Introducing MissingLink: Streamlining the Entire Deep Learning Lifecycle
Today we’re excited to announce the public launch of MissingLink.ai to help data scientists and engineers streamline and automate the entire deep learning cycle. With this launch, we hope to eliminate a lot of the grunt work associated with machine learning and to accelerate the time it takes to train and deliver effective models.
Work on MissingLink began in 2016, when my colleagues Shay Erlichmen, Rahav Lussato, and I set out to solve a problem we experienced as software engineers. While working on deep learning projects at our previous company, we realized we were spending too much time managing the sheer volume of data we were collecting and analyzing, and too little time learning from it.
We also realized we weren’t alone. As engineers, we knew there must be a more efficient solution, so we decided to build it. Around that time, we were joined by Joe Salomon, and MissingLink was born.
Why we built MissingLink
Machine learning — and specifically, deep learning — promises to impact our lives in profound ways. It has the potential to accelerate disease detection through medical imaging; provide safer roads thanks to autonomous vehicles; and enhance the safety and security of public spaces via crowd and body cams, among other things.
MissingLink helps data engineers streamline the entire deep learning cycle: data, code, experiments, and resources. It automates repetitive, time-consuming tasks, shortening model training times, and accelerating learning cycles.
Experiment Management: Get Started in Minutes
With MissingLink, data engineers can set up experiments with just three lines of code. Its version-aware data management eliminates the need to copy files and only syncs changes to data. This reduces load time and making data exploration easier, so you can focus on the more challenging tasks of your deep learning project.
To make integration with major AI tools easy, MissingLink comes with support for popular frameworks such as Tensorflow, PyTorch, Caffe, and Keras.
Real-time Experiment Monitoring and Tracking
Our product offers real-time monitoring and tracking of experiments via visual dashboards, which enables you to make decisions faster. It also automatically tracks all of your data, experiments, and code, so you can easily reproduce any experiment at any time.
MissingLink: Your Git Solution for Data
Data is growing at a fast pace. In fact, it’s expected to grow 10x in just the next five years. With this rapid increase in data comes the need to have an easy-to-use tool to manage and learn from it.
As a version-aware data solution, MissingLink gives you control over your data and the ability to run queries against specific versions, making it easy to reproduce experiments in seconds. You’ll be able to run experiments on a training machine without the need to copy or move data. Once your data is integrated, MissingLink will manage it on your machine on premise.
Some MissingLink customers have been able to run dozens of experiments simultaneously. That means less time spent tracking and managing countless elements, versions and data points used in experiments — and more time learning from them.
What our customers are doing
To understand the potential impact this could have for machine learning, you need only look at how our customers are using MissingLink.
Aidoc, for example, develops deep learning technology that analyzes medical imaging to provide the most comprehensive solution for detecting acute abnormalities across the body. With MissingLink, they’ve been able to help radiologists prioritize life-threatening cases and expedite patient care.
Another MissingLink customer, Nanit, has developed a smart baby camera that uses deep learning and computer vision to monitor children. Their technology helps keep babies safer, gives parents peace of mind, and enables both parents and baby to sleep better at night.
In the coming weeks and months we will be updating our product with many new and powerful features, so stay tuned!
In the meantime, if you are working on a deep learning project, sign up for a free account. We can’t wait to see what you’ll build with our platform and how it will improve all of our lives.