We are living in the age of artificial intelligence (AI), a technology that has made its
way into every industry and is advancing at an unprecedented pace.

Epitomizing the innovations in AI is the hyperscale AI model. The number of
parameters, which serves as an indicator of the scale of machine learning (ML)
models, doubled in just a year from the 50 million of Google’s Transformer in
2017 to the 100 million of OpenAI’s GPT in 2018. The number of parameters of AI
models, which used to multiply tenfold each year, reached 170 billion with GPT-3 in
2020 and 1.6 trillion with Google’s Switch Transformer in 2021, heralding the dawn
of hyperscale AI computing.

Hyperscale AI models can be put to multiple applications. These models, in other
words, are multipurpose. Unlike the conventional ML models, which need to be
taught how to solve problems, these hyperscale models can find solutions on their
own as long as they are given clear explanations of the problems they are to tackle.
It is this ability that allows these models to paint pictures and compose music that
rival those of human artists. The massive amounts of effort and resources poured
into processing trillions of parameters are justified by the multipurpose utility of
these models.

10,000 ML
Models for10,000

When we look at all industries, especially the manufacturing industry, the
infinite potential of AI is difficult to grasp. Deloitte’s survey of AI applications in
manufacturing reports that the success rate of AI projects for manufacturing
companies remains low at nine percent.

A major reason for this low rate of success can be found in the wide diversity of

2 │ Tech Summary

the problems and data of the many different manufacturing companies. There
is no standard model or methodology of data collection and processing among
these companies, nor are there adequate ML models that can be referenced. Any
manufacturer interested in adopting AI is thus forced to have a customized model
tailored to the particular conditions and tasks it faces.

For consumer AI, a single monolithic model—even if it is not a general-purpose,

hyperscale model—can serve hundreds of millions of users. Examples include AI-
based translation services and shopping recommendation algorithms used by

e-commerce platforms. An AI model developed for a single manufacturer, however,
can be used for only that particular company.

Consider the example of defect detection AI models—perhaps the most popular
kind in manufacturing today. Both a company manufacturing automotive cylinders
and another company manufacturing smartphone displays need defect detection
solutions to ensure the quality of their products. For those tasked with developing
AI solutions for these companies, however, the resulting models are vastly different
in every aspect, from the types of data required to the criteria used to define
defects. A state-of-the-art AI model developed to detect defects in automotive
cylinders simply cannot be used to detect defects in smartphone displays.

Andrew Ng, an American computer scientist and ML expert, thus concludes: “In
consumer Internet software, we could train a handful of machine-learning models
to serve a billion users. In manufacturing, you might have 10,000 manufacturers
building 10,000 custom AI models.”1

Manufacturers also frequently alter their processes and production line structures
and replace the parts involved in the production of finished goods. All these
changes mean that the basic data fed into the AI model need to be changed as
well, requiring the model to repeat its learning processes. In some cases, an entirely
different AI model may be needed.

1 Eliza Strickland (2022), “Andrew Ng: Unbiggen AI,” IEEE Spectrum (February 9, 2022) (retrieved from

An MLOps Strategy for AI Models in Manufacturing │ 3
In addition to design and development, the deployment and operation of a given
AI/ML model present challenges of their own. An AI/ML model developed for
manufacturing needs to be adapted to suit the particular client’s manufacturing
environment. These environments, however, vary widely from company
to company. This is what sets AI models for manufacturing apart from AI
models developed exclusively for digital services. The conditions into which a
manufacturing AI model is deployed, in other words, can be unpredictably different
from the conditions in which it was developed. From an operational standpoint, a
model must be retrained on new data after some time; and, more frequently than
expected, the changes to the data may be so significant that a new model must be

OODA Loop: Rapid
iteration for better

To be successful with industrial AI, we must approach the ML lifecycle from a
different perspective. On the surface, the cycle seems to be a linear process,
proceeding from the problem definition stage to the collection and analysis of the
necessary data, the development of a suitable ML model, and deployment. The
actual process, however, is rarely so neat, with each step having to be repeated
until it produces the successful result that is necessary for the next step to take
place. It is commonplace for ML engineers to return to the preceding stage, or
even the stage before that, due to a problem occurring at different stages of the

Figure 1┃OODA Loop and ML Lifecycle

4 │ Tech Summary

ML lifecycle.

