What is an MLOps Framework and 8 Steps to Build Your Own

AI is the buzzword of this decade. From predictive analysis to personalized recommendations, it revolutionizes how we interact with technology. As a result, integrating AI and ML into applications and software systems is becoming necessary to ensure the fast delivery of intelligent software.

AI adoption has soared in recent years, and 72% of companies already use AI in their day-to-day business operations. If you are a developer or a startup planning to build a machine-learning model to improve business processes, you may encounter some challenges. Deploying, managing, and maintaining this model in the real-world production environment will surely be a headache, which is where MLOps frameworks come into play.

What is an MLOps framework?

MLOps, or Machine Learning Operations, is a set of practices that helps you streamline the deployment and maintenance of machine learning models in the production environment. It brings together data scientists, data engineers, and operations teams to manage the machine learning lifecycle, which spans all the processes from data preparation and model training to deployment and monitoring.

There are a few reasons why startups, especially those that are cloud-native or looking to develop cloud-native technologies, should invest in an MLOps framework:

It helps you develop reliable models by training often and thoroughly before deployment to reduce the risk of errors.

Automated workflows and scalable infrastructure help handle large datasets and complex models.

An MLOps framework helps to cut back on the time to market.

Improves collaboration between data scientists, operations teams, and other engineers to build and release quality models.

Helps optimize and refine the model continuously by monitoring real-world performance data.

How DevOps Differs from MLOps

MLOps is a combination of machine learning and DevOps. Although they are similar in many ways, their focus areas differ significantly. DevOps is more concerned with software development and operations, emphasizing continuous integration and delivery (CI/ CD). On the other hand, MLOps heavily concentrates on the lifecycle of machine learning models, which is slightly different from regular software.

What are the key components of an MLOps framework?

Data management and versioning

Effective data management ensures that clean, safely procured, carefully prepared, and well-labeled data is readily available for your models. Versioning is crucial to tracking changes to models, code, and datasets over time, ensuring reproducibility.

Model training and development

Model training and development involves selecting the correct algorithms and tuning parameters to use scalable infrastructure to accommodate large datasets. It focuses on creating and refining models that meet the task’s requirements.

CI/CD pipelines

A reliable CI/CD pipeline automates model testing, deployment, and updating, optimizing resource usage and ensuring your models are always up-to-date and ready for production.

Monitoring and governance

Once your models are in production, you need to continuously monitor for potential errors and model drift. This strategy helps detect anomalies early, ensure compliance, and improve models based on performance metrics.

Challenges of MLOps framework implementation

Apart from the typical challenges encountered in a DevOps setting, implementing a MLOps framework has its own unique hurdles.

Data quality and management issue: Data inconsistencies, such as mismatches in values and formats from different sources, and errors during data preparation can result in inaccurate models.

Complexity and scalability: Building and maintaining infrastructure capable of handling the growing complexity of the models and pipelines is a significant challenge. It often requires investment in powerful computational resources, efficient data storage solutions, and specialized knowledge.

Skill gap: Training sessions and collaborative efforts are necessary to align data scientists and researchers with operations teams. This approach ensures that those working on data and models and those handling deployments are on the same page to make MLOps work.

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8 steps to build your own MLOps framework

Building an MLOps framework from scratch may seem daunting, but here is a sequence of manageable steps to simplify the process.

1. Define your goals and requirements

First, you must understand what you are trying to achieve with the MLOps framework:

Identify how things are currently accomplished within your organization.

Define your business objectives, the problems you are addressing with machine learning, and the success metrics.

This foundation is crucial for decision-making throughout the process to come.

Best practices:

Actively engage stakeholders to gather diverse perspectives on requirements.

Categorize requirements into must-have, should-have, could-have, and won’t-have (MoSCoW method).

Prioritize requirements and establish clear, measurable objectives for tracking progress.

Document the goals, objectives, and success metrics clearly.

2. Design the architecture and workflow

Design a clear picture of the MLOps framework architecture, defining how data will flow through the system from ingestion to deployment and monitoring. Include all the processes involved, especially the data pipelines and model training and monitoring strategies.

Best practices:

Utilize visual diagrams using Draw.io or Lucidchart where necessary to map the workflows.

Design with flexibility in your mind to accommodate future changes.

Design data pipelines that are scalable, reliable, and maintainable.

Set up comprehensive monitoring.

3. Choose and integrate your tools and technologies

Selecting the correct tool is a crucial step in building an MLOps framework. You will need specialized tools for data processing, model training, orchestration, CI/CD security, and monitoring. You need to consider aspects like buying or building the tool for better customization, the relevance of the tools and technologies to your use case, and your team’s experience and expertise.

Best practices:

Use benchmarking to compare the performance and suitability of different tools.

Make use of trial periods to test features.

Provide training sessions for the team on new tools.

4. Establish data management practices

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Data management includes setting up data collection, cleaning, labeling, and versioning standards. It’s essential to understand:

The level of data governance that fits your use case.

How to handle data.

What to do with corrupted data.

Legal and ethical concerns.

Best practices:

Automate data collection and preprocessing pipelines to have consistency. You can use Apache Airflow for workflow orchestration.

