It can sometimes seem that we’re living in a ‘blink and you miss it’ era when it comes to technology. Barely a week goes by without some announcement regarding a new development or improvements to an existing tool or system. It can be difficult to decide whether to try something new or stick to tried and tested methods.
Machine Learning stands as a pivotal component in the realm of artificial intelligence. For those contemplating a tech career, it’s crucial to recognize that the path diverges from traditional programming. Let’s delve deeper into this distinctive landscape.
What is machine learning?
ML is part of the AI ‘family’ that has seen rapid growth in nearly every sector. It works as an automated process that allows your systems to collect and analyze data without any direct programming input (after it’s been set up, anyway). In effect, machine learning systems ‘learn’ how that data works, what rules apply to it, and what patterns in your data are distinctive.
Because ML can learn from its mistakes – and its successes – it can be highly useful in a range of scenarios. Over time, you can see how performance improves and gives better results.
Advantages of machine learning
- Can handle large datasets. Larger datasets can be difficult to analyze manually, but ML can handle a lot of data. So whether it’s for predictive analytics based on an exceptionally large customer base, or managing cybersecurity in data lakehouses, ML can help.
- Versatility. ML is ideal for recognizing images and speech, NLP (natural language processing), and for predictive analytics. It can learn based on what you provide, meaning it’s very customizable.
- Learning. As the name suggests, ML focuses on learning from data. That means you can see significant improvements in performance over time.
- Patterns. Another area where ML excels is in seeing patterns in data that may not be obvious to a person. This makes it ideal for use in areas such as cybersecurity/fraud detection.
- Automation. The algorithms used by machine learning can mean that you can automate decision-making and other processes, speeding things up and reducing human error.
Disadvantages of machine learning
- Complexity. ML can be incredibly complex and can be difficult to debug or interpret. If you use ML, this can mean that you may face issues when it comes to understanding outputs or identifying problems.
- Time. Because of the very nature of ML, it can take time for it to ‘get up to full speed’ as it needs a lot of data before it starts becoming effective.
- Hardware. Some ML algorithms require a lot of computing power. This can place demands on your computer systems and can mean that scalability is challenging. Luckily, cloud-based solutions can help with this.
- Real-world adjustment. If you use training data in the early days of your ML model, there is a chance that the data will look very different from the real-world examples it will be applied to. You may also encounter data drift in machine learning, leading to a loss of reliability.
What is traditional programming?
Traditional programming has existed for many years and involves a hands-on approach. You write out instructions in a set language, aiming to solve a specific problem or create a particular effect.
You will find programmers working in almost every area that involves computing systems. They may be involved in application modernization, cybersecurity efforts, or making custom APIs for a tech stack. They create the rules – and logic – found in each and every program you use so that the program works as intended.
Advantages of traditional programming
- Control. Because traditional programming is so hands-on, your DevOps team can have complete control over what happens.
- Faster. You may think that ML is faster but the opposite is often true. A traditional programmer with years of experience can start straight away, while an ML model has to be trained first.
- Debugging. You may find that debugging traditional programming is easier than with an ML model. As problems are usually related to the code itself, it is relatively simple to check and fix issues.
- Security. With traditional programming, you have more control over security as your coders can manage how the program deals with any confidential data and access to your system. Plus, you don’t need to spent effort training an algorithm to understand what is CMMC – your team will already know just how to meet it.
- History. While ML is a recent development, programming has a long history with plenty of research and peer papers to draw on. This means your DevOps team can access both resources and best practices.
Disadvantages of traditional programming
- Lack of flexibility. If you have a scenario where data and circumstances change often, then manual updates of the code are needed, limiting your flexibility.
- Scalability and time constraints. If you are working with large datasets or complex issues, then manual programming is needed – which takes a lot of time and resources.
- Automation. Automating processes and systems can take a lot of time with traditional programming as every piece of code needs to be manually written or updated. That said, you can integrate automated solutions like Delta Streams into your tools to pick up some of the lack.
- Lack of insight. ML offers a level of insight into data that may be lacking with traditional programming – it can recognise patterns that humans miss, at a speed we simply can’t match.
Machine learning vs. traditional programming
Of course, the million-dollar question is which method would be best for your project. The answer is that it depends entirely on the nature of your project. Put simply, if you can identify that there are factors in your project that are problematic with traditional programming, then there is a very good chance that ML will offer an effective solution.
If your project involves larger datasets (or complex issues), then ML will be a better choice. Just remember that within the field of ML, there are different algorithmic models that you can look at and consider, such as Random Forest vs Gradient Boost.
When your project faces changing data and/or circumstances, then again, ML makes for the better option. Whereas traditional programming requires manual changes to reflect data changes, ML can do it automatically.
Another area where ML makes for a better choice is if your project involves things like NLP or image recognition. As it quickly recognizes patterns, ML can be very effective for this type of project. The thing to remember with AI is that you need to think about compliance when it comes to data protection.
Conversely, there are projects where traditional programming is your best option. For example, if you are facing issues with algorithms that have clearly defined rules and logic patterns, then manual coding is going to be a better choice than ML.
One way to approach the choice is to think of the tasks involved in your project. When you face tasks that do not have clarity when it comes to rules, then think of ML. Any tasks that involve prediction or recognizing patterns will in most cases be better handled by ML. But for things that you need complete control over, or specific logic and rules that don’t change? Traditional programming is the way to go.
The hybrid approach
One possible answer to whether to choose ML or traditional programming for your projects is to consider using both.
Having your DevOps teams utilize both ML and traditional programming could mean that you can enjoy the best of both worlds. You then have the rules-driven programming offered by a manual approach combined with the data-driven approach of ML algorithms. As projects often consist of different needs and tasks, you can see where both methods could be useful.
You need to think about what your project entails as a whole and break it down into identifiable tasks. When you do this, you can see where ML can solve issues and where you would turn to traditional programming. You can also see where ML and other AI-powered tools can help in the development process.
The takeaway
The answer to whether to choose ML or traditional programming has no definitive answer. Both methods can play crucial roles in your project and tasks. Of course, one thing to consider is that ML is still in its infancy and we have no idea what developments and improvements we may see in the future.
ML certainly comes into its own when you are working with large datasets or want to solve more complex problems. But will future developments in ML mean a decline in the need for traditional programming? Only time will tell. For now, developers need to make informed choices as to which method best suits the task at hand.
