7 MLOps Best Practices: Implementation,Challenges & Tools

It’s becoming increasingly important for teams to build a robust ML infrastructure capable of enabling continuous delivery and integration. This is why machine learning operations (MLOps) play a critical role here – it ensures that ML systems are efficiently implemented. 

But how can teams reap the greatest benefits from MLOps approaches? Here are 7 essential best practices that maximize the value of MLOps.

What is MLOps?

The acronym MLOps stands for Machine Learning Operations. MLOps is a basic component of Machine Learning engineering that focuses on optimizing the process of deploying machine learning models to production, as well as maintaining and monitoring them. MLOps is a collaborative field that includes data scientists, DevOps engineers, and IT.

MLOps is an effective method for developing and improving the quality of machine learning and AI applications. Adopting an MLOps method allows data scientists and machine learning engineers to cooperate and accelerate model development and production by establishing continuous integration and deployment (CI/CD) procedures that include proper ML model monitoring, validation, and governance.

Key Challenges in MLOps

MLOps comes with several challenges:

Challenge	Description
Managing Data Versioning and Lineage	This challenge relates to tracking data changes and preserving lineage to assure consistency, traceability, and dependability throughout the model’s lifespan, as well as simple rollback and auditing
Ensuring Model Reproducibility at Scale	Consistently reproducing model performance across contexts by managing dependencies, settings, and data, is hard – particularly when scaling up for production
Automating Model Deployment and CI/CD Pipelines	It’s essential for MLOps to use automated pipelines to eliminate manual involvement, increase agility, and assure seamless upgrades and production integration
Monitoring and Managing Model Drift	Constantly checking model performance for drift caused by changes in data or environment is another challenge, especially when teams are also correcting or retraining models to preserve accuracy and relevance
Optimizing Infrastructure Costs and Resource Allocation	Balancing performance demands with cost efficiency requires optimizing cloud resources, storage, and compute power for model training, deployment, and serving
Implementing Security, Compliance, and Governance in MLOps	Another key challenge teams must address is enforcing security standards, adhering to legislation, and overseeing governance policies to safeguard sensitive data, assure ethical AI practices and audit model behavior
Handling Experimentation and Collaboration Across Teams	Enabling efficient collaboration is essential and teams can achieve that by monitoring experiments, exchanging findings, and working on cross-functional projects

7 MLOps Best Practices

1. Exploratory Data Analysis (EDA)

Exploratory data analysis (EDA) is an iterative process of exploration, sharing, and preparation of data for the machine learning lifecycle. What enables this process is the creation of reproducible, editable, and shareable datasets, tables, and visualizations.

2. Data Prep and Feature Engineering

The process of data preparation and feature engineering has an iterative nature as well. Teams aggregate and de-duplicate data to produce refined features, which are accessible and shared across data teams by using a feature store.

3. Model Training and Tuning

To train and improve model performance, teams can use popular open-source tools such as scikit-learn and hyperopt. A simpler option is using automated machine learning technologies like AutoML to execute trial runs and generate reviewable and deployable code.

4. Model Review and Governance

Model review and governance include tracking model lineage and versions, as well as managing model artifacts and transitions throughout their existence. Discover, share, and collaborate on ML models using an open-source MLOps platform like MLflow.

5. Model Inference and Serving

Teams need to control the frequency of model updates, inference request timings, and other production-specific parameters in testing and QA. To automate the pre-production pipeline, they can use CI/CD solutions like repositories and orchestrators (which follow DevOps principles).

6. Model Deployment and Monitoring

Another MLOps best practice is automating permissions and cluster creation to make registered models production-ready. It’s key that teams enable the REST API model endpoints.

7. Automated Model Training

Automated model retraining includes setting up alarms and automating corrective actions in the event of model drift caused by disparities between training and inference data.

Strategies for Effective MLOps Implementation

Automate Workflows and Minimize Manual Intervention

Carrying out MLOps processes manually causes human error, production delays, and stakeholder pipeline visibility issues across the machine learning architecture and beyond. When problems occur in production, manually created pipelines are difficult to troubleshoot, increasing MLOps technical debt.

Automation reduces manual work and frees up time, resources, and bandwidth for your MLOps team to tackle other issues.

You can automate various workflows across ML such as:

• Version Control – Automating version control for machine learning centralizes and automates the history of code, data, configurations, models, and pipelines. Your MLOps team can trace errors, revert back changes that didn’t work, and cooperate more transparently and reliably using automatic version control.
• Deployment – Manually deploying models in a complicated business context is wasteful and time-consuming. Automating it reduces mistakes, accelerates model rollout, and cuts model training-to-production time.
• Feature Selection for Model Training – Manual feature selection is laborious and needs subject area expertise. Automating feature selection speeds up machine-learning model training on fewer datasets and simplifies model interpretation. 

Prioritize Experimentation and Tracking with Version Control

By implementing robust version control systems for both data and models, you can easily manage multiple experiments, compare model performance, and track changes over time. 

Machine learning model versioning opens the door to reproducibility by allowing developers to roll back to previous versions, audit decisions, and optimize models iteratively. 

Additionally, it enhances collaboration across teams by providing a structured approach to experiment logging, making it easier to share insights and ensure transparency in the model development process.

Ensure End-to-End Data and Model Lineage

End-to-end data lineage is the trace of a data point’s travel throughout the complete data landscape, from source (where it began) to destination (where it finished), including the transformations it took and the other data and systems it impacted.

