MLOps refers to the practice of delivering machine-learning models through repeatable and efficient workflows. It consists of a set of practices that focuses on various aspects of the machine-learning lifecycle, from the raw data to serving the model in production.
Despite the routine nature of many of these MLOps tasks, it’s not uncommon for several steps to still be processed manually, incurring massive ongoing maintenance costs. Your organization can benefit tremendously from automating MLOps to achieve efficiency, reliability, and cost-effectiveness at scale. For example, automation could:
- Decrease the time to realize business value and impact from MLOps
- Improve operational excellence, including transparency
- Minimize risk due to human errors
- Accelerate MLOps efforts at scale
- Improve performance
- Increase trust and collaboration among data science and partner teams
However, many companies lack the capabilities, talent, and infrastructure to drive machine-learning models to production reliably and efficiently. This not only means wasted time and resources but also hinders adoption and trust in AI. The sooner that companies of any size, enterprise and startups alike, invest in automating their MLOps processes to expedite delivery of machine-learning models, the sooner they can meet their business goals.
So, let’s talk about six methods for automating MLOps that can help streamline the continuous delivery of machine-learning models to production.
1. Automated Data-driven Pipelines
Delivering a machine-learning model involves numerous steps, from processing the raw data to serving the model to production. Machine-learning pipelines consist of several connected components that can execute automatically in an independent and modular fashion. For instance, different pipelines can focus on data processing, model training, and model deployment.
When it comes to machine learning, data is as or more important than code; pipelines track changes in training data and automatically trigger pipelines for processing new or changed data. Such automated data-driven pipelines kickstart further iterations of data processing and model training based on the new datasets.
Without automated pipelines, the data science team executes these steps manually. This inevitably leads to manual errors, production delays, and lack of visibility of the overall pipeline for relevant stakeholders. Manually built pipelines are harder to troubleshoot when defects creep into production, and so compound technical debt for the MLOps team.
Automating pipelines can significantly reduce manual effort and free up organizational time, resources, and bandwidth so your MLOps team can focus on other challenges.
2. Automated Version Control
In the realm of software engineering, version control refers to the tracking of changes in code, making it easier to monitor, troubleshoot and collaborate among large teams. In machine learning, the need for version control applies to data as well as code.
Version control is especially critical for machine-learning applications in domains like healthcare and finance that have a higher burden of model explainability, data privacy, and compliance. Automating version control for machine learning ensures that the history of the different moving parts—code, data, configurations, models, pipelines—is centrally maintained and fully automated. Through automated version control, your MLOps team has a more efficient ability to trace bugs, roll back changes that didn’t work, and collaborate with greater transparency and reliability.
3. Automated Deployment
Large data science organizations develop multiple models trained on structured and unstructured data for various use cases. Some of these models need to make predictions in real-time at ultra-low latencies while others may be invoked less often or serve as inputs to other models. All these models need to be periodically retrained to improve performance and mitigate challenges due to data drift.
Deploying models manually in such a complex business environment is highly inefficient and time consuming. Manual deployment is cumbersome and can cause serious errors that impacts model serving and the quality of model predictions. This often leads to poor customer experience and customer churn.
Deployment of models to production involves several steps. It starts with choosing multiple environments and services for staging the model, selecting appropriate servers that can handle the production traffic, and pushing the model forward to production. It then includes monitoring model performance and data drift, automating model retraining with more recent data and inputs, and ensuring the reliability of the models through better testing and security.
Automating these steps yields several benefits:
- Reduce errors due to manual operations
- Increase velocity of deployment of original and retrained models
- Reduce time to take a model from training to production
- improve visibility of the entire deployment pipeline for troubleshooting
- Improve tracking and monitoring of model performance after it’s been deployed
- Reduce time taken to improve and retrain an existing model in production
- Improve data and model governance in the face of increasing data compliance laws
- Improve productivity for data scientists and machine learning engineers
- Increase organizational awareness of the impact of deployed machine learning models
- Increase the efficiency of machine learning program management
4. Automated Feature Selection for Model Training
Classical machine-learning models are trained on data with hundreds to thousands of features, ie, key variables in the dataset that are often correlated with model performance. Choosing a set of features that significantly account for the predictive power of the trained models is therefore essential.
Feature selection by hand is cumbersome and requires significant subject matter expertise. Automating feature selection not only helps train the machine-learning model faster on a smaller dataset but also makes the model easier to interpret. Selecting fewer features but with high feature importance is critical in the preparation of training data.
Automated feature selection helps reduce the size of the model to make faster predictions, or to increase the speed of training your machine learning or deep learning model. Feature selection can be automated using either unsupervised learning techniques, like principal component analysis, or supervised methods using statistical tests like f-test, t-test, or chi-squared tests.
5. Automated Data Consistency Checks
A central focus of data-centric AI is the quality of data used to train machine-learning models. Data quality determines the accuracy of the models, which in turn impacts business decision-making. So the underlying data must have minimal errors, inconsistencies, or missing values.
Simplify the challenge of ensuring data quality and consistency by automating unit tests that check data types, expected values, missing cells, column and row names, and counts. Consider extending your automation to the analysis and reporting of the statistical properties of relevant features. If the training dataset consists of a few thousand to millions of samples and hundreds to thousands of features, you can’t manually evaluate every row and column for data consistency. Automated routines that test for different types of data inconsistencies makes it easier to eliminate poor quality data.
6. Automated Script Shortcuts
Processing data and training machine-learning models involves a lot of boilerplate code. Automate the creation of scripts for common tasks to save time and effort while providing better visibility and version control.
Typically, data scientists and machine-learning engineers create their own unique automations and shortcuts, which are seldom shared among the larger team. However, having a centralized repository of script shortcuts reduces the need to improvise, and perhaps even avoids a team member reinventing the wheel.
Save these shortcuts as executable bash scripts for different use cases like downloading data from data lakes or uploading model artifacts in backup folders.
Automate MLOps with Pachyderm
Fortunately, you don’t have to build these MLOps automation features in-house from scratch. Pachyderm is a software platform that integrates with all the major cloud providers to continuously monitor changes in data at the level of individual files. Whenever any existing file is modified or new files are added to a training dataset, Pachyderm triggers events for pipelines and launches a new iteration of data transformation, testing data quality, or model training.
Pachyderm can take care of automated version control and lineage for data as well as [deployment](https://www.pachyderm.com/events/how-to-build-a-robust-ml-workflow-with-pachyderm-and-seldon/. It also enables autoscaling and parallel processing on Kubernetes, orchestrating server resources for deployment at scale.
With a lot of the machine learning lifecycle still handled manually across the industry, consider automating any of the six MLOps tasks we covered here in order to achieve efficiency and reliability at scale:
- Data-driven pipelines
- Version control
- Feature selection for model training
- Data consistency checks
- Script shortcuts
A data science organization’s level of automation across its machine-learning lifecycle indicates its maturity. The velocity of training and delivering new machine-learning models to production increases significantly with that maturity, leading to faster realization of business impact.
Pachyderm, a leading enterprise-grade data science platform, helps make explainable, repeatable, and scalable machine learning systems a reality. Its automated data pipeline and versioning tools can power complex data transformations for machine learning while remaining cost effective.
Article written by Sundeep Teki