6 Things You Need To Know About Machine Learning Operations

Business technology is constantly changing and advancing. One of the latest advancements is machine learning operations. Here are six things you need to know about machine learning operations.

1. What It Is

Machine learning operations, or MLOps, is a kind of software operation that merges traditional DevOps with artificial intelligence to improve efficiency in project development progress and lifecycles. It can be used by any department, including information technology, data science and product teams, to support continuous product development, delivery and integration.

2. Best Practices

To maximize the benefits of your MLOps, there are some best practices you should follow. Make sure the system is centralized so you can more easily and quickly keep track of data. Develop a data preparation and exchange pipeline that can be easily replicated or adjusted depending on individual teams’ needs. You should also develop solid metrics for production, tracking and development before you implement your MLOps system. This way, even if different teams have some differences in their workflows, you’ll be able to measure those workflows and the end results of production against an organizational standard to ensure quality. Because of this, your employees will be better able to find and correct errors before project completion and it will be easier to train people to use the MLOps system.

3. Scalability

One of the key advantages of MLOps is improved scalability. The increased use of automation and machine learning allows your organization to project scalability models and gather better data to inform your organization’s scalability procedures. AI itself also enables you to scale up model and product development at a fraction of the resources you would normally spend on it. You should look for MLOps options that can monitor your operations and scale up or down automatically.

4. Important Platform Features

There are different MLOps platforms available for different organizational needs and processes. The majority of MLOps systems are based on five important areas: the aforementioned scalability, interoperability, usability, reproducibility and simplicity. Your platform should make scaling up or down as needed simple and straightforward. You should also look for platforms that will integrate with your other systems and software operations as seamlessly as possible. Alternatively, if you’re looking to cut down on the number of software systems your organization utilizes, an MLOps platform with the ability to replace some of those systems would be ideal. Your platform needs to ensure reproducibility, meaning it needs to enable those using it to version various models and reproduce them accurately in order to catch, study and fix bugs. Usability and simplicity go hand-in-hand. Not everyone who uses MLOps is going to be an IT professional or a software engineer. Therefore, your platform needs to be user friendly for anyone who will be working with it, from the data scientist to the system analyst.

5. Model Development

One of the main uses of MLOps is developing data models. You can deploy and funnel models throughout your workflow and monitor them as they move through the pipeline. This ensures a more accurate and complete study of the data model and any flaws or unexpected deviations that may occur in the model. Then, your developers and data scientists can work on correcting errors before the model is sent to the production environment.

6. Automation

As an aspect of machine learning, MLOps is an important automation tool. You can use MLOps to automate testing, deployment, monitoring and transfers between departments or sections of a workflow. As the program learns more over time, you may be able to automate certain processes within a workflow as well, such as fixing simple or common errors or compiling reports on processes, data and errors that an analyst can then review. While data scientists and developers still need to manually test certain models and fix more complicated errors, automation can greatly help streamline workflows.

MLOps can take some getting used to, but its ability to improve efficiency, scalability and cross-organizational standards makes it an invaluable tool for many businesses’ model development and production workflows.