Getting to the point where an organization can deploy machine learning models is pretty exciting because it makes it possible for these businesses to develop a competitive edge using their data. Still, there are some factors, such as the feedback cycle, deployment orchestration, and iterative development, which must be considered when putting these models to work.
To begin, let’s assume you’re running an e-commerce enterprise, and you’ve been tracking the conversion data of your business to develop a recommendation algorithm. Chances are that you may want to know which products to send to users via social media or email that'll trigger a purchase. This way, you will be using the customer information to define the problem, and once you have determined the issue, you will need to gather the right team to help solve it. In your team, you'll definitely need people who can collect and format the data, as well as individuals who can select and train models. Once you have done this, you'll finally get to the deployment of a machine learning model.
To determine conversions, you can run analyses in batches. In some instances, you may want to train your system using new and old user data, along with the reality on the ground, as to whether or not the users converted. Doing all these things means running your model on real data, and analyzing results to determine if your recommendations increase user conversion and engagement. You can re-evaluate and refine your problem based on the data generated from the results. Alternatively, you could enter the data into a learning loop.
Model deployment is a process which involves two equally important parts; production deployment, plus analysis and monitoring.
As a rule of thumb, a machine learning model must be simple, accurate, and scalable, to work.
To determine what works best, tack the AI models performance onto a continuous stream.
These two processes of deploying machine learning models must occur in unison while, at the same time, keeping your team on the same page. However, assembling the right team to work in tandem and sync could prove to be one of the most significant barriers to the deployment of machine learning models in production.
Many data-driven enterprises today are building (or have developed) extensive workflow tools and pipelines to handle the machine learning deployment cycle. However, there is undoubtedly often a noticeable disconnect between the teams, which makes it difficult to maintain the workflow. As a result, many contemporary entrepreneurs have to deal with many challenges when deploying machine training models.
Deploying machine learning models in production is slow, and things have to be scaled up manually. When multiple teams are tracking machine learning models separately, there is a lack of one true source, and this makes performance monitoring difficult. Performance is individually evaluated, and other stakeholders are not able to see results on the go. Hyperparameter checking is also done manually, and it is poorly documented.
Luckily, there are better ways to counter the problems mentioned above, which help the teams work in unison towards the realization of a common goal. This leads us to the next section.
A well-deployed model should be sustainable and accurate for a long period of time, and you can ensure this by following certain simple steps, including the following:
Although the process of deploying machine learning models in our everyday activities is seemingly complex, the truth is, that it is an iterative loop. Sure, it calls for full collaboration and coordination, but it can be scaled down to only essentials. MindsDB is working hard to make the process simpler.