Improve Model Performance with Dataset Analytics.
Quickly understand the characteristics of your dataset and annotations. Find unbalanced classes to determine where datasets need to be augmented for improved model performance.
Alleviate trial-and-error processes during model design by understanding data more thoroughly with exploratory data analysis (EDA).
Eliminate data distribution biases, increase the diversity of data, and construct training datasets with sufficient complexity for a variety of applications.
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The industry insights that fuel our desire to create the best-in-class machine learning annotation platform.
Is There 20/20 Vision in Computer Vision?
In this post, we discuss how man and machine can work together in the image annotation process to make computer vision training more robust.
Using Computer Vision to Tackle COVID before It Spreads
In this post, Innotescus spotlights several computer vision/ML projects that support the fight against COVID. including a ML model that interprets chest x-rays.
It’s the Journey – An Iterative Process for Data Cleaning
In this blog, we discuss how you can improve the data cleaning process through iteration and prioritization. You can also download a FREE data cleaning rulebook template.
How To Write An Effective Annotation Specification Document for Machine Learning
Let’s face it, most data scientists dread annotation. In this blog, we recommend key requirements for an annotation specification plan.
Will Your ML Project Ever Make it to Production?
What are the odds that your ML project will reach production? 13% or a little better than one in ten. In this blog, we provide you with considerations to tilt the odds in your favor.
Reducing Model Bias – ML’s Great Challenge
Arguably, the most important challenge facing ML today involves data science and social equity. In this blog, we discuss how bias can be introduced into ML models, most often inadvertently.
The Final Stages: Choosing, Training, and Deploying a Machine Learning Model
The last steps left – choose, train, and deploy a model. In this post, we discuss considerations when ML scientists finalize their solutions.
Blending Machine Learning and Domain Knowledge with Feature Engineering
Data augmentation is effective when addressing dataset shortcomings. In this blog, we’ll discuss augmentation basics, and techniques for augmenting image data.
Stretching your Dataset with Data Augmentation
Data augmentation is an effective way to address the shortcomings of a dataset. In this blog, we’ll discuss the basics of augmentation, and the common techniques for augmenting image data.