Job Description,
Requirements, Responsibilities, and Career Progression
Machine learning is not restricted to laboratory and
experimental purposes anymore. The use of smart algorithms can be seen in
businesses when it comes to recommendations, prediction, automation, fraud
detection, customer experience, decision-making, etc. However, the process of
developing a machine learning model is just half the job done. And the rest is
related to making the developed model dependable, scalable, safe, and useful in
production environments.
That is why DevOps gains
its importance.
A Machine Learning DevOps Engineer combines the skills and
expertise of software engineering, infrastructure, automation, cloud computing,
and machine learning workflows for transitioning intelligent applications into
dependable production environments. The position allows organizations to
minimize friction in deployments, monitoring of systems, automation of
repetitive tasks, and maintenance of machine learning applications.
What Does a Machine Learning DevOps Engineer Do?
A Machine Learning DevOps Engineer is a technologist who
applies DevOps principles to machine learning systems.
Traditionally, software can be tested and deployed with the
use of rather predictable code. In addition to this, machine learning
introduces extra variables such as training datasets, model iterations,
experiments, dependencies, inference performance, and evolving data patterns.
Given all of this, machine learning projects need more than
conventional development operations.
A Machine Learning DevOps Engineer is supposed to take
care of:
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Model deployment automation
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Cloud infrastructure management
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Development of CI/CD pipelines
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Application and model performance monitoring
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Development environment management
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Machine learning workflow facilitation
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Increase in system stability
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Security measures implementation
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Collaboration between development and operations
teams
The goal is not only the deployment of a particular model.
The goal is to develop a system that will allow updating of models and
applications in a safe manner.
The Importance of DevOps in Machine Learning
The typical DevOps lifecycle gives guidelines for
moving software from planning, development, testing, deployment, and
monitoring. Similar steps are applicable to machine learning projects with some
additional nuances.
One reason why a model works well during development but
poorly in production is that the production data is different from the training
data. It can also happen that a new version of the model behaves differently
despite no changes in the application.
DevOps allows the implementation of automated testing,
deployment, monitoring, and infrastructure management.
It means that DevOps automation is especially useful
in companies having many machine learning models and regular releases.
Main Responsibilities of Machine Learning DevOps
Engineers
Infrastructure Management
A Machine Learning DevOps Engineer can build and manage the
infrastructure for running machine learning applications.
This can include computing capabilities, databases,
containers, networking, storage, authentication services, clouds, etc.
An efficient DevOps infrastructure must be scalable
and maintainable at the same time.
Automated infrastructure allows avoiding differences in
configurations for development, testing, and production.
Creation of Automated Pipelines
One of the key ideas of DevOps is automation.
Machine Learning DevOps Engineer is able to create automated
pipelines that will transfer code and models through the various phases of
development.
The pipeline can be as follows:
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Code validation
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Automated testing
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Model validation
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Packaging
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Deployment
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Monitoring
Using tools for continuous delivery and continuous
deployment tools will help to minimize manual effort during the process of
software and machine learning model release.
Continuous Testing in DevOps
When it comes to machine learning, testing becomes even more
crucial.
In case of traditional software testing, the purpose is to
check if the program complies with specific rules of work. Testing of machine
learning systems may need some extra checks related to model accuracy, data
quality, performance, etc.
Continuous testing in DevOps will help you to find
potential problems early in the development process.
Automated testing can be done not only for the application
but for all its components.
Monitoring and Observability
Deployment of an application does not mark the end of the
journey.
The operation of a production machine learning system
necessitates constant monitoring of technical and modeling issues. DevOps
observability will provide visibility into application performance,
infrastructure health, latencies, resource utilization, and many other factors.
Monitoring tools from the DevOps toolkit may assist
the team in identifying issues before they become visible to the users.
In case of machine learning systems, monitoring can also
include the tracking of model performance and changes in input data.
Key Competences of a Machine Learning DevOps Engineer
Cloud Computing
Contemporary machine learning applications are built around
the need for flexible computing resources. The cloud allows an organization to
build scalable infrastructure.
Knowledge of cloud computing application development and
cloud infrastructure can be very useful for Machine Learning DevOps
Engineers.
They can work with storage services, virtual machines,
containers, managed databases, serverless computing, and any other cloud
services.
Automation and CI/CD
A good understanding of CI and CD tools is critical.
Continuous integration allows developers to combine code and
test it often, whereas continuous delivery or deployment helps to reliably
deliver software to production.
Programming and Scripting
Programming skills may be required for Machine Learning
DevOps Engineers in order to automate processes and connect various systems.
Python is one of them, as it is extensively used in machine
learning and data science environments. Scripting and infrastructure automation
will allow engineers to streamline their operational processes.
Security
Machine learning systems can work with sensitive information
of businesses and their clients. Hence, the aspect of security should be taken
into consideration through all the stages of development.
This has led to the emergence of DevSecOps, which
incorporates security processes into development and operations instead of
considering security as a separate process.
DevOps vs. DevSecOps
The main difference between DevOps and DevSecOps lies in the
place of security in the development process.
While DevOps focuses on collaboration, automation,
integration, deployment, and operations reliability, DevSecOps includes
security aspects at all those stages.