The most common case is for an ML model to be successfully developed and
deployed into the production environment only for it to fail to operate as expected.
This is due to the aforementioned differences between the development and
deployment environments. In this case, the ML engineer must return to the
modeling stage to synchronize the two environments. However, even returning
to modeling may not be enough to solve the problem, necessitating a further
regression back to data collection or analysis or the revision of the entire data
collection or analysis method. In some cases, ML engineers may have to return to
the very beginning, where the problem itself needs to be redefined.

In other words, the AI/ML lifecycle is a process of improving a model or application
by exploring different possibilities through iterations of each stage of the lifecycle.
Given the unpredictability of the variables involved, it makes sense to find ways to
accelerate the necessary iterations instead of perfecting the quality of each step of
the loop through enormous investments of time and effort.

The observe-orient-decision-act (OODA) loop strategy is such a method for
accelerating the requisite iterations so as to obtain the desired results. The OODA
loop is actually a military concept developed by U.S. Air Force Colonel John Boyd,
who found inspiration for the concept in his observation and analysis of dogfights
between U.S. and USSR fighter jets during the Korean War. In essence, the OODA
loop holds that the greater the speed with which one side completes its loop
of decision-making and execution in response to the enemy’s movements, the
greater that side’s chances of winning. In other words, the speed of iteration
matters more than the quality of iteration. Boyd himself successfully applied this
doctrine to become an undefeated victor in mock dogfights.

When applied to AI development, the OODA loop strategy means that, the faster
we can iterate the ML lifecycle of problem definition, data collection and analysis,
modeling, and deployment, the greater the chances of success for the AI project.

An MLOps Strategy for AI Models in Manufacturing │ 5

the Prerequisite for
Accelerating the

What, then, is needed to accelerate the completion of each ML lifecycle?

ML models lie at the heart of AI, but applying models to an actual business
environment is a demanding process that requires a multiplicity of factors in
order to be successful. A successful ML model requires not just the collection,
verification, and processing of data but also multiple tests and debugging
runs, infrastructure capable of hosting the given model as well as the ongoing
monitoring thereof, and integrated management of resources and data. Any of
these elements could go awry without forewarning, rendering the whole model

Recall that ML models for manufacturing are often developed and applied in
vastly different conditions. Data scientists and ML engineers typically handle the
development phase, including problem definition, data collection, and modeling.
Software engineers then install and operate the final model. Development and
deployment, however, involve not only different working conditions but also
different modes of thinking. The separation of these two phases thus slows the
OODA loop process.

What matters as much as developing a good model is therefore establishing a
system that ensures the effective deployment and use of that model.

This explains the growing appeal of MLOps today. MLOps involves establishing a

Figure 2┃MLOps Bridging Development and Operation

6 │ Tech Summary

comprehensive system that ensures smooth transition between the steps of the
ML lifecycle by enabling prompt responses and troubleshooting in any part of the
loop, including not just model development but also the software engineering
components involved in implementing applications from the models and operating
them in actual production environments.

The key to MLOps is bridging machine learning (ML) and operations (ops). The
disconnect between these two areas presents a grave challenge to the model’s
ability to learn new data with speed. Through its training pipeline, MLOps delivers
data to the software that generates the model. Those tasked with operating
a given ML model can therefore generate a new model by simply and directly
entering new data into the pipeline without having to go through the ML engineer.
MLOps ensures that the data fed into the pipeline comes from the same source
and thus has the same format and attributes.

MLOps, moreover, ensures that the software developed as part of the ML model
can continue to function in actual operations. Although all IT solutions must face
the discrepancy between development and operation environments, the gap
becomes especially detrimental when it concerns AI/ML models because the data
scientists and operating engineers involved think fundamentally differently. One
solution to this problem is to deliver an automatically tested and verified training
pipeline, which is the central feature of all MLOps platforms.

