Despite its vast success in a variety of industries, measuring machine learning within an organization is still a major challenge. These objectives and key results (OKRs) tracked by teams and stakeholders must cover variety of factors, including business cases, vendor requirements, and the processing of structured, unstructured, and semi-structured data. The combination of these should always orient your team toward machine learning operations.
The good news? There are plenty of meaningful indicators that will show if you’re on the right track or not with your ML models—even when they are just getting started. The key is knowing what to look for. Find out if you’re heading in the right direction to ensure success.
In this article, we will cover:
· Standardizing Deployment and Monitoring Policies
· Optimizing Your Resources
· Monitoring Real-World Practices
· Proving Reproducibility
· Utilizing Short Iterative Cycles
Standardizing Deployment and Monitoring Policies
The point of machine learning is to improve business operations, productivity, and profitability with repeatable processes that ensure your ML models can run successfully within a given period and at scale every time.
MLOps provides a framework that allows this to happen without your team needing to put in a lot of work every time. Standardization is the key to having this work across the board for all your learning models. The most simple and successful framework to implement is CI/CD. This stands for continuous integration, continuous delivery, and continuous deployment.
CI/CD is a best practice for machine learning and devops teams because it automates the integration and delivery process. This frees up more time for your employees to improve code quality, create better software security, and innovate within the business itself instead of focusing on the iterative parts of the MLOps process.
At its core, standardization of MLOps and other policies allows machines to work more efficiently for you and your business.
On top of implementing the CI/CD framework, you can further improve success by reinforcing the standardization of these policies with your own processes. By utilizing an automated framework, you’ll also have a much easier time pinpointing what is working as well as what might need more attention.
Optimizing Your Resources
With a field as complex as machine learning, there are endless avenues that you can venture down in terms of the resources and data pipeline tools you use to scale with your business. One of the best ways to improve productivity and reduce storage and computer expenditure is by implementing microservices and datum-aware versioning into your every-day applications.
With data at the core of your MLOps pipeline, you can parallelize your operations to speed up processing and easily scale your business. Otherwise, you are left with a single framework that won’t scale without you spending copious amounts of time and money. Investing in a data-centric framework eliminates that issue.
Monitoring Real-World Connections
Using ML models for business operations is becoming a more common practice so, naturally, companies are searching for better methods to scale and improve operations over time.
While the success of MLOps needs to grow and be tracked over a longer period of time, you can still observe the value of these models early on. That way, you can have peace of mind about making such a large investment that is backed with real data.
The best way to do this is to test it in real-world situations. This means investing in pilot stages, using ML models in your day-to-day business initiatives, implementing practical MLOps tools that will accelerate the rate at which your models work, and building the models around a single ML program that is reproducible and scalable.
Proving Reproducibility
This brings us to the next crucial piece of the puzzle, which is proving that your ML program is indeed reproducible. Otherwise, your team won’t trust it, which makes it all the more likely to fail. The best way to ensure this doesn’t happen is to set realistic goals for your model and be fully transparent with your team about it.
If people don’t have faith in your ML models, your chance for success diminishes greatly. Once you have set a goal for your project, be sure to invest in the right MLOps tools and automation practices that ensures its success.
While standardization allows these models to operate on their own, it’s also important to track their success during early phases to ensure nothing is going wrong within the code. All this together will ensure your ML models can be reproduced across a variety of functions.
Utilizing Short Iterative Cycles
Rather than creating complex, drawn-out models, practice using shorter, accelerated cycles. Not only will this allow your teams to work faster and serve more cross-functional goals, but it also provides you with valuable data in less time, so you can be sure your ML models are working at their peak performance.
Moreover, shorter cycles leave less room for errors to occur in the first place, which will make them more successful and productive in the long run.
Monitor and Improve Your ML Models with Pachyderm
Practical MLOps frameworks consist of five steps that must be completed to ensure your models’ success. These steps include understanding your KPIS, collecting data, developing an ML model, deploying it, and then monitoring it over time.
By understanding how to measure the success of your MLOps, you’ll be able to track which machine learning models are working and which ones need to be altered.
At Pachyderm, we offer top-of-the-line MLOps tools and solutions to ensure the ongoing success of your business operations over time. Learn more about how to further improve your machine learning modes with our whitepaper and get in touch with our team to complete the machine learning loop.
