Machine learning (ML) is a well-known concept, but very few have an answer to what is MLOps. The concept of MLOps rose due to the increasing difficulty in productionizing machine learning. The machine learning lifecycle is complex and requires collaboration between data science, ML engineering and data engineering.
Since the entire process of ML was becoming increasingly complex for organizations, machine learning operations came to the rescue. Here is what it is and why we need it.
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MLOps or Machine Learning Operations, is a core function within ML engineering that focuses on streamlining the deployment, maintenance and monitoring of machine learning models in production environments. It plays a critical role in ensuring that ML models move efficiently from development to real-world applications. As a collaborative discipline, MLOps typically involves coordination between DevOps engineers, IT professionals and data scientists, all working together to ensure models are reliable, scalable and continuously improving over time.
An imperative question to answer here revolves around the problems solved by MLOps in organizations.
Some issues solved by this practice are-
Bottlenecks are common with non-intuitive, complicated algorithms. With MLOps, collaboration between data and operations teams becomes more feasible, which helps in reducing the severity and frequency of such issues.
MLOps employs a framework to manage the ML lifecycle in a more efficient and effective manner. A more iterative and structured workflow is constructed to match technical prowess with business expertise.
Machine learning is still in its blooming phase. There are still plenty of requirement changes and updates being released by the regulatory body. Machine Learning Operations stays updated and takes ownership of all these aspects.
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The lifecycle of MLOps is composed of nine major stages.
The first step in this lifecycle is to clearly define the problem that needs to be solved. This problem should be one that can be fixed through this philosophy.
The data collection phase is where the data is employed to train models. It should be based on appropriate sources, such as user behavior.
Gigantic amounts of data are needed to effectively train the models. Data lakes/ warehouses are generally used for storage. The data is then clear as a stream or in batches, as per the requirements.
Metrics must be clearly defined to ensure that the model quality is appropriate. These metrics aid in determining whether the problem ruled in the first step is successfully solved or not.
Hypotheses are developed about the techniques to be used.
This is where it is determined what features need to be used as inputs for the models.
The best fit approach is chosen to move ahead with and implemented in this phase.
Models are now integrated into the product and then deployed within a cloud system such as AWS. New hooks or services might also be built in this step.
After being deployed, the models must be monitored closely to rule out issues like model bias or data drift.
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Machine Learning Operations is becoming increasingly important for organizations as more of these enterprises begin using AI capabilities. There are plenty of reasons why it has become the next important skill to learn.
No, it's not a framework but a set of practices and principles for streamlining and automating the deployment and management of machine learning models in production.
It's the process of managing and automating the lifecycle of machine learning models, from development to deployment and monitoring, keeping efficiency and reliability in mind.
The types of machine learning operations include data preprocessing, model training, deployment, monitoring, and maintenance.
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