MLOps Specialized
for Industrial AI

Through the experience it has gained in diverse AI projects in a wide range of
manufacturing fields, including the semiconductor, energy, and automotive
industries, MakinaRocks has come to recognize the need for an MLOps platform
that is tailored to AI solutions for industrial and manufacturing sites. RunwayTM is
the culmination of the experience and expertise that MakinaRocks has gained over
the years.

Industrial AI models are customized to suit the specific circumstances and
characteristics of the given manufacturers. As there are no uniform standards
governing the quality and scale of the data generated by these manufacturers
or the environments in which the models are to be built, customization is a

An MLOps Strategy for AI Models in Manufacturing │ 7

Figure 3┃RunwayTM: MLOps Flow

requirement when it comes to manufacturing clients.

MakinaRocks’ RunwayTM provides flexibility in all stages of the ML lifecycle so as
to ensure timely responses to the widely-varying datasets and problems that
can emerge in industrial environments. It has also been designed to ensure the
seamless operation of ML models and provide standardized environments for the
development, deployment, and operation of ML models, thus guaranteeing rapid
iteration throughout the ML lifecycle.

Integrating LinkTM, another MakinaRocks invention and a Jupyter-based ML
modeling tool, into RunwayTM enables users to develop high-performance ML
models using diverse datasets with a variety of libraries and frameworks. Moreover,
the pipelines for retraining are generated immediately after the models are

Furthermore, RunwayTM offers a broad range of features that help resolve the
chronic issues associated with operating AI models in manufacturing, including
synchronization of the development environment and the operations environment
and providing direct access to deployed models for retraining and debugging.

8 │ Tech Summary
An MLOps Use
Case inAnomaly

AI/ML has a broad array of industrial applications. The most well-known ones
include anomaly detection, intelligent control and optimization, and predictive
analytics. Through its participation in a large number of AI projects involving various
Korean manufacturing companies and companies in other industries, MakinaRocks
has built wide-ranging references for automated chip design, anomaly detection
in industrial robot arms, anomaly detection for semiconductor facilities, detection
of future emergency shutdowns of petrochemical reactors, remaining useful life
prediction for EV batteries, solar power generation prediction, and optimization of
EV energy management system controls.

Using MakinaRocks’s MLOps platform to detect laser drill anomalies is a
compelling example of MakinaRocks’ use of the platform in real-world
situations. In semiconductor manufacturing, laser drills are crucial tools. The
client company was operating dozens of laser drills with identical specifications.
Nonetheless, different AI models needed
Figure 4┃Model Design Capable of Coping with Unstable Data to be developed and deployed separately
as each drill was operated with different
core parts and parameters and produced a
unique distribution of data. Moreover, any
interruption in the operation of just one
laser drill led to the shutdown of the entire
production line. These problems added to the
urgency of adopting a comprehensive and
more effective AI solution.

In manufacturing, normal data is easy to
collect, while collecting data on anomalies
is notoriously more difficult. MakinaRocks’
solution thus featured an autoencoder
model capable of compressing, restoring,
and training with normal data. The resulting
model was designed to detect and identify
signs of forthcoming interruptions with a high
level of precision based on semi-supervised
and continual learning. The model thus

Input data
Column B
Column D
Column F
Column H

Autoencoder Anomaly


▲ When all data is normal

Input data
Column B
Column D
Column E
Column F
Column G
Column H

Autoencoder Anomaly


▲ When some data is unstable
Anomaly Detection Model
Anomaly Detection Model

An MLOps Strategy for AI Models in Manufacturing │ 9
User-selected retraining data range

Extracting data from selected period

Hundreds of items
of sensor data

event ratio
data ratio

Taking all
factors into
of retraining

Model output
for given data

for selected

[“2022-10-01”, “2022-10-07”]

informed facility maintenance staff of these signs one month in advance so that
they could maintain timely upkeep.

The defining achievement of this project is the design of a model capable of
providing uninterrupted inferences in the absence of the guaranteed availability
of relatively less important data. The previous models in place were unable to
produce inference results when only some of the multiple sensors installed on the
laser drills failed. Such frequent interruptions prevented continuous monitoring and
inferences and resulted in the client’s dissatisfaction with the given AI solution.

