Artificial intelligence

Devops For Machine Learning: Accelerating Model Development And Deployment

Figure 2: MLOps framework

A Crucial method for releasing the data potential and empowering organisations to become more innovative, efficient, and sustainable is machine learning. For businesses looking to maximise the value of their data and enhance their operations, machine learning has become crucial (Chen et al., 2014). Thanks to the emergence of big data, businesses today have access to enormous volumes of data that may offer insightful information. The sheer amount of data, however, makes it challenging for businesses to manually extract insightful data. Machine learning can help with this. By using machine learning algorithms and techniques, businesses can rapidly and correctly analyse massive amounts of data, find trends, and make data-driven choices. By using MLOps concepts, machine learning algorithms, for instance, may be used to automate manufacturing operations (Zeng & Shi, 2019), estimate customer attrition, optimise supply chain management, and identify fraudulent financial transactions (Kshetri, 2018). The goal of MLOps is to automating and streamlining the process of installing and maintaining machine learning models in production. With the growing use of machine learning in enterprises, MLOps has emerged as a crucial discipline for assuring the dependability, scalability, and effectiveness of machine learning systems (Géron, 2019). MLOps practises may help organisations manage the whole machine learning model lifecycle, from training to deployment and monitoring, and guarantee that models stay correct and current over time.  By employing MLOps practises, businesses may decrease the time and effort needed to deploy machine learning models in production and lower the chance of model failures and downtime (Wu et al., 2020). Additionally, MLOps may help firms become more innovative and agile by enabling them to quickly iterate on and enhance their machine-learning models in response to shifting business requirements and market conditions (Sculley et al., 2015). In today’s data-driven economy, MLOps is therefore a crucial part of a machine learning strategy that may help organisations stay one step ahead of the competition (Bonomi et al., 2020). MLOps is a practice in which three disciplines collaborate: machine learning, software engineering, and data engineering. MLOps aims to bridge the gap between development and operations by utilizing machine learning systems in production.

By utilising the following principles: Continuous Integration/Continuous Deployment automation, Orchestration of workflow, Data versioning, Reproductivity, model, and code, Collaboration, Continuous training of ML and evaluation, tracking of ML metadata and logging, Continuous monitoring, and Feedback loops—MLOps aims to facilitate the development of machine learning products, according to Kreuzberger et al. (2022). MLOps is a method that covers all aspects of conception, execution, monitoring, deployment, and flexibility for machine learning products from beginning to end.

Machine Learning: 

In recent years, machine learning has made considerable advancements, especially in applications for manufacturing. In applications like natural language processing, picture and audio recognition, and self-driving cars, to mention a few, deep learning models have excelled. The use of reinforcement learning to improve robots and industrial processes is another breakthrough. Additionally, explainable AI approaches have been developed to increase the interpretability of ML models, and transfer learning has been utilised to increase the effectiveness of training models on limited datasets. ML has been applied in manufacturing for a variety of purposes, including as supply chain optimisation, predictive maintenance, and quality control. ML in production has also benefited from developments in automated machine learning and distributed computing. While distributed computing enables ML algorithms to scale and manage vast volumes of data, AutoML solutions are meant to streamline ML processes and eliminate the need for user involvement. These advancements are supported by a number of scholarly sources. A Google study shows how scalable and effective machine learning pipelines can be created using Kubernetes and TensorFlow in real-world settings. Another IBM research highlights the benefits of AutoML in cutting down on the time and expense of ML development. In a GPT-4 technical paper, Open AI explores current developments in deep learning and their effects on different disciplines, including industrial applications. These examples show how machine learning has significantly impacted commercial applications and the possibility for further development.

Machine Learning Operations (MLOps):

The literature of today shows how academics and industry work together to increase ML output. Model development has gotten a lot of attention during the past 10 years, claims Andrew (2021). The academic community has concentrated on developing machine learning models and benchmarking rather than using sophisticated machine learning systems in practical settings, claim Kreuzberger et al. (2022). The MLOps fills the current gap between production and development. The model component will be installed and used in the production environment by the ML engineering team (Andrew, 2021). It is time to think about monitoring and maintaining the system without interfering with production after the full ML project or system is running. Sculley and his colleagues describe how challenging and expensive it is to maintain ML systems using the technical debt metaphor (Sculley et al. 2015). This metaphor was first used by Ward Cunningham in 1992 to illustrate the long-term consequences of fast progress in software engineering. It is effective to plan out all the actions that must be taken during the process by thinking through the ML project lifecycle. To assist with the evolution of ML projects through the lifecycle, the MLOps discipline has developed a set of tools and concepts.

