It seems like everybody is breaking into machine learning (ML) nowadays. The proliferation of it alongside ‘big data’ has naturally led to a scramble among organizations who want to figure out how to use all the data that they’re collecting in a way that delivers value to their bottom lines. Indeed, growth in the ML space is occurring at such a rapid speed that the machine learning market cap is expected to hit US$117 billion by 2027.
Although it’s great that the influx in popularity of ML is leading to lots of newcomers entering the ML space—after all, AI and ML solutions can only improve if there’s widespread participation—it needs to be made clear that building and incorporating an ML project in a production environment is a highly technical feat. It’s no walk in the park, and firms entering the market without sufficient ML experience could be in for a rude awakening.
Unfortunately, many firms seem to be under the impression that running an ML project is fairly straightforward as long as you’ve got the right data and computing resources for training. This couldn’t be further from the truth, though, and it’s an assumption that could cause organizations to needlessly waste money by embarking on projects that had a near-zero chance of ever making it to deployment.
In this article, we’re going to discuss what the life cycle of a machine learning project actually looks like in a bid to give organizational leaders a better understanding of what’s involved.
The reality is that machine learning projects are not straightforward. Rather, they’re a cycle iterating between improving the data, the model, and evaluation. The cycle never truly finishes, and it’s crucial for developing ML models because it focuses on using model results and evaluation to refine your dataset.
This means that unfortunately, the ML lifecycle isn’t something that you can complete once and forget about. Much like any system, a deployed ML model requires ongoing monitoring, maintenance, and updates. ML models that have been deployed in production environments are going to need regular updates as you uncover biases in the model, add new sources of data, require additional functionality, and more.
With the right approach and tooling, however, managing the ML lifecycle needn’t be something to fret about. We’re now going to break the process down into its four main phases: data, model, evaluation, and production.
The lifeblood of any ML model is the quantity and quality of data that it’s rained with. The biggest data-related tasks in the typical machine learning lifecycle are:
When trying to improve model performance, ML teams will spend most of their time trying to perfect the data. This is because if a model is not performing well, the cause is almost always a data-related problem such as a training dataset containing too many biases. In addition, making improvements to models generally involve things like hard data mining, rebalancing, and updating annotations and schema.
The model phase involves creating a model and training pipeline and training and tracking model versions. Despite the end result of any ML project being a model, the model phase requires the least amount of time spent on it in comparison to data, evaluation, and production.
Typical model phase tasks might include:
Once you’ve got a model that has been trained, it’s time to see how well it performs on new data by evaluating it. Tasks include:
You can only get to the production phase when you’ve got a model that performs well without any major errors. But this doesn’t mean that the work is over. Far from it, actually. Production is the most important and most difficult to manage phase and it involves a lot of work, including:
Only a very small (we’re talking minuscule) number of organizations that try to incorporate machine learning actually make it to the stage where a model is deployed into production. This is because, despite what people may think, developing, deploying, and managing an ML model is an incredibly complicated and labor-intensive process.
That shouldn’t put you off, though. While it was once the case that only organizations with significant amounts of money behind them or dedicated machine learning teams (or, in most cases, both!) could be said to be in a position to deploy their own ML models, the proliferation of machine learning services and tooling has made the prospect of doing so much more accessible to smaller businesses.
That’s not to say it’s easy, though. Even with the best ML tooling in the world, building and deploying an ML model is a lot of work. Whether this is worth it depends entirely on your organization, what you’re trying to achieve, and how much potential value ML could deliver.
For smaller businesses that do decide to build their own models, they’re increasingly turning to platforms like Qwak to get the job done.
Qwak is the full-service machine learning platform that enables teams to take their models and transform them into well-engineered products. Our cloud-based platform removes the friction from ML development and deployment while enabling fast iterations, limitless scaling, and customizable infrastructure.
Want to find out more about how Qwak could help you deploy your ML models effectively? Get in touch for your free demo!
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