How To Fit Machine Learning into Your Business Tech Stack : Fitting Machine Learning into Your Business Tech Stack

Fitting Machine Learning into Your Business Tech Stack

Machine Learning into Your Business Tech Stack Machine learning (ML) has become popular for its revolutionary impact on how tasks are completed in industries and offices. Machine learning trains an algorithm model to ‘learn’ how to come up with a prediction or output data. ML can learn this by first being trained using training data. The training dataset contains the attributes and relationships of the data that you’d want your ML model to understand and learn.

However, building ML algorithm models doesn’t have to paralyze your entire business operations. Your business already has an existing tech stack for most aspects of your operations. Such as marketing, sales, advertising, finance, accounting, planning, and operations. You can check cnvrg.io to find ways to fit your ML models into your business tech stack. Here are some suggestions.

Familiarity With Machine Learning

To make the most of machine learning and artificial intelligence (AI), you must train your entire team or organization on the capability, applications, and potential uses of ML in your business stack.

Your employees and workforce have both their group and individual creativity. And skills that can be tapped to fit ML into your business tech stack. But they should at least have a conceptual or working knowledge of what ML and AI can do. Once they have that knowledge, they’ll be in the best position to unleash the potential of ML for your business. ML isn’t that difficult to learn, and some say machine learning and artificial intelligence are just like vibrations.

For example, you can train your employees on predictive analysis and the things that it can do. Your marketing team will then know how to fit it into their market analysis and forecasting applications. Your supply team would know how to use it in improving their supply chain management applications. Here are some online resources for remote training on ML applications and integration.

Identify the Problem You Want Machine Learning to Address

When you’ve already trained your people on ML and its wide set of applications, you should ask them to list down the urgent problems they’re facing. They need to frame the problems correctly to help ML zero in on the problem and develop the solution.

When your people understand the capabilities of ML, they’d know how these capabilities can be harnessed and applied to improve or enhance your existing products and services. They might already be working on something, and then they’d realize that the capabilities of ML can help them solve a problem or add a feature to a product that customers have been asking for based on client feedback analytics or using ML for SEO.

Some companies hold workshops for their employees. The technical exchanges and detailed discussions proved to help come up with productive outputs that  solve a problem or resolve issues. The specific ML capabilities that can be applied would vary from one team to another, but the bottom line is that ML can be used to come up with faster and smarter solutions to persistent business problems.

Prioritize Concrete Business Value

ML can do a lot of things for AI, and there are limitless ways by which AI can help sort out problems in your business, but having the capability to do a wide array of things doesn’t mean that you have to do everything you can. You have to keep in mind that while the permutations of what ML can do for your business are practically limitless. Your work hours, financial resources, and human resources are not. 

You have to examine the various ML applications your team identified and proposed for project development and implementation. Evaluate which among these proposals and recommendations have the highest potential business and financial value. You’ll need to decide which options will make your business more profitable, increase its productivity, or allow it to corner a larger share of the market. 

An industry expert observed that it’s easy to get lost in the ‘pile in the sky’ discussions about ML and AI. To come up with your company’s priorities for implementation. You should closely examine the dimensions of potential and feasibility of each specific proposal. Some industry analysts recommend that you prioritize based on how fast or early the project’s deployment and implementation will take. You should also look at the possible financial contribution of each proposal to your company.

Endnote

When you consider which ML projects to do, keep an open mind. You shouldn’t immediately reject a proposal just because it seems too difficult to complete, execute, or accomplish. Moreover, be able to sell more products, or earn more money. In the same way, just because a proposal looks quite easy to iterate. Furthermore, doesn’t mean you have to go for it. Lastly, if it’s not going to bring in more money or customers. You’ll have to think harder about why you want to do it.

Artificial Intelligence & Machine Learning – Rebellion Research

Fitting Machine Learning into Your Business Tech Stack