Figure 1: MLOps life cycle

Figure 1: MLOps life cycle

An ML model is created and enhanced throughout its life cycle through a workflow or analytics pipeline that interacts with stakeholders across the organisation (Sweenor et al., 2020). Reducing friction in these pipelines and workflows is key to realising the potential of data science and machine learning. ML models require ongoing improvement. They incorporate relationships with constantly changing data and data transformations, and the accuracy of their predictions is impacted by data drift. In contrast to conventional software application building, operationalizing ML pipelines has management consequences. Long-term accuracy in particular requires regular tweaking, retraining, and even total remodelling (Sweenor et al., 2020). MLOps are currently being used by creative organisations. Data pipeline, model pipeline, and deployment pipeline are the three pipelines they use to organise their data science and machine learning pipelines (Sweenor et al., 2020). The existing literature on MLOps is still primarily fragmented and intermittent, according to Testi et al. (2022). The core principle and rationale underlying the processes remain the same across the literature, despite the variations in the phases and pipelines.

Any ML project’s deployment phase is one of its most thrilling and difficult phases. Deploying ML models is challenging because of problems with both software engineering and machine learning. When deploying an ML system, users must make sure that the system has a way to handle changes like idea drift and data drift on an ongoing production system. When adopting an ML model, however, software engineering faces several challenges, including real-time or batch processing, cloud vs. edge/browser, computing resources (CPU/GPU/memory), latency, throughout (QPS), logging, security, and privacy. The manufacturing system must also operate constantly at the lowest cost while generating the greatest amount of output (Andrew, 2021). MLOps incorporates model governance, business and legal needs, and helps to enhance the quality of production models. MLOps is attempting to address issues like: – Ineffective workflows: MLOps offers a framework for effectively and efficiently managing the machine learning lifecycle (Hewage et al., 2022; Garg et al., 2022). By combining business knowledge with technical skill, MLOps develops a more organised, iterative approach. – Bottlenecks: With complex, illogical algorithms, bottlenecks can frequently occur. Collaboration between the operations and data teams is facilitated by MLOps, which helps to lessen the frequency and severity of these kinds of problems (Hewage et al., 2022). The MLOps promoted cooperation makes use of the skills of hitherto isolated teams, enabling more effective machine learning model creation, testing, monitoring, and deployment. However, it takes time and work to find solutions to these issues. The majority of MLOps experts refer to the attitude and culture in ML governance, where having a large number of experienced workers on staff is essential to the success of MLOps. Automation of operations, ML infrastructure, and software all face significant hurdles, including concepts and data drift (Garg et al., 2022; Kreuzberger et al., 2022).

Industry Needs MLOps:

The necessity for MLOps in the business arises from the requirement to continually monitor and maintain machine learning models as they become more commonplace in order to guarantee that they continue to produce accurate and trustworthy results. MLOps enables organisations to quickly identify and address problems, enhance the performance and accuracy of their models, and ultimately produce better business outcomes by automating and streamlining the process of deploying, monitoring, and maintaining machine learning models in a production environment (Makinen et al., 2021). MLOps also makes it easier for data scientists and IT operations teams to work together and ensures that models are properly managed and adhere to compliance standards.

Since the introduction of MLOps and its guiding principles, the market has demonstrated that ML solutions currently do not benefit from MLOps’ advantages. The following essential elements serve as a concise summary of the benefits of MLOps in revolutionising the ML industry: Collaboration: In the prior setting, all ML project teams, including ML engineers, data scientists, software developers, and IT operations engineers, operated in isolation (Kreuzberger et al., 2022). This antiquated method slowed down the project and made it challenging to manage divided teams. MLOps enables teamwork among all members. 

– Automation: 

Machine learning and software automation are both necessary for achieving targeted business goals. Diverse teams may concentrate on more important business concerns by automating the lifecycle of ML-powered software, leading to quicker and more dependable business solutions (Battina, 2019). 

– Effectiveness: From conception to deployment, MLOps improves the productivity of all production teams and the methodology used to construct machine learning projects (Battina, 2019). 

