Many enterprises are cognizant of the transformative benefits of AI/ML and, therefore, have been implementing the insights gained from this technology to improve their output. However, organizations face several challenges when it comes to the scaling of their AI/ML models. Such problems usually arise due to the lack of a standardized AI/ML workflow within the enterprise. That is where MLOps comes into the picture. According to a study , 98% of the leaders in the IT industry believe that MLOps will give their company a competitive edge and increase profitability. In this article, we will understand why an enterprise should leverage MLOps to scale AI/ML models.

1. Key to a Smarter Future: Leverage MLOps to
scale AI/ML
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2. Many enterprises are cognizant of the transformative benefits of
AI/ML and, therefore, have been implementing the insights
gained from this technology to improve their output. However,
organizations face several challenges when it comes to the
scaling of their AI/ML models. Such problems usually arise due to
the lack of a standardized AI/ML workflow within the enterprise.
That is where MLOps comes into the picture. According to a
study
, 98% of the leaders in the IT industry believe that MLOps will give
their company a competitive edge and increase profitability. In
this article, we will understand why an enterprise should
leverage MLOps to scale AI/ML models.
What is MLOps?
MLOps or Machine Learning Operations provides organizations
with a standardized end-to-end solution to design, build,
manage, and test Al/ML models. Adopting this technology in
your organization can increase the product's quality, simplify
the management throughout the AI/ML lifecycle, and
automate the deployment of AI/ML models. Eventually, as the
technology upgrades, MLOps will be able to automate the
development of AI/ML models and eliminate any kind of human
intervention in the process.

3. AI/ML System Lifecycle
To get a better understanding of MLOps, let us understand a
little bit about AI/ML system lifecycle. There are several moving
parts involved in the AI/ML lifecycles, such as data ingestion,
data preparations, model training, model tuning, model
deployment, model monitoring, and much more.
Here is a list of teams and their responsibilities that are involved
throughout the AI/ML system lifecycle:
● Business Development or Product team: This team is tasked
with defining business development goals and KPIs.
● Data Engineering Team: The data engineering team takes
care of data acquisition and preparation relevant for the AI/ML
models.
● Data Science Team: Members of this team are responsible for
architecting AI/ML solutions and developing models.
● IT or DevOps Team: Deployments, monitoring, and testing are
handled by the DevOps team.
MLOps streamlines the whole AI/ML lifecycle and enables the
teams to work in sync at different stages of the lifecycle

4. Difference Between MLOps & DevOps
As the concept of MLOps is derived from DevOps, there are
several fundamental similarities between them. Both practices
enable IT teams to develop, test, deploy, and scale
applications in an efficient manner. However, there are a
couple of differences between MLOps and DevOps.
Here are some essential points that differentiate MLOps &
DevOps:
● Development: In DevOps, development refers to the process
of the creation of code for an application. On the other hand,
in MLOps, development means the codes that build and train
an ML/AL model.
● Monitoring: MLOps focuses on monitoring the AI/ML model
and eliminating data drift and model accuracy defects. In
DevOps, monitoring refers to overseeing the entire software
development lifecycle.
● Team Members: In DevOps, the team required to build
applications would mostly consist of software engineers. While in
MLOps or any AI/ML project, the team comprises software
engineers, data scientists, and AI/ML researchers.

5. How Can MLOps Add Value While Scaling AI/ML Models?
Here is how MLOps can help your business:
● Enhanced Productivity
The majority of executives working in the IT industry can agree that
implementing AI solutions from an idea stage can take at least nine
months, which makes it difficult for the organization to keep up with
the dynamic changes in the market.
On the other hand, with MLOps, businesses could take their AI solution
from the idea stage to the working model in just two to twelve weeks.
This would allow the companies to scale AI/ML faster without adding
more members to the team.
● Enhanced Reliability
Enterprises often spend a lot of resources on developing AI/ML
solutions, only to discover that 80% of them don't add value to
business after a period of time. In contrast, many studies have shown
that MLOps reduce the number of shelves by 30%. This is due to the
capabilities of MLOps to integrate constant monitoring and efficiency
tests in the workflow, which makes AI/ML solutions more reliable.
With MLOps, your IT team can automate critical monitoring and
testing tasks, allowing them to detect and resolve issues and leverage
the gained insights across the AI/ML lifecycle to improve the model’s
performance.

6. ● Reduced Risk
Instead of making substantial investments for the governance of
the AI/ML models, many enterprises make the mistake of
undermining the risks their models pose and don’t have proper
procedures lined-up to mitigate them. This can cause significant
operational and financial damage to the company in the
event that any AI/ML models malfunction.
MLOps incorporates exhaustive risk-mitigation measures into the
lifecycle of AI/ML models. It offers reusable components that
reduce the possibility of errors. For example, a financial-services
company utilized MLOps practices to provide their IT team with
an auditable trail. This allowed them to pinpoint models that
were at risk and eluded severe damage to their models.
● Better Talent Retention
Retaining talent in the tech team of the organization is crucial
when it comes to scaling AI/ML efficiently. It is because
onboarding a new member and training them about their role
in the team can be time-consuming and might also cause
delays in the process. Also, without MLOps, top talent can
quickly become frustrated with monotonous tasks like data
cleansing and might feel alienated from their work.

7. That’s why MLOps can play a huge part in attracting and
retaining critical talent. Most of the technical talent is excited
about working with cutting-edge technology and tools like
MLOps that allow them to focus on solving complex analytics
problems and see the impact of their work in production.
Conclusion
MLOps can offer businesses a competitive edge by
accelerating the development of reliable AI/ML systems and
letting them scale with ease. It can also automate the
operational and synchronization aspects of the AI/ML lifecycle.
However, handling and supervising all the MLOps practices can
sometimes get overwhelming. That’s why it is advisable to
collaborate with a firm that can assist you in implementing
MLOps.
Mindfire Solution is an IT service provider that has successfully
implemented AI/ML algorithms for organizations around the
world at reduced development costs and timelines. With over
two decades of industry experience, Mindfire Solutions have
worked out best practices that add value to your business. Visit
Mindfire Solutions
to learn more about us.

8. Thanks You
Content Source: Quora
Contented by : Mindfire Solutions