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.
Listen to Your Data: The What, Why, and How of Exploratory Data Analysis
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 of EDA in regards to computer vision.
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 help teams save time and produce high quality annotations.
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 process and the challenges it often presents.
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 issues that are addressed during data cleaning, and some potential solutions.
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 pro-tips on determining the right amount of data required to train a machine learning model.
The Devil is in the Data: Machine Learning Process Simplified
Machine Learning may seem daunting and sound like the science fiction portrayed in The Matrix movies, but in reality, it is merely data, algorithms, and training iterations. In this blog we will break down the nine common steps of Machine Learning.
Innotescus helps scientists and engineers break the 80/20 rule
Are you frustrated with the quality and amount of time spent managing data? In this blog you will learn about new tools that help scientists and engineers break the 80/20 rule – allowing for more algorithm development, feature engineering, and model tuning.