– Workflow: Each machine learning project involves data scientists team and machine learning engineers developing state-of-the-art models manually or digitally. When choosing an ML model for training, data scientists take into account the model’s complexity and how the model has evolved. Environments used for development and testing are different from those used for staging and production. 

According to Battina (2019), ML manual process models usually are unable to adapt to changes in the dynamics of production environments or in the data used to represent such environments. As a means of facilitating process automation, MLOps is introduced. A completely automated procedure results from the participation of all concerned teams in the search for a single solution.

This study is built on the MLOps framework created by John et al. (2021). The data pipeline, model pipeline, and release pipeline are just a few of the pipelines and stages that the framework describes as part of the adoption of MLOps. Each pipeline contains a number of phases to complete and a responsible professional or professionals.

Figure 2: MLOps framework

Figure 2: MLOps framework

In this study, examining an MLOps paradigm and responding to the research topic may be done utilising both quantitative and qualitative methodologies. The quantitative technique is applied to survey data to enable statistical analysis. In order to find patterns, trends, and correlations between variables, quantitative approaches can produce accurate, objective data that can be statistically evaluated (Creswell, J. W. 2009). This makes it possible for researchers to make quantitative judgements and make generalisations about a bigger population. Researchers employ the qualitative approach to better understand the intricacies, context, and individual experiences of interviewees. In-depth research of human behaviour, attitudes, beliefs, and motives is made possible by qualitative methodologies. Creswell, J. W. asserts that qualitative data can offer rich and in-depth insights that quantitative approaches alone cannot. This combination enables the researcher to provide solid and trustworthy results.

The procedure that the researcher in this study guided is shown in the image. MLOps implementation issues and solutions, as well as MLOps pipeline for product recommendations on e-commerce platforms, are two sub-tasks the researcher has to concentrate on even though the study’s overall goal is to establish whether the MLOps paradigm is a game changer in the machine learning business. There are two kinds of data in place—primary and secondary—to meet these subtasks. To gather primary data, the researcher employed questionnaires and conducted interviews. Secondary data come from earlier, related studies. 

Figure 3: MLOP’s paradigm

Figure 3: MLOP’s paradigm

Data Collection Methodologies: 

A literature study, survey, and interviews with experts in ML, data engineering, and software engineering from diverse organisations are all part of the mixed-methods research the researcher has done. MLOps engineers, ML engineers, Data Scientists, Data engineers, DevOps engineers, Software engineers, Backend developers, and AI Architects are the individuals targeted. The investigation uncovered the manner in which these interviewees and respondents make sense of MLOps. The researcher employed professional networks, social media, and online platforms like LinkedIn to target people in the defined field of study interest, which helps to obtain trustworthy feedback, in order to discover more people to conduct interviews and questionnaire responders.

Secondary Data: 

The majority of the material in this study was accessed from reliable scholarly resources mostly using the Google Scholar search engine. A researcher found a wealth of useful publications, books, and blogs about MLOps by searching for terms like MLOps survey, MLOps machine learning, and MLOps DevOps. To increase the speed and effectiveness of machine learning development and deployment, the multidisciplinary discipline of machine learning operations combines software engineering techniques, data engineering, and machine learning. With research and development attempts to enhance the end-to-end machine learning development process, there has been a growing corpus of literature on MLOps. These initiatives attempt to speed up and streamline model deployment while enhancing the security, reliability, and openness of machine learning systems. Various organisations have created tools and frameworks to aid in the development of MLOps. The MLOps life cycle requires the use of MLOps technologies and frameworks including TensorFlow Extended (TFX), Kubeflow, Apache Airflow, AWS SageMaker, and Google Cloud AI Platform. These technologies offer a centralised platform for creating, deploying, and maintaining machine learning models. They also have a number of features, including automated model training, model serving, and monitoring.

