You don’t need to be an expert to integrate AI in your startup
Published (updated: ) in Cloud, Product Engineering, Startups.
Originally published on The Next Web.
We’re used to hearing that AI and machine learning is hopelessly complex, impossible to implement quickly, and that if you want to get on board the machine learning bandwagon you’ll need to invest heavily in PhDs, specialists and expensive experts.
This way of thinking is simplistic and behind the times: machine learning is a broad set of technologies, and over the past few months and years there have been huge strides in making machine learning’s benefits much more accessible to startups, scale ups and lone developers alike.
Over the past few months I’ve spent a great deal of time investigating, learning about and iterating on a number of different machine learning technologies to take advantage of the vast quantities of time series data we have about infrastructure performance from my company’s product.
We’re collecting billions of metrics every data from hundreds of thousands of systems, all of which can be used to understand patterns and make future predictions. Read on for some easy, actionable advice on how to get started from scratch with machine learning — it’s easier than you think!
Avoid TensorFlow — for now
Google made headlines in 2015 by open-sourcing TensorFlow, their internal AI and machine learning framework. Released as an open source project, TensorFlow is following the same strategy as Kubernetes — provide such a good product that it becomes the industry standard, and offer a hosted, managed cloud version for those who don’t want to maintain it themselves.
You can run TensorFlow workloads yourself but Google’s Cloud Machine Learning Platform offers a much more optimised version, running on proprietary TensorFlow Processing Unit chipsets. The strategy is all about making Google Cloud the best choice for these jobs.
However, popularity can be deceptive and based on my personal experience TensorFlow is often not the best solution for startups and small companies. TensorFlow is great in that you get a high degree of control over your project but that control comes at a cost. TensorFlow is a framework, and we’ve found it requires significant data science knowledge and a lot of trial and error in building, iterating and improving your models.
It’s not a toolset you should pick up if you’re after easy results or plug-and-play functionality. Unless you’re a big corporation (which we’re not) or have the budgets to hire data scientists to get into model development, it might be tricky to secure enough budget to invest in TensorFlow from the start, so you’d be much better trying more simplistic managed solutions first.
The rise of ‘machine-learning-as-a-service’
For companies just starting out, the best place to begin is looking at the managed service solutions from the likes of Amazon, Microsoft and Google. These solutions are much more accessible to generalist teams, and companies that use them get the benefit of vendors updating them and improving service over time. Indeed, your own datasets help to improve the models!
This is because the larger the training data set, the more accurate the models can be. Anyone can play with theoretical models but the truly interesting work comes out of having real data, and this is an advantage the big players have even before they add your data into the mix.
We’ve found that Amazon Machine Learning is a great place to start. AML differs from TensorFlow in a number of ways: with TensorFlow, you build your own models and can then execute them against your datasets wherever you like whereas AML requires you upload your dataset to Amazon then use their API to execute queries. The downside is you don’t get to control the models and can’t see into the workings of the system – you rely on Amazon to get it right. This “plug and play” type approach but is less customised and flexible, so you may end up needing replacing it with something more specialist in the future.
If you need a very particular type of functionality — detecting items in a video, speech to text or translation, then there are specialist services from all the cloud providers. These services use machine learning behind the scenes, but you don’t need to think about it — send over the item for analysis and get the results through an API. These APIs are quite specific and so if they do a good job, you can just leave them to get on with it. It’s unlikely you’ll want to customise them enough to make it worth starting from scratch.
Outside of the big three cloud providers, there are a host of technology startups including Algorthmia, BigML and MLJar aiming to offer machine learning through an API or SaaS application.
Put your use cases first
I’ve seen many companies make the mistake of rushing into machine learning without having a clear use case in mind, and this is a significant error. There are robust ecosystems around each of the above ‘MLaaS’ platforms, and so you’ll need to have awareness of the APIs available to you. Tools like Amazon Polly (text to speech) or the Google Cloud Video Intelligence API deliver specialist functionality without requiring a high degree of knowledge as a prerequisite.
Since they are offered as an API, you can mix and match across providers and even test which does a better job where the service is the same. Most people will probably stick with the cloud platform the rest of their infrastructure is hosted on, but that’s not always necessary (data transfer cost and latency may become an issue once you hit scale though).
At my company, we’ve been migrating from IBM Softlayer to Google Cloud and the data transfer fees of (encrypted) traffic across the internet is part of the total cost consideration, and an incentive to complete the move quickly! Once it’s all within Google’s network then the lower (or zero) data fees apply when using their services, and Google is widely considered to have well designed machine learning capabilities.
I’ve found the advantage of using machine learning as a service APIs is that any developer can pick them up and start playing. Serious machine learning with TensorFlow requires a lot of time and real data science knowledge, which may be worth investing in over the long term. However, to get something up and running quickly and test the value proposition to your users, there are a variety of options.
I’ve had a lot of fun testing out the different machine learning APIs and solutions out there, and this element of fun and discovery makes it much easier to lead a team on a small exploratory project. I’ve also found that implementing something like Google’s 20 percent time, or even an internal hackathon could also be a good opportunity to get everyone focused on building an initial prototype.
Machine learning is a very over-hyped set of technologies — it’s currently ranked by Gartner as a buzzword, at the very top of their peak of inflated expectations. However there’s a vibrant set of technologies under this umbrella term, and you don’t necessarily need to have a highly-specialised workforce to take advantage of them. Start small, use the managed services provided by the big tech firms, and you’ll be surprised by how far you can go.