this means detecting the shape of objects of different categories in images. Therefore, when segmenting an image, we give a class … Continue reading Improving data annotation with Superpixels
What Workflow to Follow to Manage Model Accuracy Performance? Introduction Enhancing a model performance can be challenging at times. I’m sure many of you would agree that you’ve found yourself stuck in a similar situation. You try all the strategies and algorithms that you’ve learned, yet performance does not increase significantly. As a result, we … Continue reading What Workflow to Follow to Manage Model Accuracy Performance?
How to build an efficient data labeling plan? Introduction You need a lot of labeled data in order to train your ML models. Although it's not always necessary, you will sometimes need to label yourself the data you will use to train your model. The process of annotating data in an end-to-end ML project is … Continue reading How to build an efficient data labeling plan?
Better Training Data Better AI. Since the 80's the AI paradigm has been Better Models = Better AI. Today the limitations of this paradigm are clear: significant efforts for marginal performance improvements, restricted access to overspecialized engineers, low explainability, low control, and prohibitive project costs. @Kili Technology, we are believers. 3 years ago, Edouard d’Archimbaud … Continue reading Better Training Data, Better AI
My State-Of-The-Art Machine Learning Model does not reach its accuracy promise: What can I do? Data Quality as a first response Introduction The ultimate goal of every data scientist or company that builds ML models is to create the better model with the highest predictive accuracy in production. Usually, we start with state-of-the-art algorithms being … Continue reading My State-Of-The-Art Machine Learning Model does not reach its accuracy promise: What can I do?
What Dataset should I use to retrain my model ? Retraining a model is a necessary step in a model conception. What do I do if new data is available to me ? What do I do if my model shows lower prediction performances compared to the time I trained and tested it ?Suppose that … Continue reading What Dataset should I use to retrain my model ?
Data annotation: leveraging interactive segmentation to achieve state of the art quality and speed Machine learning models have proven to be extremely powerful for automating tasks. Automatic image recognition for example has seen an incredible leap thanks to the development of convolutional neural networks. We see that our models today have not yet reached their … Continue reading Data annotation: leveraging interactive segmentation to achieve state of the art quality and speed
Label engineering: Bounding box vs Polygon When setting up a project of object detection, you will have to choose your annotation tool. The most commonly used tools in machine learning and artificial intelligence projects are bounding boxes. However, other tools such as polygons also exist in the industry. But what are these differences and which … Continue reading Label engineering: Bounding box vs Polygon