The interview approach is frequently used to collect data since it enables the researcher to learn more about the topic under study in-depth (Alshenqeeti, 2014). It also enables the researcher to pursue further information and pose follow-up queries. Structured (using a pre-defined questionnaire) or unstructured (allowing for a more open-ended dialogue) interviews can be carried out in person or over the phone. The adaptable approach may be modified to meet particular research demands and objectives. Interviews can also be used to collect information from a small group of participants, making them appropriate for exploratory or qualitative investigations. To specifically select experts in the ML area, a purposive or strategic sample method was adopted. To make sure that the participants picked are the best matches for the research topic and can give the most pertinent information, deliberate sampling is frequently employed in qualitative research (Lemp J. et al., 2012). Participants may be chosen by the researcher based on their background, level of experience, subject-matter knowledge, or point of view. Contrary to random sampling, purposeful sampling includes choosing individuals based on predetermined traits or standards that are pertinent to the study topic or hypothesis. In this study, three semi-structured interviews were conducted. The researcher met a group of experts in a retail firm for the first interview on February 15, 2023. This investigation was greatly aided by the eight employees of the organisation who were working on the MLOps project. The team was made up of software developers, ML engineers, MLOps engineers, and AI architects. An AI research scientist was interviewed for the second time on February 23, 2023. On February 27, 2023, a senior data consultant was the subject of the third interview.  Research technique called as a semi-structured interview combines the adaptability of an unstructured interview with the framework of a structured interview. In a semi-structured interview, the researcher has a list of predefined questions. However, the interview also enables follow-up queries and inquiries to dive more into the comments of participants. Semi-structured interviews are frequently used in qualitative research to gather data about participants’ experiences, viewpoints, and attitudes. They preserve some structure and consistency throughout interviews while allowing for a more in-depth study of the participant’s opinions than a structured interview.

Ethical Considerations: 

MLOps introduce a number of ethical issues that, like any technology, need to be taken into account in order to guarantee that the models are created, implemented, and used properly. The next section discusses some of the most important moral issues with MLOps. 

Fairness and Prejudice: 

The potential for bias and injustice in machine learning models is one of the most important ethical issues in MLOps (Niemelä et al., 2022). The training of models using biased data or the encoding of pre-existing biases in the data can lead to biased models. Particularly when it comes to financing, hiring, and criminal justice applications, bias can result in inaccurate or discriminatory outcomes. Teams from MLOps must work together to make sure that models are evaluated for bias prior to deployment in production and trained on a variety of representative data. 

Privacy and Security: Security and privacy are important factors in MLOps. Sensitive data, including financial information, medical records, and personal information, is commonly used in machine learning models to generate predictions. MLOps teams are responsible for protecting both the models themselves from unauthorised access and modification and the data used to train and deploy the models. 

Explainability and Transparency: 

Understanding the reasoning behind a particular choice might be difficult due to the complexity and difficulty of machine learning models. Machine learning may come under suspicion as a result of this lack of openness. In order for stakeholders to understand how choices are made, MLOps teams must make sure that models are clear and simple to grasp. MLOps teams must also think about the moral application of machine learning models. 

For instance, models employed in the criminal justice system need to be impartial and fair, but models used in the medical area ought to be utilised to help people rather than to damage or exploit them. Model development, deployment, operation, and usage must all be done ethically and for the intended purpose by MLOps teams (Niemelä et al., 2022). Continuous monitoring and improvement: To make sure that machine learning models operate as intended and that any biases or other ethical issues are addressed, MLOps teams must continually monitor and enhance these models. This can entail upgrading the algorithms, updating the data used to train the models, or altering the way the models are utilised in actual production. Machine learning operations teams must make sure that models are created, used, and maintained in an ethical and responsible manner. By taking into account the aforementioned factors, MLOps teams may contribute to ensuring that machine learning models benefit rather than damage society. Every step of this study’s execution was done in accordance with research ethics. No personal information would be asked, and all responses would be fully anonymous, according to the language on the questionnaire. Prior to the interview, participants were made aware that the interview would be recorded and, if required, quoted in the text. The participant was then informed of their rights, including the freedom to terminate the interview at any moment and the right to refuse to answer any uncomfortable questions. Additionally, they received information about how their names would be obscured and how their data would be handled and kept in compliance with GDPR guidelines.


In this chapter, the researcher analyses the primary data, both interviews and questionnaires. Interviews are analysed using a thematic approach. The analysis of questionnaire data is done in Jamovi software to develop statistical analysis. 


Examining interview data All of the interview material was gathered, and then it underwent a thematic analysis. The choice was made to analyse themes. Thematic analysis, according to Braun & Clarke (2006), is a method for methodically locating, classifying, and offering insight into meaning patterns (themes) in a data collection. It looks for patterns in the data gathered, which was particularly helpful for our project because it explores MLOps and addresses difficulties businesses encounter while applying it. Additionally, Nvivo, a programme for qualitative data analysis, was used to analyse the data. Learning more about the material and improving your understanding of how to categorise it into distinct themes was the first stage in a thematic analysis. Five themes were developed based on the interviewees’ responses: importance, challenge, teamwork, future trend, and policy. Figure 4 of the coding chart illustrates the six themes and their percentage weights.

