Why the Push for MLOps?Developing machine learning products remains incredibly challenging due to the siloed, slow nature of internal ML processes. Here’s a brief rundown of the internal problems that hold organizations back from building ML:
- There’s very little automation of internal processes.
- Data scientists and operations teams operate in silos despite the need for collaboration.
- Very few clear pipelines exist.
- Retraining models post-production isn’t happening to the extent models require, leading to poor performance over time.
- Insufficient oversight on regulatory and compliance issues.
- Combines expertise for efficiency: MLOps prompts communication between teams that are traditionally isolated from each other. It combines the business sense of your operations team with the ML-specific knowledge of your data scientists, looping them together for collaborative endeavors. At the same time, each team can focus on what they do best.
- Defines ownership of regulatory processes: Your operations team can oversee regulatory and compliance issues, keeping abreast of any changes in these areas and ensuring the data science team is immediately aware.
- Reduces waste: With the current way ML development is done, there’s a ton of waste in the form of time, money, and opportunity cost. Data scientists spend much of their time focused on repetitive tasks they weren’t hired for, for instance. MLOps leverages the skillset of each team so they’re working on what they do best, it automates pipelines to enable speedy delivery and reproducibility.
- Enables rapid iteration: Through continuous integration, delivery, and pipeline automation, MLOps enables teams to iterate quickly. This means shorter time-to-market for successful deployments as well as more deployments overall.
- Produces more enriching products: By leveraging best practices across the ML lifecycle, MLOps ensures your team is using advanced tools and infrastructure to support deployments. With the additional ability to rapidly integrate, teams have time to experiment more to achieve greater accuracy in their products. As an end result, the user experiences a more enriching, high-quality product.
How to Implement MLOps in Your OrganizationAt a high level, it’s evident how MLOps can create powerful, positive changes in ML development. But how do you practically implement MLOps in your own organization? Let’s simplify by breaking this down by the various parts of the ML lifecycle:
DataThe data portion of a project involves several key pieces:
- Data collection: Whether you source your data in-house, open-source, or from a third-party data provider, it’s important to set up a process where you can continuously collect data, as needed. You’ll not only need a lot of data at the start of the ML development lifecycle, but also for retraining purposes at the end. Having a consistent, reliable source for new data is paramount to success.
- Data cleansing: This involves removing any unwanted or irrelevant data, or cleaning up messy data. In some cases, it may be as simple as converting data into the format you need, such as a CSV file. Some steps of this may be automatable.
- Data annotation: Perhaps the most time-consuming and challenging, but critical, stages of the ML lifecycle is the process of labeling your data. Companies that attempt to take this step in-house are often faced with limited resources and spend far too much time doing so. Other options include hiring contractors to do the work or crowdsourcing, broadening the options to a more diverse set of annotators. Many companies choose to work with external data providers, who can give access to large crowds of annotators, platforms, and tooling for whatever your annotation needs are. Parts of the annotation process can also be automated, depending on your use case and quality needs.
ModelIn the model build stage, you’ll complete the following tasks:
- Model training: Use your labeled data to create a training set and a test set. The training set is used at this step to teach the model what features it needs to learn to recognize. There are many methods of model training in machine learning (from fully supervised, to semi-supervised, to unsupervised, and everything in between). The method you choose will depend on your use case, resources available, and what metrics are important to you. Certain methods can include automation.
- Model testing and validation: The model’s performance should be evaluated against the test set to see if it achieves the desired KPIs. Before deployment, the overall system must be validated to ensure it’s working properly and as intended.
- Model deployment: The model is deployed into production; the system is online.
Post-productionAfter you deploy your model, you’ll need a continuous testing process set in place. This includes:
- Monitoring: Continuously monitor the model against your KPIs. Have alerts and plans in place if the model fails to meet any KPIs.
- Retraining: A critical but often missed step of ML development is retraining. Models must be consistently retrained on new data as their external environment changes.