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.
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.
Exploratory Data Analysis for your Dataset – Explained
EDA allows ML scientists to visualize datasets from many angles so they can make informed decisions about how to improve them. In this blog, we’ll discuss the fundamentals…
Data Annotation: The Meat and Potatoes of Machine Learning Part 2
Many people don’t consider all the problems that pop up during data annotation until they start annotating. In this blog, we’ll discuss solutions to those problems that…
Data Annotation: The Meat and Potatoes of Machine Learning
Achieving quality annotations is trickier than most assume. The right tools make all the difference in the success of a project. In this blog, we’ll begin to dive into the annotating…
5 Common ML Data Cleaning Problems and How To Solve Them
Though there’s no shortage of data today, most data needs quite a bit of work before it can be leveraged into machine learning solutions. In this blog, we discuss five common…
The Brave 1st Step of Machine Learning: Dealing with Data
Finding data that’s well-suited to train Machine Learning solutions isn’t as easy as it may sound. In this blog we will discuss best practices for managing data along with…
We are a group of scientists, engineers, and entrepreneurs with a vision for better AI. With backgrounds primarily in Machine Learning and Computer Vision, the Innotescus team understands the importance of having full control over and insight into data used to train Machine Learning models.
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