Figure 4: Coding chart

Figure 4: Coding chart

Theme 1: The value of MLOps to the machine learning sector for developing and deploying ML models at scale, MLOps is essential. As companies strive to utilise ML to boost company performance, it is becoming increasingly important to the ML sector. This subject seeks to comprehend MLOps, the distinction between MLOps and DevOps, and its significance in the machine learning (ML) sector. The primary distinction between MLOps and DevOps, according to one interview, is “Data, model, and concept versioning over code versioning.” In essence, MLOps is a particular DevOps approach for machine learning projects and pipelines. Automating the full machine learning lifecycle, from data gathering to model deployment and monitoring, is what MLOps includes.

Theme 2: MLOps implementation difficulties To make sure that ML models are delivered in a trustworthy, repeatable, and scalable way, MLOps is integrating machine learning into the software development and deployment process. To implement MLOps, which is a challenging but essential practise for businesses to adopt in order to maximise the value of their ML investments, requires a combination of technical competence, efficient coordination, and a thorough awareness of the business and regulatory context.

Organizational Challenges:

Business domain (understanding): The consent of senior management is necessary for the adoption of MLOps in an organisation. Senior managers occasionally want assistance in comprehending why a business can invest in this technology. MLOps has a wide range of specialisations, which might lead to a horizontal misinterpretation. Mentality: Developers have a long-standing informal rule that goes something like this: “If it works, don’t touch it.” This mentality is still prevalent in the development sector, where some claim that businesses cannot invest in new technology if the old technology does the same tasks. They must take into account further benefits including toughness, precision, and speed. application of MLOps with microeconomics 


A full MLOps life cycle requires a lot of resources, such as manpower and funding. Ten full-time experts, including software engineers, ML engineers, data scientists, and MLOps engineers, are working on the MLOps project, according to one of the organisations the researcher spoke with. The MLOps project was still unfinished at the time of the interview, even though the team had been working on it for two years. Everyone can guess how much the business is spending on the team, and the expense is not only in personnel. 

Skilled Professionals: MLOps is a relatively new idea. The idea is being learned by many people in machine learning, data science, and software development. There are skilled people, but combining their specialised knowledge to create the whole MLOps life cycle is still difficult. Collaboration is essential for the MLOps project to be successful among the personnel. Bringing together all of these professionals who are engaged in the same project but have separate duties is difficult.

ML Systems Challenges: 

Data protection laws: The data protection rules might occasionally prevent the MLOps process from being automated. The GDPR requires that personal data be treated fairly, legally, and openly. Additionally, people must be notified about how their data is processed and given specific rights, including the ability to view, amend, or delete their data as well as object to its processing. Additionally, the GDPR establishes strict guidelines for how AI should treat personal data, including verifying data accuracy, limiting the amount of data gathered, and disclosing the details of AI algorithms. 

Automating the Lifecycle: 

An MLOps engineer said, “…automating the entire lifecycle of machine learning products is the key mandate of MLOps” However, it is a challenge to make it fully automated. “…we always face problems in the automation process”. Data drift and concept drift, deployment complexity, and model explainability and interpretability are other ML systems challenges.

Operational challenges Monitoring [model layer and platform layer]: A strong monitoring approach that takes into account both the model layer and the platform layer is necessary since monitoring is a crucial component of MLOps. MLOps teams can make sure their machine learning models function at their best by keeping an eye on performance indicators, resource use, mistakes and failures, and security concerns. 

Theme 3: Teams working together across the MLOps life cycle Data scientists, software developers, and operations experts must work together to implement MLOps. The success of the ML projects depends on the teams’ ability to collaborate, which is difficult to sustain throughout the MLOps life cycle. For the creation, implementation, and maintenance of realistic machine learning models, collaboration amongst the teams engaged in the MLOps life cycle is crucial. The sprint sessions “help maintain good team collaboration,” according to one respondent. As said by a different interviewee, “…we have to be professional if you understand your task, you work on it, and respect the schedule.” The MLOps cycle involves a variety of workgroups with distinct roles and responsibilities, including business stakeholders, data scientists, data engineers, and DevOps engineers. For successful model creation and deployment, teams engaged in the MLOps life cycle must work together. By creating agreed objectives, lines of communication, and procedures, team members may create reliable and scalable machine-learning solutions. 

