The new rules of Build vs. Buy in an AI-first world
As artificial intelligence as a service (AIaaS) emerges, the build vs buy debate is clear.
Since the field of AI and specifically computer vision has evolved to a point of being accurate and scalable enough to be useful in real-world applications, enterprise businesses have begun to realize the tremendous efficiency and revenue gains that are possible. These types of process efficiency gains are hard to ignore, and as a result, enterprise businesses are seriously evaluating the spectrum of possible AI strategies for the first time. The problem for many businesses is that it’s difficult to make an informed decision when there are so many possible solutions. There is the traditional ‘build in-house’ route dominated by open-source toolkits like Kaffe, Theano, and Tensorflow, or the full-service consulting approach led by IBM, with the artificial intelligence as a service (AIaaS) approach somewhere in-between.
This is why the engineering and product management teams that we work with at Clarifai often look to our expertise to help them make sense of the noise. When we help businesses with their AI strategy, there’s just one question that really matters:
Are you an AI company?
By that, I mean, ‘Is AI your single core competency?’ Do you have teams of data scientists and machine learning engineers dedicated to this cause alone? Are you willing to dedicate engineering time away from improving your product features, understanding your consumers’ behavior, or building core infrastructure? If the answer if yes, then utilizing the myriad of open-source machine learning toolkits that exist and building and maintaining your AI in-house makes sense. If the answer if no, and your development teams are focused on building products and features that support your e-commerce, media, content, or social platform, you are better off choosing the best AIaaS service for your needs. Here are three reasons why:
1. Minimizing time to market to maximize the impact of your AI strategy
Building machine learning in-house takes considerable time, effort, and upfront investment. You need a team of data scientists, the right infrastructure, tons of data, and a lot of time to build a production-ready machine learning solution. And it’s not just about the time spent actually building and optimizing the machine learning solution itself — business leaders should also factor in the time involved in hiring the right people and getting the budgets approved for multiple line items.
2. Keeping up with continuous innovation and paying down technical debt
The AI space is evolving rapidly. New papers and concepts are being introduced regularly and staying on top of the research that is required for best-in-class AI can be daunting. Having a team of R&D experts at hand who are dedicated to understanding, incorporating, and introducing new technologies and concepts to an AI platform is a huge benefit of working with an AIaaS partner dedicated to building solutions in this space. Furthermore, AI is not a one-size-fits-all solution, so leveraging experts who can train and customize models to suit your unique business needs is invaluable in getting the most business value out of AI.
Another consideration for businesses building machine learning into their workflows and customer experiences is ongoing maintenance and technical debt related to machine learning. A recent Google Research paper argues that “Machine learning systems have a special capacity for incurring technical debt because they have all of the maintenance problems of traditional code plus an additional set of ML-specific issues. This debt may be difficult to detect because it exists at the system level rather than the code level.” Using AIaaS removes a layer of technical debt and helps businesses scale and future-proof their AI solution.
“Machine learning systems have a special capacity for incurring technical debt because they have all of the maintenance problems of traditional code plus an additional set of ML-specific issues. This debt may be difficult to detect because it exists at the system level rather than the code level.” — Google, 2015
3. Leveraging shared infrastructure and expertise
Both compute power and analytics tools are key to scaling any AI initiative. Working with a partner that analyzes millions of visual pieces of content per day has huge advantages. There’s no need to worry about the cost of monitoring and scaling an IT backend infrastructure as volumes grow and technologies evolve that require more compute power. There’s also the additional benefit of being tapped into a network of data that far exceeds your own, and being able to apply the aggregate learnings from this data to your own unique use case.
Any business can benefit from partnering with AIaaS, whether they have an in-house machine learning team or not. For businesses without an in-house machine learning team, AIaaS can provide a full end-to-end solution for your AI strategy by enabling your development team to take advantage of machine learning with a simple API. For businesses with in-house machine learning teams, AIaaS can allow those teams to focus on the fun, rewarding parts of machine learning without having to worry about the hassles of maintaining infrastructure.
To recap, if you’re not an AI company, don’t build it yourself. AIaaS lets you focus on your core competencies while leveraging all of the best things AI can let you do. AIaaS allows you to get all of the benefits of building machine learning in-house (and more!) and none of the hassle.
And it wouldn’t be a blog post if it didn’t end in a shameless plug! Clarifai is an AIaaS company that allows you to benefit from the intelligence of billions of data examples while abstracting away all the boring work of maintaining infrastructure and cleaning data. If you’re a business whose goal is to get up and running with a tailored AI solution as quickly as possible, let’s continue the conversation here or at firstname.lastname@example.org.