Have you been wondering lately what is MLOps? With Machine Learning taking over many different industries, understanding how this technology enables powerful app features through the use of best practices becomes necessary for any business interested in implementing it. That is where Machine Learning Operations, also known as MLOps, plays an important role.
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Because of the impact that Machine Learning can have on your business, understanding Machine Learning Operations is no longer a subject that concerns only developers and data scientists. Business owners and managers need to understand this set of best practices in order to succeed when implementing this form of Artificial Intelligence.
Like any other software development project, Machine Learning models demand a systematic, rigorous, and professional approach. To guarantee a successful Machine Learning project, it is important to have a clearly structured data science and development process. This allows your team to successfully build and deploy models in production through a continuous delivery approach. Failing to do so may compromise your app, consequently damaging your User Experience, and ultimately, your business.
A Machine Learning model also referred to as an ML Model, can make or break your app. In order to guarantee the success of ML models and the subsequent features for your app, it is important to understand what is MLOps (Machine Learning Operations). In this post, we discuss the importance of building Machine Learning models the right way through this approach.
What Is MLOps?
Similar to DevOps, Machine Learning Operations can help data scientists and IT teams improve their processes, gaining productivity and efficiency. Rather than a specific set of tasks, MLOps is a philosophy or set of best practices that help make ML systems work better, reducing mistakes and potential risks.
By focusing on improving the data engineering aspects related to ML models, data scientists and IT professionals can address critical issues in ML pipelines before they become problematic. Using MLOps tools, data scientists can manage the necessary data, algorithms, and cloud infrastructure in such a way that frictions and pain points are reduced throughout ML systems.
In other words, a rigorous data science and development approach like the one found in MLOps best practices can help an ML model collect, store, manage, and process the right data, helping solve, among other things, the ‘garbage in, garbage out’ problem that has become so common to many Machine Learning features. Additionally, it can also help allocate resources more efficiently so that infrastructure use is optimized, helping your company save time, money, and a lot of headaches.
Data is probably the most essential element in any ML model. ML pipelines cover, in a fully automated way, the different stages through which your data goes through in order to be processed by ML models. This includes different activities such as data collection, storage, management, and processing. The steps involved throughout an ML pipeline are portrayed in greater detail in the following image.
Although model training is a crucial step when automating Machine Learning processes, many companies fail to realize that the quality of their data and how it is managed should be a top priority. Robust ML systems operate under this principle, something that system architects consider at the moment of automating the ML pipeline. By doing so, they help guarantee not only the quality of the process but also the outcome. The result is powerful Machine Learning features that strengthen your User Experience.
MLOps (Machine Learning Operations) In a Nutshell
Like many different software development activities, Machine Learning requires a methodical and systematic approach that helps automate repetitive tasks, reducing the risk of errors, and thus improving the way things are done. This is where MLOps plays an important role.
As mentioned, Machine Learning Operations is a set of best practices that combine development and operations. It ultimately seeks to combine the different processes related to development and operations of data into one greater process so that continuous delivery can be achieved via automation.
ML models focus exclusively on model training. Model training is one of the different aspects considered throughout ML pipelines. A common mistake is to focus excessively on it while neglecting other aspects that might be even more critical. Keep in mind at all times that an ML pipeline tends to be only as good as its weakest link. If one of its components fails, the entire process will most likely become sub-optimal.
MLOps considers many different aspects related to data science, such as testing, integration, management, and delivery. Additionally, how different cloud components like the data warehouse are managed also matters. Lastly, the MLOps tools that data scientists and developers use are critical to guarantee the success of any ML model.
The data engineering and development tools used throughout the different aspects of ML models are critical to the success of any Machine Learning feature. They make the work of data scientists and developers easier, and they help reduce the risk of errors.
These are some of the most common tools used when developing Machine Learning features:
- Python (TensorFlow, PyTorch)
- Amazon SageMaker
Final Thoughts on MLOps
Data scientists and developers face great challenges when it comes to building Machine Learning features for apps. They need to build pipelines that are able to automate some complicated and repetitive tasks so that users can make use of the data in the form of app features.