Theme 4: The MLOps future trend as more companies use machine learning and artificial intelligence, MLOps is crucial for guaranteeing the efficient and successful deployment of these models. Experts in machine learning claim that MLOps will never go away. Their justifications are based on the significance of MLOps in modernising ML engineering. One interviewee provided a summary of his outlook on MLOps’ future and the technologies that will influence it. “I think that the integration of AI and automation in the deployment, monitoring, and administration of ML models will have a significant impact on the future of MLOps. Along with the growth of containerization and orchestration technologies, the trend towards cloud-based MLOps solutions will only intensify. MLOps has a promising future, and I’m eager to see how it will transform the way we think about machine learning operations. The future of MLOps is expected to be marked by more automation, integration with well-established DevOps practises, and a concentration on transparency, security, and compliance. As machine learning is more frequently used, MLOps will continue to be essential in guaranteeing the efficient and successful deployment of these models in diverse applications. The MLOps scope, according to one interviewee, includes these three crucial stages: Designing the ML-powered applications, ML experimentation, development and deployment, and ML operations and observability are the three main aspects of the full MLOps process. Automating the many machine learning funnel phases, including data collection, preprocessing, model training, testing, deployment, and monitoring, is what MLOps comprises. Automating the MLOps lifecycle can decrease manual involvement while simultaneously increasing the machine learning pipeline’s overall effectiveness. By automating the MLOps lifecycle, businesses may improve the speed, dependability, and quality of machine learning models while reducing the likelihood of inconsistencies and raising productivity.

Theme 5: Policy Because they were developed with conventional software development processes in mind and may not take into account for the special properties of machine learning models, existing regulations and data protection policies can make it more difficult to adopt MLOps. Data protection, privacy, and security regulations may be more severe for machine learning models since they routinely employ sensitive and individually identifiable data (Char DS et al., 2018). Model transparency and explainability policies may need more testing and documentation, which might make them difficult to apply.


Examining the survey results 84 people participated in this study and answered the questionnaire. 54 (64%) of the respondents work for organisations that have adopted or are considering implementing MLOps, whereas 30 (36%) are aware of MLOps but believe that their organisations still need to do so. The survey’s findings show that 42 (50%) respondents have experience of less than five years, 36 (43%) have experience of five to ten years, and 6 (7%) have experience of more than ten years. This makes sense given that MLOps is a novel idea and that many responders employ MLOps in their organisations. The researcher and his supervisor decided to end the poll after one month of gathering opinions from MLOps’ employees. Because all 84 participants are in the targeted group and are better knowledgeable about the subject of the inquiry, this quantity is adequate to provide a clear image of the MLOps field. MLOps engineers, ML engineers, Data scientists, Software engineers, Data engineers, DevOps engineers, Backend developers, and AI architects are the individuals targeted. The distribution of the replies is shown in Figure 5 of question 1. It is encouraging that many who replied to the poll hold relevant employment and have experience in machine learning engineering.

Figure 5: Distribution of respondents

Figure 5: Distribution of respondents

Figure 6 of the third question shows the size of our respondents’ company. The figure shows that 30% of respondents come from more prominent companies with more than ten thousand employees. This is understandable because the bigger the company, the more adoption is easier. Big companies have the resources to implement MLOps.

Figure 6: Size of companies

Figure 6: Size of companies

The difficulties businesses have while deploying MLOps are depicted in Figure 7 of the fifth question. The MLOps life cycle has a lengthy list of difficulties, with a dearth of experienced personnel at the top. Every problem is examined in the analysis section along with suitable solutions. Organisational problems, ML system issues, and operational challenges are the three categories into which challenges are divided.

Figure 7: Challenges companies face when implementing MLOps

Figure 7: Challenges companies face when implementing MLOps

The reply demonstrates the importance that respondents place on MLOps in reshaping and revolutionising the machine learning sector. The researcher again draws conclusions from the input of our respondents in the next phase of the analysis.

According to an analysis of the survey results, Figure 8, many respondents concur that MLOps is more focused on fusing the efforts of ML engineers and developers to automate ML products. This demonstrates why modernising the ML sector requires automating its processes. Respondents do not want to firmly agree with the statement because the idea is still novel to many experts.

Question 6 was “To what extent do you currently agree with the following statements? MLOps is more concerned with combining the work of ML engineers and developers to automate ML products.”

