Insights from the deep learning industry.
We all understand the importance of reproducibility of machine learning experiments. And we all understand that the basis for reproducibility is tracking every experiment, either manually in a spreadsheet or automatically through a platform such as Valohai. What you can’t track what you’ve done it’s impossible to remember what you did last week, not to mention last year. This complexity is further multiplied with every new team member that joins your company.
Software patents raised a lot of hairs twenty years ago, mainly because while governments are slow to react to change, software evolves rapidly, and patents thus live on for too long in comparison to hardware. Let’s in this blog post take a look at how AI patents are similar and different from software patents and what challenges can be seen in AI patenting.
Production-grade machine-learning algorithms never come out perfect on the first try. They require the same approach to iteration and testing as any other software project. But validating machine-learning algorithms is particularly hard—harder than writing simple unit or integration tests. And iterating on machine-learning algorithms gets harder as the team contributing to it grows. Companies like Netflix, Google, and Amazon have designed special workflows to deal with this issue. They’ve built in-house tooling and created rules and procedures for testing and evaluating the algorithms their large teams are working on. But most companies new to machine learning lack a well-designed ML workflow when they find themselves getting to their first ML projects, and they encounter a number of problems:
All over the world, patents are known as the best way to protect inventions. They provide inventors with a period of up to 20 years to use an exclusive, monopoly-like position in the commercial exploitation of their creations. It is the key for getting returns on the investments they made during the research and development of their new technological solutions.
I see the quote “AI is the new electricity” thrown around in about every other blog post. I think there is truth in it, but I also think most people don’t go to the bottom of what it really means for their business. Let’s first define what we mean by AI: in this context, I’m referring to new advances in machine learning and deep learning.
[Valohai success story] Nyris is developing a high-performance visual search engine that understands the content of an image. The visual search engine works as an easy-to-use API that companies can use to inject visual search as a part of their solution.
Valohai is a deep learning platform that helps you execute on-demand experiments in the cloud with full version control. Jupyter Notebook is a popular IDE for the data scientist. It is especially suited for early data exploration and prototyping.
Pointlessly staring at live logs and waiting for a miracle to happen is a huge time sink for data scientists everywhere. Instead, one should strive for an asynchronous workflow. In this article, we define asynchronous workflows, figure out some of the obstacles and finally guide you to a next article to look at a real-life example in action in Jupyter Notebooks.