A few years back, building a machine learning system or model belonged in the realm of science fiction. Fast forward to the present day, resources are more accessible than ever before. Whether you decide to cobble together your own model or outsource from a software-as-a-service (SaaS) provider, the options are endless.
Given the current prevalence of machine learning technology and its obvious benefits, it’s only a matter of time before it becomes a business must-have. So if you are yet to come to that fork in the road, it will happen sooner or later.
The big question is, which one is better? Should you build your own machine learning system or outsource machine learning from SaaS providers? In this article, we will simplify the decision by highlighting the benefits that have made developers flock to Microsoft Cognitive Services. You can draw on the lessons from this scenario to make an informed choice between to build and to rent.
Top Reasons Why Developers Love Microsoft Cognitive Services
Ever since the launch of Microsoft Cognitive Services, developers have enjoyed the release of new innovation after another. It offers lots of handy benefits that allow users to integrate machine learning into apps and services in a seamless and hassle-free way.
Take a look at some of the benefits:
Cutting Back on Costs
Microsoft Cognitive Services is a collection of APIs, SDKs and services designed specifically for developers to assist then in the creation of intelligent apps. As any developer will tell you, building these from scratch is prohibitively costly.
Besides the development process, other expenses include high maintenance requirements and the costs of getting the rare forms of expertise needed for such projects. Try to envision how much it would take to create your own algorithms and build a server specifically designed to handle the workload.
That’s not really necessary when you have the option of transferring the weight to cognitive services and paying a subscription fee in exchange.
Thanks to Microsoft Cognitive Services, developers can easily create, manage and deploy powerful applications using just a few lines of code. This way, they can focus on the customization aspects of development and fine-tuning models for optimal performance.
They can add high-quality speech, vision, language, search and knowledge technologies into their apps without much of a hassle. The models they create can easily confirm identity using appearance or voice, talk naturally and identify relevant content in images.
A lot of the AI systems in the market today are more annoying than functional. If you have ever had an online retail store recommend items you already bought, you might be familiar with poorly designed algorithms.
But by taking advantage of the tried and tested APIs on Microsoft Cognitive Services, developers have the opportunity to create algorithms that not only work but work effectively and efficiently. And the result is better customer experiences which, in turn, can enhance performance.
Another important aspect of using this software-as-a-service offering is the fact that it significantly cuts down development time. Building a machine learning model from scratch involves creating code for the algorithm, collecting and cleaning data and then training the model.
Before it becomes perfect or even close to it, a lot of training and testing goes into the process and that takes time. But with the opportunity to customize pre-built models, deployment takes a much shorter amount of time.
Microsoft has created a comprehensive list of cognitive tools. These allow developers to access powerful intelligence using just a handful of lines of code. Rather than being confined to the pages of fantasy, machine learning is now an everyday reality, within every developer’s reach.
And they do not have to go through extensive training programs to access the benefits. It is now easier than ever before to make use of these provisions to get aboard the artificial intelligence bandwagon.
Harnessing Microsoft’s Boundless Potential
Given the resources at the disposal of large tech companies like Microsoft, it is easy to see why developers are flocking to Microsoft Cognitive Services. The company has some of the most powerful bot tools in the global market. They are thus able to provide what developers need to create more natural computer and human interactions.
At the same time, the company has at its disposal massive computational power offered via cloud computing. This far surpasses what most enterprise users can afford to acquire or maintain. It also makes seemingly implausible feats a reality, especially for the resource-intensive field of machine learning.
They have already developed the necessary algorithms for machine learning and have trained them using huge amounts of high-quality data. As such, the potential they provide for developers is far beyond what an individual can access under proprietary systems.
Core Ingredients of Machine Learning
In order to place the above benefits in context, it is necessary to understand the dynamics of machine learning.
Implementing a machine learning system in your organization requires three key ingredients:
- Lots of computing power
- Massive amounts of data
The idea of a three-ingredient recipe might sound easy enough to implement. But a hard look at the individual ingredients paints a different picture. The first step to assessing your capability of building vs. renting has to do with analyzing strengths. Weaknesses are best filled using third party as-a-service options.
Getting the computing power required for machine learning requires massive investments of time, cost and expertise. But thanks to cloud infrastructure, that may not be necessary. Additionally, top service providers like Microsoft do not simply have the scale required. They also have custom hardware for machine learning. Creating a system that can compete at par with such proprietary models is based more on fantasy than reality.
Data strength is a lot more common than the other two ingredients. But getting the right amount and quality of data required to train a model is no mean feat. It is important to note that if you were to take the same neural network and expose it to different datasets, the results would be remarkably different. This underscores the importance of getting the right data for your machine learning system.
It might sound lucrative to create your own algorithms, after all, everyone wants code that can do things better than everyone else’s. But the cost of development for such an algorithm can be incredibly high.
A more realistic outlook can be realized if developers ask themselves, “What is more valuable, owning an algorithm or delivering results?”
Platforms like Microsoft Cognitive Services offer pre-trained models that you can easily customize to your needs. It’s therefore more important to have API management skills than to make a huge investment into in-house algorithms.
Do You Have to Do It Yourself?
In view of the complexities involved in building your own model, vis-à-vis the benefits of outsourcing highlighted above, it is clear that you do not have to reinvent the wheel. Leverage the strengths you have and find partners to cover your blind spots with as-a-service offerings.
This way, you will get aboard the hype and help grow cognitive technology without spending millions and decades in the development process. Whether you are an enterprise decision-maker or a developer, the use of Microsoft Cognitive Services provides a handy transition to the new AI-driven world that is fast unraveling.
Latest posts by Janica San Juan (see all)
- Machine Learning for Content Filtering – Winning the Battle against Harassment and Trolling - January 30, 2020
- 7 Steps to Correct Data Preparation for Machine Learning - January 22, 2020
- Machine Learning Chatbots: What You Should Know about Neural Conversation Agents - December 18, 2019