Figure 8: A plot of MLOps automation

Figure 8: A plot of MLOps automation

Out of 84 respondents, the findings indicate that 47 (56%) agree with the statement, 19 (23%) strongly agree, 13 (15%) are indifferent, and 5 (6%) disagree. We learned from the interview part that firms have various difficulties when they deploy MLOps. Professionals are highly optimistic about the contribution MLOps is making to the business, despite the fact that development is still ongoing in many firms and the difficulties they confront.

Figure 9 asked whether MLOps usage and development were still in the early stages in many businesses. What do you think of MLOps in general?

Figure 9: A plot of MLOps adoption

Figure 9: A plot of MLOps adoption

The survey’s findings show that 35 (42%) of respondents have a very favourable opinion of MLOps, while 34 (40%) are positive, 12 (14%) are neutral, and 4 (%) have a bad opinion of MLOps. The purpose of the study is to determine whether MLOps is revolutionising the ML engineering sector. Figure 10 demonstrates that the majority of respondents concur that MLOps is revolutionising the ML engineering sector. What percentage of the following statement do you agree with, according to question 8? Machine Learning Engineering is being revolutionised by MLOps.

Figure 10: A plot shows how MLOps is revolutionizing ML field

Figure 10: A plot shows how MLOps is revolutionizing ML field

According to poll results, 28 (33%) respondents and 41 (49%) respondents both strongly agree that MLOps is revolutionising machine learning engineering. The density map in Figure 11 below demonstrates that experts from various firms have a very similar view of how MLOps are used and how they help to advance the area of machine learning engineering. They concur that MLOps is revolutionising the field of ML engineering.

Figure 11: A density plot shows how MLOps is revolutionizing ML field

Figure 11: A density plot shows how MLOps is revolutionizing ML field

Analysis shows that big companies adopt and implement MLOps more than small and medium companies. As we discussed above, the implementation and adoption of MLOps require companies to have enough resources in terms of skilled staff and necessary budgets. It is easier for established companies to afford the cost of MLOps implementation than startups or growing companies.

Findings And Discussion: 

Implementing MLOps: While utilising MLOps may be challenging for enterprises, the experts we spoke with stressed how important it is to build and maintain reliable machine learning models in operational contexts. Create, develop, test, and manage massively scalable machine learning models for business use with the help of MLOps, a collection of best practises and tools. However, employing MLOps may provide a variety of difficulties for businesses. Lack of collaboration between the operations teams, data scientists, machine learning engineers, and software developers is one of the main issues with deploying MLOps. Siloed teams, poor communication, and delays in model rollout can be the results of this. To overcome this issue, businesses should create cross-functional teams with clear roles and duties and set up a framework for communication and cooperation that includes frequent meetings, shared planning, and work documentation. Reproducibility is another problem, which is challenging since models may be delicate to even little alterations in the environment or data. To assure repeatability, businesses may manage code, data, and model artefacts using version control. They may utilise containers to assure consistency across environments and provide a clear methodology for creating, training, and deploying models. Monitoring is a problem with MLOps implementation. Businesses must keep an eye on data drift, model performance indicators, and other production-related concerns. They may do this by implementing monitoring, alerting systems, automated testing, and validation to find problems as they appear. Due to the integration with current systems, managing dependencies, and assuring scalability, deployment can also be complicated. By containerizing models and managing dependencies using software like Kubernetes and Docker, businesses may overcome this issue. They can leverage CI/CD to hasten the deployment process and infrastructure as code to automate deployment and configuration. Security and privacy are major considerations when using MLOps since machine learning models have the ability to reveal sensitive data or be exposed to attacks. Businesses may solve this issue by using security and privacy measures including access restriction, encryption, and data anonymization. They can make advantage of secure coding techniques and safe deployment procedures.