In a typical data environment, end-to-end data lineage would start with a source system and go via ETL or ELT procedures, data storage repositories, analytical systems, and eventually analytics and reporting systems. It’s essential to include lineage for both data and models to power your MLOps practice.

Align MLOps with Business and Regulatory Requirements

ML models must be consistent and meet business objectives at scale with a rational, easy-to-follow model management approach.

One way to achieve this is by creating a Model Management framework. By using it, teams can:

• Actively handle issues like regulatory compliance
• Track data, models, code, and model versioning for repeatable models
• Package and distribute models in repeatable configurations for reuse

MLOps Essentials

Data Version Control (lakeFS)

lakeFS helps supercharge MLOps tools. it’s an open-source, scalable data version control system providing a Git-like interface for object storage. lakeFS enhances data quality, supports large-scale datasets, and accelerates MLOps workflows by improving reproducibility, ML experimentation, and collaboration.

Main features:

• Git branching, committing, and merging via any storage service
• Faster development with zero-copy branching facilitates experimentation and collaboration
• Pre-commit and merge hooks ensure clean Write-Audit-Publish operations in lakeFS
• Data issues can be resolved faster with resilient platform reversal capabilities

Orchestration and Workflow Pipelines (Kubeflow)

Kubeflow supports Kubernetes machine learning model deployment by making them portable and scalable. You can use it to prepare data, train models, optimize models, serve forecasts, and enhance production model performance. Install machine learning processes locally, on-premises, or in the cloud.

Main features:

• Centralized dashboard with UI
• Reproducible and efficient machine learning pipelines
• RStudio, JupyterLab, and Visual Studio Code native support
• Neural architecture search, hyperparameter optimization
• Tensorflow, Pytorch, PaddlePaddle, MXNet, XGboost jobs
• Job scheduling
• Ability to isolate several users

Experiment Tracking and Model Metadata Management (MLflow)

MLflow is an open-source tool for organizing machine learning processes. It contains four major components:

• The tracking component allows you to record machine model training sessions (known as runs) and conduct queries using Java, Python, R, and REST APIs.
• The model component defines a standardized unit for packaging and reusing ML models.
• The model registry component allows you to centrally manage models and their lifespan.
• The project component bundles the code used in data science projects, allowing it to be reused and experiments to be replicated.

An experiment is the fundamental unit of MLflow structure. All MLflow runs are part of an experiment. For each experiment, you may examine and compare the outcomes of several runs, as well as readily get metadata artifacts for study by downstream tools. Experiments are managed using an MLflow tracking server hosted on Azure Databricks.

Feature Stores (Feast)

Many data teams deal with issues such as data silos, data duplication, and lack of version control. This is where a feature store like Fest comes in. 

Feast emphasizes using current infrastructure, guaranteeing data integrity, and limiting leakage. This open-source nature solution is highly scalable and surrounded by an active community.

Feast allows you to:

• Share features across teams and projects to avoid duplicating work
• Ensure data consistency across projects
• Separate feature engineering from model development 
• Make the access function available in real time
• Support is integrated with current tools that ML practitioners know (such as Airflow, Dagster, MLFlow, and K8s)

Model Testing (Evidently AI)

Evidently is an open-source tool for analyzing and monitoring ML models. The solution creates interactive reporting on machine learning model performance in production. 

Using Evidently, you can analyze model health and data drift in production to debug the causes of model degradation. The solution also allows teams to analyze and compare models prior to deployment to identify weak points and regions of low performance.

Key features of Evidently include:

• Data drift detection
• Drift analysis for targets and predictions
• Model performance analysis (regression, classification, and probabilistic classification models).
• Identifying regions of low performance

Model Deployment & Serving (TensorFlow Serving)

TensorFlow includes a framework for creating and training ML models, as well as tools for deploying those models in a production setting.

TensorFlow Serving is a library for serving TensorFlow-based machine learning models. It lets users install TensorFlow models in a production environment and deliver them via an HTTP REST API or gRPC interface. 

TensorFlow Serving simplifies the deployment and management of ML models by providing features like model versioning, automated request batching, and support for canary deployments.

TensorFlow Serving enables users to serve their TensorFlow models in a production environment without worrying about the underlying infrastructure or serving specifics.

Applying MLOps Best Practices with lakeFS

MLOps is all about managing models. When managing the model, you manage data aspects such as data quality, model performance, and the data path that led to the model. However, this approach doesn’t protect data. What you get is a logging system that doesn’t enable the branch mechanisms or commit or diff between data versions.

This is where lakeFS can help, serving as the infrastructure layer that empowers MLOps.

lakeFS allows teams to manage their data using Git-like methods (commits, merges, and so on) while handling billions of files and petabytes of data. 

One of the most critical lakeFS functionalities is environment isolation. Using lakeFS, many data practitioners may work on the same data, establishing a separate branch for each experiment. Data can be labeled to reflect specific experiments, allowing them to be replicated using the same tag.

When the update works for you, you may push it or merge it back into the main branch, making it available to consumers. Alternatively, like with S3, you may undo changes instantaneously without having to go through each file individually. You can reverse the change and revert to the previous good condition.

lakeFS ensures your data will always be controlled, reproducible, experiment and production-ready.

Conclusion

MLOps comes with a number of challenges that teams can address with the help of the best practices and tools we listed above.