The risk of failing a project is higher when an organization is adopting a new and unfamiliar technology. Keep in mind these key elements to succeed in a Machine Learning project.
A true (silent) revolution is flooding the environment. Even though it came to the surface in 1959, it is in recent years that it has gained strength and relevance in the business world. Machine Learning (ML), when machines access data and learn for themselves, has found its own place within the 21st century tech trends, helped by the high demand and improvements of data storage, processing power, and ML tools.
Such has been the growth of Machine Learning that O’Reilly Media published the study ‘The state of Machine Learning adoption in the enterprise’ where they got into how industries have adopted and deployed their ML capacities. There, they noticed that 49% of organizations (from several regions) were looking into adopting ML. Another 36% claimed to be early adopters, and only 15% considered themselves sophisticated users.
Content related: Is your company ready for Machine Learning?
Perhaps you are considering exploring Machine Learning as a quick solution to complex, data-rich business problems so you can enhance your business scalability and improve your operations. If this is your current situation, here are some elements you need to consider when embarking upon a Machine Learning project.
1. Store properly
It is the ability to collect, store, and access large volumes of data. More data means more cases and therefore, more precise models. We can process more data faster and cheaper than ever before. Why? Because we now have solid-state storage and falling RAM prices. All these things are increasing storage capacities and reducing latencies.
2. Processing power
Here we are talking about the ability of processing collected data. The general-purpose units (GPUs) as well as the CPUs and their continued evolution has unlocked processing power in an unthinkable way. When it comes to scientific computing and ML, these chips are invaluable.
3. Software tools
We refer to execution programs and logic to process all the stored data. All the stored data and the processing power would be useless without the Machine Learning algorithms and software. The more people have access to these tools, the more incredible applications we can find.
The improvements on storage and processing power as well as the available tools make it possible for engineers to test their models in a faster way, reducing the development time. Machine Learning algorithms that process huge amounts of data are now available to a wide range of industries, and they do not need specialized knowledge.
Content related: How to become a data-driven organization
Although these three things I just mentioned are the core of a Machine Learning project, we cannot leave aside the following.
Make the right question
The first step to a successful Machine Learning project is usually the most difficult part, and it is asking the right question: What do you want? What are you expecting to achieve? Having a concrete question focuses you and your team effort, as well as defines the objective function (for example, maximize accuracy) and helps to identify the data you need to do the work. Without asking the right question, you and your team could expend many hours collecting, refining, and modeling data that produce useless products.
To avoid this, before moving forward, take a pause and clarify the question that you want to answer and define the objective function that you want to use to measure your progress. Take into account that your question may not be the right one on the first try, but it could be a good starting point where to iterate from.
Prepare your data
The second step is collecting and preprocessing data. Understanding the data as well as gathering and cleaning it is part of this stage. Data is a central pillar of a successful data science project. Approximately 90% of the effort is collecting and preparing data; the other 10% is for testing and tuning your model. That is why a domain expert (at least one) is needed.
At this point, it is good to remember that the data is considered the fuel of any ML project, so companies must treat the data like money: protect it and keep it safe and reliable.
Also, consider including a data scientist in the team, who is skilled at finding insights in data and has a high aptitude in math and statistics. Keep in mind that software engineers do not usually have this skillset.
To Sum Up
Advances in Machine Learning are the result of more than 50 years of gradual and continued evolution. Even though it seems like a lot, we have only just heard about it in recent years. The good news is that Machine Learning is here to stay and will keep growing within the business world so that it will be impossible to avoid it.
Many companies all over the world have begun to embrace this new wave and its benefits. The key to beginning on the right foot and making a good approach to this technology begins with knowing all the elements you have to bear in mind to succeed.
Taking enough time to define the right question, properly preprocess data, and consider the impact of using a specific model can greatly improve the success of your Machine Learning project. Are you ready to take the next step and jump to the top of the wave?
Comments? Contact us for more information. We’ll quickly get back to you with the information you need.See All Posts