Synthetic Data: What, Why, and How?
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.
Computer Vision Projects Help Support the Fight Against COVID
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.
A Complete Guide to 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.
Writing an Effective Annotation Specification Document for Image Annotation
Let’s face it, most data scientists dread annotation. In this blog, we recommend key requirements for an annotation specification plan.
Data Labeling Tools to Get Your Next ML Project 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 Data 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.
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.