When we think of computer vision and AI applications all around us, from self-driving cars to smart home devices, the majority of us don’t know the complex processes involved in pushing that product from blueprints all the way to production. That involves a hands-on approach by teams of specialists ranging from CV engineers, data scientists, ML engineers, and more. Without harmonious and efficient workflows, moving forward with the machine learning project lifecycle is slow, inefficient, and certainly not beneficial to enterprises that heavily rely on ML application production. MLOps is a relatively new concept that only emerged around 2018 and has since become fundamental for ML production. Today, we’re answering the most frequently asked questions surrounding MLOps such as what is MLOps and why do we need it, and why MLOps is important in our current age of ML production.
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What is MLOps?
MLOps is short for ‘Machine Learning Operations’, and if you think the name closely resembles DevOps, then you certainly aren’t wrong. Many will go as far as emphasizing the formula for MLOps which is ML + Dev + Ops = MLOps. But what exactly does MLOps entail? It’s a set of practices that aim to manage, maintain, and deploy machine learning models to production to essentially establish automated lifecycle management thanks to efficient workflows. Executing MLOps requires the collaboration of data scientists, DevOps engineers, operators, and IT.
All of the following are vital components of MLOps needed to achieve promising results:
- Training data
- Infrastructure management
- ML model serving
- Model training
- Model re-training
- Model validation
- Model version control
DevOps vs. MLOps
MLOps reminds specialists of DevOps in many ways since the latter came first, paving the way for MLOps to channel many of its core principles. DevOps refers to ideas, practices, and technologies that improve an organization's capacity to provide applications and services at high velocity for continuous delivery. In terms of benefits, DevOps achieves the same goals and brings similar benefits as MLOps such as risk reduction and increased scalability and efficiency. Much of the differences between DevOps and MLOps lie in 1) the people involved (mostly data scientists and engineers for MLOps but software engineers and DevOps engineers for DevOps ) and 2) the fact that machine learning models require frequent re-training when models degrade, which is not a concept present in DevOps.
CI/CD for machine learning
MLOps adopts something from DevOps called CI/CD, which stands for Continuous Integration/Continuous Deployment. CI/CD for machine learning allows rapid delivery and deployment of code to production, resulting in basically an automated pipeline that produces production-ready code quicker than possible otherwise. This is a continuous process of updating, identifying faults in the model, returning to update the model based on the new data, and then going back again. Through it, you can automate your machine learning pipeline and notice a great reduction in the need for intervention by data scientists in the process, allowing them to concentrate on much more valuable aspects of the lifecycle. All of this is also known as CI/CD pipeline automation.
Continuous Training (CT)
This is a concept unique to MLOps that maintains the constant freshness of data to refrain from drifts or data skews. With CT, you can be certain that the algorithm will be updated via retraining upon the first signs of decay. Operators can decide the frequency of the retraining on a needs-based basis. For example, a training cycle can be created where the retraining can be initiated: daily, weekly, monthly, only once new data is available, only once model performance drops, or only when initiated manually.
Machine learning project lifecycle
In order to understand the MLOps lifecycle, we need to be aware of the standard lifecycle of a machine learning model from start to “finish”.
The cycle can commonly be broken down into three phases:
1) Development of the pipeline
2) Pipeline training
If we expand a bit further on the process, the steps are undoubtedly more extensive and follow this order: data collection, pre-processing of data, dataset construction, model training, refinement, evaluation, and finally deployment. The great part of MLOps is that it covers the entire ML lifecycle — and more. MLOps spans from the entire design and development process of a pipeline to training the model, deployment, and monitoring in post-production.
The crucial thing to keep in mind about the machine learning model development lifecycle is the fact that once begun, it becomes an open loop. Unlike code-based applications, machine learning models must be continuously monitored and maintained over time to see how they're performing and shifting with new data–ensuring that they're delivering real, ongoing business impact. When data becomes outdated, the model must be re-trained with relevant data to promote accuracy and so on. That makes the MLOps lifecycle an open-looped process as well.
Why we need MLOps
Now that we’ve taken an in-depth look at what is MLOps, we can answer the question in the back of many people’s minds, “Why do we need MLOps?” The better question would be to ask “Why not?” When MLOps best practices are integrated into the business model, a plethora of advantages become apparent. As with anything in the business world, a cost-benefit analysis is necessary and it’s up to you to decide what works best. Let’s break down the pros and cons.
- Increased scalability — A primary benefit of introducing MLOps to your business is the immense increase in scalability. Essentially, the goal of scalability is to achieve a greater magnitude of production without doing extra work that will inhibit growth. With MLOps solutions in place, the business will have the upper hand of not starting processes from scratch and replicating previous models for future implications, thus providing more avenues for the business to scale up.
- Security and governance — Issues related to security and governance are by far one of the most common without the presence of MLOps. A primary purpose of MLOps is to monitor all environment changes that limit the possibilities of information loss and unidentified changes, along with ensuring the model reaches its target goals thanks to model risk management.
- Maintain ML health — Much of MLOps follows through to the period after production where constant maintenance and monitoring is necessary to keep the ML model up-to-date, along with detecting any possible drifts. Enabling MLOps considerably boosts ML health thanks to consistent monitoring and adjustment.
- Quicker and efficient deployment — Did you know that on average it takes a month to deploy a single ML model without MLOps? Some may not even surpass this stage altogether. Streamlining this process is at the center of MLOps and aids businesses in pushing more models to production to increase ROI.
- Initial cost of implementation — Opting for an MLOps framework will be costly to implement which is what repels many people who first learn about what MLOps is. Over a five-year period, you can expect to spend anywhere in the ballpark of $90,000 for it. Many will affirm that it’s nonetheless an investment that pays off over time, yet it is costly for companies who do not have sufficient resources for it.
- Manpower limitations — Another one of the MLOps challenges businesses face is directly related to the individuals handling the operations. As mentioned above, ML operations are a collaborative effort that involves individuals from various backgrounds including DevOps specialists, data engineers, and data scientists. As production quantities expand, businesses will require more manpower to not only maintain current models but manage additional ones, increasing labor costs respectively. You will also need to oversee proper hand-offs in the case of shifts in staffing.
Does your business need MLOps?
Unsure whether or not your company needs to implement MLOps right now or even in the near future? Many sources will tell you that if you have the intent of productionizing machine learning models, then MLOps is not an option but a necessity. However, there are companies with fewer projects and resources that are not in a hurry to implement MLOps just yet. Here are a few tell-tale signs that MLOps is becoming a necessity for your business:
- You work with either Edge AI or cloud computing environments.
- You work with diverse languages, libraries, and tools to the point that it becomes difficult to keep track of them all.
- You aim to scale your ML applications in the near future.
- You have multiple models that are stuck in the deployment phase, not making it to production.
MLOps is a relatively new approach to machine learning model management that has taken a lot of inspiration from DevOps. It enables faster intervention when ML models degrade, resulting in higher data security and accuracy, as well as allowing enterprises a faster approach to the creation and deployment of machine learning models that is difficult to achieve otherwise. With MLOps in place, data scientists will not need to be involved in architecture operations or deployment and concentrate on the phases of the lifecycle that are in their expertise, decreasing stall times to production. With this, the full process from planning to moving to production can be executed more efficiently to benefit both from a business and science standpoint. At this point in time, MLOps offers more benefits to the ML lifecycle than limitations and is considered as a fundamental instead of a consideration.