In the case of machine learning systems, it means
ensuring the safety of:
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Training data
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APIs
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Cloud environment
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Credentials of applications
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Endpoints of models
Ø Information
of users
Organizations working with confidential data can take
advantage of introducing security controls into automated development and
deployment processes.
DevOps vs. SRE
Yet another comparison considered by contemporary
engineering teams is site reliability engineer vs DevOps.
DevOps is usually a more general concept and approach
centered around improving cooperation between development and operations with
increased automation and efficiency.
SRE, or Site Reliability Engineering, focuses more on
reliability, availability, performance, and measurable operational goals.
The two methodologies may have a lot of similarities.
The responsibilities of a Machine Learning DevOps Engineer
include automating deployments through DevOps techniques and ensuring system
reliability with the help of SRE principles.
Cloud Migration and Machine Learning
When migrating older applications or infrastructure to the
cloud, an organization may need a cloud migration solution taking into
account not only technological but also business requirements.
Workloads based on machine learning may have specific
requirements in terms of computing power, specialized hardware, scalability,
and fast data processing.
A properly organized cloud migration can increase
flexibility, but migration shouldn’t just mean migrating infrastructure without
proper architecture evaluation.
Evaluate the following when teams decide on cloud
migration:
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Dependencies of applications
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Data storage
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Security needs
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Costs of infrastructure
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Scalability of solutions
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Performance of programs
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Disaster recovery capabilities
For businesses with limited budgets, cloud migration can be
beneficial because of providing access to infrastructure that would have been
very expensive otherwise.
Development of Cloud Applications
Today, intelligent systems are developed as cloud
applications, which are not independent software products anymore.
Cloud-based application development allows apps to
utilize distributed infrastructure, remote services, APIs, databases, and
computing power.
For instance, a business application can include such
components as a web interface, mobile app, a machine learning algorithm, a CRM,
an analysis system, and cloud infrastructure.
In this case, architecture becomes a crucial element.
Web Development and ML Solutions
ML algorithms do not work independently of other systems in
a company's digital infrastructure.
It can be necessary to perform web development of a
site that will serve as a user interface to work with an AI solution.
Thus, for example, a website may serve to connect a customer
to a recommendation system, chatbot, prediction or search engine, personalization
platform, etc.
CRM and Machine Learning
CRM systems can also leverage machine learning to gain an
advantage.
A contemporary CRM
system can leverage intelligent technology to analyze customer
interactions, identify patterns, prioritize leads, personalize communication,
and perform sales forecasting.
If CRM is combined with machine learning models, companies
will be able to turn massive datasets of customer information into insights.
But the proper combination requires reliable APIs, security
when working with data, adequate infrastructure, and the right processes.
The Machine Learning DevOps Lifecycle
The machine learning lifecycle usually consists of several
connected phases.
Planning
The business problem, technical requirements, data
requirements, and the outcome are defined.
Development
Application development, data pipeline creation, and machine
learning model creation are conducted by developers and data scientists.
Testing
Everything that was developed is tested before deployment
into production.
Deployment
Deployment of validated components via automated deployment
or other deployment processes.
Monitoring
Monitoring of production systems in order to detect issues
with infrastructure, application failures, degraded performance, and other
factors that might impact model quality.
Improvement
User feedback, monitoring data, and additional information
can be used for improving future iterations.
This endless cycle is why contemporary DevOps is not only
about deployment.
DevOps Transformation for Modern Businesses
When a company decides to use machine learning, it will find
out that the technological transformation is not about model change alone.
A good DevOps transformation will include a series of
steps, such as workflow redesign, infrastructure automation, improved
cooperation, introduction of monitoring systems, and deployment
standardization.
The idea behind such a transformation is to make sure that
the technology environment allows the teams to implement changes without
reducing security and reliability.
It is especially important when companies are going to link
machine learning to websites, CRM, mobile apps, cloud technologies, and custom
software.
How Businesses Can Prepare for Machine Learning
DevOps
When a company is thinking about adopting machine learning,
it should start with its own needs.
A practical methodology involves:
1. Process identification of potential intelligent
automation opportunities.
2. Current applications and infrastructure assessment.
3. Assessment of data sources and quality.
4. Security and compliance requirements definition.
5. Choice of adequate cloud and development architecture.
6. Implementation of CI/CD and testing practices.
7. Introduction of monitoring and observability practices.
8. Results measurement post-deployment.
A DevOps assessment will be useful for identifying
possible flaws in current infrastructure and development practices prior to any
major transformations taking place.
Conclusion
The position of a Machine Learning DevOps Engineer lies in
the intersection of ML engineering, software development, cloud computing,
automation, and operability.
As intelligent applications are becoming more frequent, just
developing an efficient model is not sufficient anymore. Organizations require
robust and reliable systems that would be capable of deploying models,
monitoring them, securing sensitive data, and adapting to evolving needs.
From DevOps automation and CI/CD to cloud computing,
observability, DevSecOps, website development, and CRM integration – modern
technology environments are becoming more intertwined every day.
Firms that are keen on developing or upgrading their systems
require a technological partner who is well-versed not only in the technology
itself but also in the operations behind it. QTO
DEV can assist firms in exploring new digital options when it comes to
software, web, cloud, and intelligent technologies.
Want to make your technology needs a reality? Contact QTO DEV today.
