Video: Tips for Getting Started with Video AI Platforms
Learn more about AI platforms at Streaming Media's next event.
Read the complete transcript of this clip:
Jun Heider: If you don’t have developers in-house at your organization, a lot of these vendors offer demo versions of their platforms that you can play around with. The free version of Microsoft Azure Video Indexer, for example, is quite robust. You can actually take widgets and embed them into your own systems. Of the four platforms we discuss in this article, Video Indexer is the most complete product that you can access without having to go through contract negotiations.
Google Cloud and AWS offer demos on their pages, but they don’t really expose them to the degree that you can embed them into your own system. With AI platforms, “embed” essentially means taking a code, putting an I-Frame into your system, and loading up their page inside of your system. The online demo versions of Google Cloud and AWS don’t allow that, but you can upload your own videos, wait for the apps to process them, and see how well the service will work with your video.
IBM sells several Watson products, such as Video Enrichment (top right in Figure 1). They are API-first. Theoretically, you can buy their paid product, and communicate between your system and theirs to pull metadata into your system.
This is one of the big use cases for AI: You have this media asset manager (MAM) that houses all this metadata about all the assets being stored there. Wouldn’t it be great to have the computer generate some of that metadata for you, so that in addition to the title and the season and the episode name, you have things like “car crash,” “leopard,” and “little baby girl,” or whatever terms apply to your content, that you can actually search and discover your content much more easily? The first thing your developers are going to want to learn about is, what’s the learning curve? What software development kit is available for each of these platforms?
AWS has a client SDK, which basically acts as an accelerator so your developers don’t have to write all the code from scratch. They can build their code on top of the boilerplate AWS provides, with SDKs for Android, iOS, Java, .NET, etc. My team of developers has played around with this SDK, and they have found it comprehensive, and much less wordy than Google Cloud.
One important step in choosing a video AI platform is to let your developers test whatever demo version is available and listen to their feedback, because your team is going to be more productive if they feel that they don’t have to spend as much time trying to grok how to work with the software. Like AWS, the Google Cloud platform has a good number of client SDKs: C#, node.js, Go, Java, etc. But from a developer’s perspective, their API documentation is quite verbose, and it takes a lot of clicks to get to what you need. If I just want to know how to send my video up to their service, rather than seeing the signature of the payload that I want to upload, I have to wade through 3-4 paragraphs on every single data point within that signature.
IBM Cloud’s API reference lives behind a pay wall. You talk to the IBM Watson media people, and you say, “This is my use case. I want to play around with your system, because I think I’m going to buy it.” Then you probably sign some contracts. In my case, as an IBM partner, they were kind enough to share API documentation with me. It looked pretty straightforward.
Azure Microsoft Video Indexer has a client SDK for .NET only. If you don't have .NET developers in your shop, Video Indexer might not be the right solution for you. Amazon and Google have things like Java, Android, iOS, Ruby, and so on. Your developers might be able to build something faster on those platforms. The Video Indexer documentation is outstanding. It’s well laid-out, and you can inline-test it as long as you have an active account.
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