When development and operation teams use specific machine learning models or patterns, then we are actually utilizing a new model of operations known as MLOps, or machine learning operations.
As big data is becoming more than ever vital to organizational success, ML engineers need to find ways to automate aspects of model validation, choosing valuable data inputs, processing datasets, and other data science tasks.
MLOps is a relatively new concept that has become increasingly important as machine learning, algorithms, and artificial intelligence (AI) become critical aspects of developing complex and big applications and computer systems.
MLOps promises to evolve to meet those and other needs.
Collection of procedures known as MLOps endeavors to install and maintain machine learning models in production in a reliable and effective manner.
In the following sections, we'll describe the essential aspects of machine learning operations (MLOps) and how they can benefit everyone, from data scientists who want to optimize big data mining to company stakeholders who want to streamline workflows.
We'll also provide some real-world use cases to help you understand MLOps can improve your:
If you don't feel up-to-date on the latest trends in artificial intelligence and machine learning, catch up by reading reading our earlier blog post.
As we said earlier, MLOps uses concepts in DevOps to make machine learning models more reliable and efficient. For example, MLOps seeks to provide continuous integration and continuous delivery throughout an application's development lifecycle.
Reaching these goals means that data engineers and data engineering teams must review various machine learning models and monitor real-time data.
Once software engineering teams find ML models that perform well, they can automate many of the steps that contribute to a product's ongoing success.
With that said, we recognize that this is a rapidly evolving area of technology. We encourage you to reach out and learn more about testing model performance, comparing model versions, and retraining algorithms.
We'll give you advice based on the latest information, but we expect to see numerous exciting future advancements.
We use MLOps because human minds cannot comprehend or process massive amounts of data. Model development has become so complex that data scientists must rely on trained algorithms to automate processes and make informed decisions.
You can think of MLOps as a group of tools that do work humans can't accomplish. The tools cannot work without human assistance, though.
After periods of ML development, the services can operate with relatively little oversight. As long as you monitor them and make adjustments throughout the machine learning lifecycle, the tools should serve teams for long periods.
Some specific reasons we use MLOps include:
MLOps initiatives will find new problems to solve as the technology advances. It's exciting to think that this young technology has already accomplished so much.
Currently, experts recognize three levels of ML software:
Each level contains several sub-levels that contribute to a project's goals.
ML models can only work well when they have access to high-quality, meaningful data. The importance of quality data means that teams need to spend a lot of time creating and testing their training data.
Creating and testing often involve:
Machine learning pipelines determine how the ML workflow will function and whether model training reaches its intended goals. Typical tasks of building machine learning pipelines include:
Model engineering might require an iterative approach with several rounds of testing before teams find a successful ML pipeline for the production environment.
Before finalizing an algorithm, teams need to take some final steps that usually include:
You might already use machine learning for benefits like:
MLOps only adds to these advantages. Successful machine learning projects based on MLOps principles can lead to:
You already collect a lot of information when users interact with your products. Don't waste time, storage space, and effort. Instead, you can deploy MLOps to find novel, insightful uses for your data.
Obviously, you can use MLOps in numerous ways. The following use case describes how a rideshare company might use MLOps to predict demand, lower abandoned requests, and set dynamic prices.
RideShareCo wants to streamline its processes to become more competitive while increasing revenues. The company decided to use machine learning to review historical data and detect patterns.
RideShareCo knows that it can't afford to hire a new team of data scientists to oversee these operations, though.
It also knows that data can evolve rapidly, making historical data less valuable over time. The company contracted with MLOps experts to discover ways they could use this new modeling approach.
Over time, RideShareCo and its partner underwent several iterations of its algorithms. After intensive testing, they created tools that could automatically:
Now, RideShareCo has someone review the models to ensure they work correctly. The other features happen automatically and become increasingly accurate as they process more data.
Are you interested in exploring the full options of machine learning operations and how they can benefit your organization? We're excited to help!
Reach out so we can assist you find an ML platform, develop a unique MLOps process, or simply discuss opportunities for success. We know that new concepts can seem daunting at first. We're here to make the adoption process as easy as possible!