Ensuring Your Machine Learning Platform Is Future-proof
One of the biggest problems in the machine learning industry is that things are changing so rapidly. Because of that, machine learning platforms have a massive problem constantly keeping up with change. These machine learning platforms come in the form of commercial toolsand also in-house solutions.
As a business, it is crucial for you to have principles in place to ensure that you don’t end up with a big problem. There are three principles that can help guide your efforts in building machine learning models. ML models are the output in every project, so it is crucial that you can successfully deploy them every time.
The most important thing you need to know is that the right machine learning platforms will be the ones that you can use in the future. Building platforms will become an industry itself.
Have a Process for Working with ML Data
The first step to ensuring that your ML platform will be useful in the long run is to have a streamlined data operation that can stand up to the test of time. One of the easiest ways to do that is to use open formats for your data. By avoiding all things proprietary, you make it easy to continually use tools supported by the entire industry.
Data is also all about the platform used to process it. We know that the cloud is the future of computing, which is why you should orient your data formats and storage to the cloud. You can do it in a way that lets your data scientists have easy access to all the information they need in R and Python. That makes it easy for them to do the hardest part of the machine learning process. You might think that machine learning models would be difficult to build because of the engineering challenges involved, but the reality is that data processing is the most difficult part. It is usually the part of the process that takes up the most time and effort. Any aspect that can be automated will result in massive increases in productivity.
Build Bridges Between Data Science and Machine Learning Engineering
The second principle involves the fact that machine learning projects need both data scientists and machine learning engineers to bring to market. However, there isn’t an ML platform that both can use simultaneously. As it stands, ML models go through an iterative process that starts with data scientists and ends with machine learning engineers.
One of your biggest priorities should be facilitating the collaboration of these two groups of workers. You need to have the right tools and processes to ensure that there is a lot of back and forth between these two groups. You also need to have a management structure in place that works well with both of them. Sharing your machine learning models should be a major priority for your project as well.
Be Adaptable to Changing Environments
The traditional machine learning model development process looks like what you see above. However, it is crucial to note that things will not always be the same way. You’ll eventually have to plan for a world where machine learning model development is streamlined. Having a scalable solution is also crucial in this environment. Do your machine learning platforms scale? That is something you need to figure out before you even get started. You also need to look at potential software and technological changes in your stack. If you don’t want to go to all of this, you might want to stick to an off-the-shelf end-to-end solution like xpresso.ai.
