Nowadays, data scientists and Machine Learning engineers face many commonly recognized pain points in the process of developing and serving high-performing ML models in operations. Some of the typical challenges are:
The gap between ML development and ML operation is rooted deeply and has been a challenge for a while. In the real-world business environment, only a small fraction of the ML system is composed of the ML code. As shown in the following chart, many required surrounding elements are vast and complex. Thus, to make an ML system function efficiently and productively, data engineers have to deal with other elements, such as data validation, feature engineering, model analysis, metadata management, etc.
Thus, the MLOps concept was developed to adapt to the industrial long-term and fast-growing needs of serving a highly-performing ML system. Applying DevOps principles to ML systems (MLOps) will hinge the ML engineering stages and help develop and operate complex systems more effectively. Practicing MLOps aims to advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment, and infrastructure management.
As MLOps is getting more and more popular within the industry, an ever-growing set of standards is built to better unify ML system development (Dev) and ML system operation (Ops). According to Google, there are 3 levels of automation to define the ML process maturity.
Many teams at the fundamental level of maturity, or level 0, have data scientists and machine learning researchers who can construct cutting-edge models, but their method for building and deploying ML models is essentially manual.
MLOps level 1 aims to perform continuous model training by automating the ML pipeline. This allows you to give the model prediction service continuously. To automate the process of retraining models in production using new data, you must include automated data and model validation processes in the pipeline, as well as pipeline triggers and metadata management.
MLOps level 2 can be determined by Continuous Integration/Continuous Deployment, or CI/CD, pipeline automation because a robust automated CI/CD system is required for a rapid and reliable update of production pipelines. This automated CI/CD solution enables ML practitioners to quickly explore novel ideas in the areas of feature engineering, model design, and hyperparameters.
Notably, you might already be familiar with the CI/CO in DevOps, but in MLOps, the concepts are slightly different.
MLOps seeks to provide an end-to-end machine learning development process for designing, building, and managing reproducible, testable, and evolvable ML-powered software via Machine Learning Model Operationalization Management. Following are the MLOps components that GCP products can unify:
The ML system components must run at scale on a stable and dependable platform in a production setting. The chart below detailedly introduces how each stage of the ML pipeline is executed utilizing a managed service on Google Cloud, ensuring large-scale agility, reliability, and reproductivity.
Step |
Google Cloud service |
Data extraction and validation |
|
Data transformation |
|
Model training and tuning |
|
Model evaluation and validation |
|
Model serving for predictions |
|
Model Storage |
MLOps provides the following benefits by combining ML engineering with ML system development:
Overall, the MLOps platform provides a collaborative environment for data scientists and software engineers that enables iterative data exploration, streamlines collaborative capabilities for model experiment tracking, as well as optimizes controlled model transitioning, deployment, and monitoring. In conclusion, with the MLOps strategies, the machine learning lifecycle's operational and synchronization parts will be automated and integrated seamlessly. MLOps-enabled model creation and deployment implies faster time to market and lower operating expenses and will enable managers and developers to make more agile and strategic decisions. MLOps is a hot topic that’s continuously and rapidly developing, with new tools and processes adapting all the time. If you get on the MLOps train now, you will get a huge competitive advantage.
References:
https://www.cloudskillsboost.google/course_sessions/5831067/video/392201
https://cloud.google.com/architecture/mlops-intelligent-products-essentials
https://www.linkedin.com/pulse/team-building-collaboration-mlops-strategies-creating-monika-obrocka/