You don’t need a data scientist to launch your AI project

It’s now or never. Substantial progress in various areas in artificial intelligence (AI) has only accelerated the development of this technology. This has in turn potentially reshaped the competitive landscape of companies across geography and industries. If you haven’t already invested in AI or have machine learning (ML) implemented into your business process, then you are just losing your competitive advantage over your peers but you are being left out of the competition altogether.

Over the last few years there have been plenty of breakthroughs in AI. Much of the progress can be associated progress in the following three areas.

Improvements in computing power

There has been a continuous progress from central processing units (CPUs) to graphics processing units (GPUs) at the silicon level. Today, GPUs can be up to 80 times faster than the fastest versions available a decade ago. This development can put early adopters ahead of competition with the advantage to use the resources to drive breakthroughs in AI development.

Access to data

It is undeniable that we have not just created unprecedented amount of data but we also have easier access to it. In fact, International Data Corporation (IDC) forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That's ten times the 16.1ZB of data generated in 2016. The diversity of data is enormous generates new insights that businesses can leverage.

Better algorithms

AI platforms and models are built and run on algorithms. The better the algorithms, the better the AI models. Advances in deep learning techniques are enabling algorithms that are more accurate in data classification and prediction. Deep learning uses neural networks at large scale that learn through the use of training data and backpropagation algorithms. There are emerging techniques such as meta-training which attempt to automate the design of ML models by classifying images in large data sets.

Once you’ve decided to kickstart an AI model, the next question you want to address is whether or not the AI efforts will be successful. According to recent studies, nearly 70% of AI projects fail even before they reach production. The success of your AI project is defined by the impact it has on your business. But how do you know if it really is making an impact?

While there are plenty of reasons that can be associated with the failure of AI, there are two reasons that constantly surface.

Lack of unicorn data scientists

There are two parts to this – a shortage of good data scientists and the cost of hiring one. While it’s difficult to find a unicorn, the cost of hiring one can weigh heavily on your budget, particularly if you are unsure of the success of your AI project. But you’d be happy to know that not all AI projects require you to have a data scientist onboard. Brainalyzed Insight offers pre-built algorithms and easy-to-build AI models with zero coding expertise required. All you need to do is select the data that you want to train the model on, select the objective/outcome, and just like that you have the predictions you need.

The readiness of the data

If you want to build effective AI models, you need to train the AI model efficiently with the right data. You will also need great data scientists to help you work through building the model. Relying on scarce IT resources to help you with preparing your data is quite the bottleneck you don’t want in your AI project to begin with.

Also, it makes it difficult for your AI project to be scalable. Brainalyzed Insight’s powerful artificial swarm intelligence (ASI) platform help you prepare your data easily without the help of a data expert. It’s self-service data preparation module is built keeping the non-data scientists in mind. It enables users to perform sophisticated data preparation tasks by themselves.

But that’s not all. Brainalyzed Insight provides a collaborative environment that allows you to reuse the AI projects, the steps within the data preparation, or previously created custom datasets. This gives you a head start on new AI efforts.

The ASI platform also manages the entire AI lifecycle from end to end – beginning from sifting through and organizing raw data, making it AI friendly, creating the ML model, and then deployment.

For organizations scurrying around to find a data scientist to handle their project, using a no-code AI platform is the best way to make the most of the AI efforts and automate business processes effectively.

Closing thoughts

Not many were prepared to take on this new technology – AI or ML – and scale it. However, the market is flooding with AI and ML platforms and its expanding as more data is being collected. If you want to stop playing catch up, you need to start now. In this article we’ve discussed what you are missing out on if you have not invested in AI already and what you can do to get things going. A good place to start is to use an automated machine learning platform like Brainalyzed Insight. This will not just save costs for you but also give you a head start on your AI project. This is the right tool that is best suited for users who are not familiar with programming and coding. Why not give it a try and see for yourself? Sign up for a free trial

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