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def collect_data():
# Code to collect data
pass

def preprocess_data():
# Code to preprocess data
pass

default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2024, 1, 1),
'retries': 1,
}

dag = DAG('data_pipeline', default_args=default_args, schedule_interval='@daily')

t1 = PythonOperator(task_id='collect_data', python_callable=collect_data, dag=dag)
t2 = PythonOperator(task_id='preprocess_data', python_callable=preprocess_data, dag=dag)

t1 >> t2

Perform data validation checks. You can use Great Expectations for data validation.

import great_expectations as ge

df = ge.read_csv('data.csv')

# Define expectations
df.expect_column_values_to_not_be_null('column_name')
df.expect_column_values_to_be_in_set('column_name', ['value1', 'value2'])

# Validate data
validation_results = df.validate()

print(validation_results)

Use data versioning to keep track of different versions of your datasets.

Implement data governance policies to ensure compliance with legal and ethical standards.

5. Automate the processes with CI/CD

Automation is at the heart of MLOps. Implement the CI/CD pipeline to automate the entire process of model testing and deployment. You can also use it to automate repetitive tasks such as data validation, model performance tracking, and version control for the data and models.

Here is a comprehensive CI/CD pipeline configuration for GitLab CI. It covers below steps:

Data validation

Model testing

Version controlling

Deployment

stages:
- validate
- version
- test
- deploy

# Stage 1: Validate Data
validate-data:
stage: validate
script:
- echo "Running data validation..."
- great_expectations checkpoint run my_checkpoint
only:
- master

# Stage 2: Version Control for Data
version-data:
stage: version
script:
- echo "Versioning data..."
- dvc add data/dataset.csv
- git add data/dataset.csv.dvc .gitignore
- git commit -m "Version data"
only:
- master

# Stage 3: Test Model
test-model:
stage: test
script:
- echo "Running model tests..."
- pytest tests/
only:
- master

# Stage 4: Deploy Model
deploy-model:
stage: deploy
script:
- echo "Deploying model..."
- kubectl apply -f deployment.yaml
only:
- master

6. Time for monitoring and observability

With the automated pipelines implemented and your models deployed, continuous monitoring is essential to ensure they perform well and do not encounter anomalies. You can set up monitoring solutions to track KPIs and trigger alerts in response to faults and inconsistencies.

Best practices:

Set up dashboards using a tool like Grafana to visualize performance in real-time.

Implement logging and tracing using a tool like ELK Stack to improve troubleshooting.

Configure alerts using Prometheus Alertmanager to notify your team of any real-time issues.

groups:
- name: example
rules:
- alert: HighErrorRate
expr: job:request_latency_seconds:mean5m{job="myjob"} > 0.5
for: 10m
labels:
severity: page
annotations:
summary: "High request latency"
description: "Request latency is > 0.5s (current value: {{ $value }})"

7. Collaboration across teams

MLOps will not function properly if teams work independently without coordination. Therefore, it’s essential to use communication and collaboration tools and platforms to ensure everyone involved is updated on what’s happening with the models and pipelines.

Best practices:

Conduct regular cross-functional meetings to discuss progress and concerns.

Ensure all code, data, and model changes are tracked and managed through a version control system.

Maintain documentation and knowledge bases to share information across teams.

8. Ensure security and compliance

Like any technology, your MLOps framework needs tight security and compliance strategies and standards. Ensure that data and models are protected from unauthorized access, breaches, and compliance violations against regulations like GDPR.

Best practices:

Encrypt data at rest and in transit to safeguard sensitive information. You can use AWS KMS (Key Management Service) or Azure Key Vault for this.

Configure access control and authentication mechanisms using AWS IAM (Identity and Access Management) or RBAC (Role-Based Access Control) in Kubernetes.

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: default
name: mlops-role
rules:
- apiGroups: [""]
resources: ["pods", "services"]
verbs: ["get", "list", "watch", "create", "update", "patch", "delete"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: mlops-role-binding
namespace: default
subjects:
- kind: User
name: "mlops-user"
apiGroup: rbac.authorization.k8s.io
roleRef:
kind: Role
name: mlops-role
apiGroup: rbac.authorization.k8s.io

Conduct regular risk assessments, security audits, and compliance checks. You can use AWS CloudTrail or Azure Security Center.

Ensure compliance with legal and ethical standards.

Harness the power of MLOps with Control Plane

Building your own MLOps framework involves careful planning, selecting the right tools, automating processes, and ensuring robust security and compliance. Following the above steps, you can create a scalable, efficient, and secure MLOps pipeline that enhances your machine learning workflows and business outcomes.

Furthermore, you can use advanced tools like Control Plane to improve your infrastructure management. Although it is not directly an MLOps platform, Control Plane provides highly relevant and beneficial features in an MLOps context. For example, Kubernetes is a popular choice for orchestrating and scaling machine learning workloads. Control Plane’s expertise in Kubernetes can help MLOps teams effectively deploy, manage, and scale their ML models and pipelines in a cloud-native environment. Book a Demo today to discover how Control Plane supports your journey towards cloud-native maturity.