It’s no secret that the idea of implementing MLOps can seem overwhelming. While the goal of machine learning operations is to find faster and more effective ways to productize machine learning, many organizations struggle with it early on.
The first step is understanding what MLOps is and how it works, which we’ve covered in an existing post. Now, the question is: What’s next? In this article, we’ll break down the MLOps principles and best practices you can follow to set yourself up for long-term success.
- Establish Business Objectives and Clear Goals
- Open the Lines of Communication Right Away
- Build for Scale From Day 1
- Choose Your MLOps Toolkit Strategically
- Establish Clear Naming Conventions
- Keep the First Model Simple and Get the Infrastructure Right
- Maintain Consistent Processing Functions Across Training and Serving Pipelines
1. Establish Business Objectives and Clear Goals
The first step in your MLOps model lifecycle should always be to scope out the project by identifying what business problem(s) you are aiming to solve. After all, if you don’t know where you’re going, how will you get there?
Most MLOps teams are greatly dispersed, with members working in various departments throughout the organization on model development. By establishing business objectives and goals for each model – for instance, reduce manual review time of video files by at least 50% – you give everyone on your team clear direction and you make it much easier for all team members to stay aligned and on schedule.
Adhering to the MLOps principle of asking, “What do you want from the data?” at the beginning of every model lifecycle also prevents the development of models that don’t serve the organization in the long run. Knowing upfront that you want to identify an object with a specific set of characteristics from video data, for example, saves resources and helps to ensure the MLOps team is always providing real value to the company.
2. Open the Lines of Communication Right Away
As we’ve previously mentioned, implementing and maintaining machine learning operations in the long term requires collaboration between a variety of professionals that are often dispersed through various departments. Typically, this cross-functional team structure involves data scientists, engineers, analysts, operations, and other stakeholders. While they may be divided by official industry silos, they don’t need to be divided in how they operate and communicate day to day.
We recommend opening the lines of communication at the very beginning. Introduce members of the team to each other and make sure that everyone knows each person’s roles and responsibilities. Make it clear that the MLOps team will operate as just that, a team, and give them a space to do so—whether it’s an MLOps-specific slack channel or a specified team room in your company’s intranet.
Fostering communication and collaboration right off the bat reduces friction and opens up bottlenecks, empowering your MLOPs team members to gather insights and iterate new ideas much faster and more efficiently than a multi-departmental team otherwise would.
3. Build for Scale From Day 1
Scalability is incredibly important in machine learning operations. Despite that, it can also be difficult to achieve. According to a KDnuggets poll, 43% of people say they get roadblocked in ML model production and integration. You can avoid that roadblock if you build your infrastructure with the intent to scale from the start.
Ideally, every component of your ML system should be scalable, including hardware, modularity, data sourcing, the ability to automatically spin up new clusters in the cloud when necessary, and build processes that enable scale. Models should be built for production scale from the beginning, not as an afterthought after prototypes are proven out
4. Choose Your MLOps Toolkit Strategically
All MLOps tools are not created equal. When building your MLOps toolkit, it’s important to think strategically to ensure you’re putting yourself on a path towards long-term success. To do this, you’ll need to consider a few different factors such as your business objectives and budget, the MLOps tasks you’ll need to address, the sort of data sets you’ll be working with, the level of experience your team will have, and more.
One option is to build a portfolio of MLOps tools, each with different features to tackle the various stages of the machine learning lifecycle. You’ll need to evaluate vendor tools for data versioning, orchestration, experiments, parameter tuning, model serving, and production monitoring.
MLOps AI platforms such as aiWARE provide models for you to simplify and accelerate the MLOps process to just model training, serving, and monitoring. But if you choose to build your own model, evaluate vendor tools for model building and use aiWARE to onboard those models into a production-ready environment.
5. Establish Clear Naming Conventions
What’s in a name? When we’re talking about MLOps, naming conventions are an element you can’t afford to overlook. This is the foundation to keeping everyone on the same page. There are several different variables to manage in machine learning systems, and ultimately you can decide to use the naming convention that makes the most sense for you. A few things you should consider including are:
- Project name
- Model name
No matter what you choose, you need to ensure everyone understands and uses these conventions consistently, for example across pipeline output variables. This plays a big role in keeping things consistent and avoiding any potential confusion.
6. Keep the First Model Simple and Get the Infrastructure Right
With machine learning operations, the possibilities are endless. While this makes it a very exciting discipline, it also makes it easy to get lost in the complexity and face issues with model validation down the line. This opens your MLOps team up to frustration and failure before they’ve even begun. It’s best to keep the first model simple for a quick win, and build more complexity from there.
Doing this is an MLOps best practice for one main reason: you will run into many more infrastructure issues than you expect. Prioritizing the infrastructure build and using the simple model as a test of sorts will save you many headaches down the road.
Many successful MLOps teams aim for a “neutral” first launch—deprioritizing machine learning gains to avoid getting distracted.
7. Maintain Consistent Processing Functions Across Training and Serving Pipelines
“Training serving skew,” the difference between performance during training and performance during serving, is a phenomenon that is common in deploying production-stage machine learning models. After deploying a model, the MLOps team will notice that the production performance is significantly below the performance seen at the training stage.
This discrepancy between training and production results can be hard to detect and can render a model’s predictions useless–a major reason why most AI projects are scrapped post-production. To avoid training serving skew, you need to ensure that you maintain the exact same processing functions across both the training pipeline and serving data. Additionally, before deployment takes place you should set up the processes necessary to monitor the real-time performance of the model and compare it to expected performance to detect any performance drift and correct it.
Want More Information on MLOps?
On the Veritone blog, we’re digging into some core MLOps topics that may interest you and help you expand your knowledge. If you’re interested in diving deeper, keep an eye on our posts. We’ll publish more in-depth content that covers ModelOps, MLOps tools, and MLOps versus AIOps.
You can also jump into another resource right away in our on-demand webinar: MLOps Done Right: Best Practices to Deploy. Integrate, Scale, Monitor, and Comply.
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