MakinaRocks’ solution was able to maintain its inference performance by training
an additional model with data that excluded the unstable sections. As Figure
4 shows, the solution actually involved two models: the main model, applying

its autoencoder to normal data, and the replacement model, applying a sub-
autoencoder to the less important data with fluctuating availability. The solution

alternated between these two models depending on the availability of data. The

Figure 5┃Determining Whether Chosen Datasets are Appropriate as Training Datasets

10 │ Tech Summary

streaming-serving and model-shadowing features of RunwayTM, in other words,
enabled the solution to continue to monitor the laser drills and detect anomalies
notwithstanding trouble in some of the sensors.

Furthermore, MakinaRocks’ solution also included an algorithm to determine the
appropriateness of datasets for retraining, enabling general users of the solution
to retrain ML models with the right data without the help of data scientists or
ML engineers. During a major event, such as the replacement of a core part, the
distribution of data under observation changes significantly, necessitating that the
ML model be retrained with the new data. For lay users who lack data analysis
skills, however, it is difficult to choose what data should be fed into the model for

The user-friendly user interface and dashboard feature of RunwayTM support
general users’ decision-making based on multiple variables. This particular
algorithm considers a variety of factors simultaneously, including the ratio of
events involving the given core part as saved on the dashboard, ratio of objective
anomaly data included, anomaly-detecting algorithm based on the scope of data,
and data-clustering algorithm. As a result, general users can train and operate the
AI model themselves, even during major events, without the help of data scientists
or ML engineers.

Introducing AI
That Solves the
“Real Problems” of

Operations is ultimately where an AI project comes to completion. An ML model
boasting the most advanced design would not have much value if it failed to
operate in the target environment. Models that fail to retrain themselves without
interruption are also of little use. MLOps thus holds the key to the success of AI

MLOps is a broad-ranging concept that spans design and operations alike.
The definition of an MLOps solution thus varies from company to company. A
company with sufficient data science capacity should ideally adopt a bespoke
MLOps that allows freedom in ML modeling based on coding flexibility, in the form
of a platform-as-a-service (PaaS). Another company that lacks such development
capacity and needs an MLOps platform that supports citizen data scientists would

An MLOps Strategy for AI Models in Manufacturing │ 11

Learn more about MakinaRocks Runway

RunwayTM Applications across Sectors
Sector Objectives Issues/challenges RunwayTM effects

•Focus on project-based model
•Deploy 50+ ML models.

of developing open source-based
•6+ months needed for deployment

deployment from six months to four
weeks (reductionof about80%).
•Reducedrequisite manpower for
developing MLOps environments by
about 50%.


•Introduce an ML-based
semiconductor production facility.

•Lack of trained data scientists
•Lack of capabilities to operate/
manage high-performance ML

•Provided intuitive UI/UX to support
•Enabled users to retrain and deploy
models without requiring extensive
experience with ML.

•Improve the solar power generation
predictionmodelfor 600+solar
power plants.

•Rising cost of model maintenance
due tolack of synchronization
between development and
deployment environments

•Simplified debugging by using a built-

•Accelerated the scaling-out of the
identical model.

find an MLOps solution-as-a-service (SaaS) product more useful. For the majority
of companies, however, the choice is not always so clearcut.

MLOps solutions should be flexible so that they can accommodate the different
concerns and business focuses of different clients. Some clients want a solution
that can be interfaced with diverse legacy systems. Some want a solution whose
training and operating environments are kept completely separate in order to
maximize the efficiency of resource use. As industries require high-performance AI
solutions, it is also crucial to ensure that the solution, developed through countless
hours of experimentation and analysis by experts, can continue to perform
optimally when applied to actual industrial situations. The operating process, too,
should reflect the distinct business logic of the client company.

Equipped with extensive experience in AI development and application,
MakinaRocks has created an MLOps platform that is capable of meeting such
diverse needs of industries. Going forward, the company will continue to lower the
barriers to industrial AI solutions and help establish AI standards that can solve the
real problems of industries.