By providing systematic and automated administration of the whole machine learning process, from creation to deployment and maintenance, MLOps has revolutionised the field of machine learning engineering. By utilising the greatest software engineering and operations practises, MLOps has improved the maturity and dependability of machine learning development, making the process of creating, testing, and deploying models quicker and easier. By promoting improved communication between data scientists, machine learning engineers, software developers, and operations teams, MLOps is revolutionising the field of machine learning engineering. With clearly defined roles and responsibilities, these teams can work together easily using MLOps to develop and implement machine learning models that are suitable for the needs of the company. Additionally, MLOps makes it simpler to manage all phases of the workflow of ML, data pre-processing, model training, release, and monitoring. Using automated tools and procedures, MLOps has decreased the amount of human effort necessary, allowing teams to concentrate on more complex activities like feature engineering and model optimisation. By enhancing model repeatability and dependability, MLOps is also revolutionising machine learning engineering. MLOps combines continuous integration and deployment pipelines, automated testing, and version control to make sure that models are produced quickly and effectively, reducing the possibility of mistakes or problems. Additionally, MLOps is dealing with model deployment and maintenance problems. By using Docker and Kubernetes containerizations, MLOps makes it simpler to deploy models in a variety of contexts, from on-premises data centres to cloud-based platforms. Teams are able to immediately identify problems with production models thanks to MLOps’ automated monitoring and alerting systems. As more businesses come to understand the advantages of employing this kind of machine learning engineering, MLOps has a promising future. MLOps will play a greater role in helping organisations build trustworthy and scalable models that provide business value as machine learning becomes more prevalent across sectors and use cases. One of the major themes for MLOps in the future is the advent of increasingly specialised tools and platforms created especially for machine learning engineering. Implementing MLOps best practises and automating crucial operations like data preparation and model training will be made easier by these technologies. Another development in machine learning models is the elevated value placed on interpretability and explain ability. As organisations work to create models that are clear and easy for end users to comprehend, MLOps will be essential in ensuring that models are developed and deployed in a manner that satisfies these needs. The direction of MLOps will also be influenced by developments in AI and machine learning research. MLOps must expand in order to embrace new breakthroughs and enable organisations to benefit from the most recent developments in the ML area when new algorithms and approaches are created. As a result of providing a systematic and automated method for managing the whole machine learning lifecycle, MLOps is a game changer and is changing machine learning engineering.

Written By: Naveen Kumar Athmakuri | Senior Software Engineer


Read more:


  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … & Zheng, X. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467. 
  2. Alshenqeeti, H. (2014). Interviewing as a data collection method: A critical review. English linguistics research, 3(1), 39-45. Andrew, N. (2022, December 16). Machine Learning Engineering for Production (MLOps) Specialization. Retrieved January 3, 2023, from 
  3. Battina, D. S. (2019). An Intelligent Devops Platform Research and Design Based On Machine Learning. training, 6(3). Braun Virginia & Clarke Victoria (2006) Using thematic analysis in psychology, Qualitative Research in Psychology, 3:2, 77-101, DOI: 10.1191/1478088706qp063oa 
  4. Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care – Addressing Ethical Challenges. N Engl J Med. 2018 Mar 15;378(11):981-983. doi: 10.1056/NEJMp1714229. PMID: 29539284; PMCID: PMC5962261. 
  5. Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. (3rd Ed.). Thousand Okas, CA: Sage. 
  6. Garg, S., Pundir, P., Rathee, G., Gupta, P. K., Garg, S., & Ahlawat, S. (2022). On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps. 
  7. Hewage, N., & Meedeniya, D. (2022). Machine Learning Operations: A Survey on MLOps Tool Support. 
  8. John M. M. , H. H. Olsson and J. Bosch, “Towards MLOps: A Framework and Maturity Model,” 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Palermo, Italy, 2021, pp. 1-8, doi: 10.1109/SEAA53835.2021.00050. 
  9. Kreuzberger, D., Kühl, N., & Hirschl, S. (2022). Machine Learning Operations (MLOps): Overview, Definition, and Architecture.
  10. Lemp, J. D., & Kockelman, K. M. (2012). Strategic sampling for large choice sets in estimation and application. Transportation Research Part A: Policy and Practice, 46(3), 602-613. 
  11. Niemelä, P., Silverajan, B., Nurminen, M., Hukkanen, J., & Järvinen, H. M. (2022). LAOps: Learning Analytics with Privacy-aware MLOps. In CSEDU (2) (pp. 213-220). OpenAI, 2023. GPT-4 Technical Report, 
  12. Palmes, P. P., Kishimoto, A., Marinescu, R., Ram, P., & Daly, E. (2021). Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization. arXiv preprint arXiv:2107.01253. 
  13. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., … & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in neural information processing systems.
  14.  Sweenor, D., Hillion, S., Rope, D., Kannabiran, D., Hill, T., & O’Connell, M. (2020). ML Ops : operationalizing data science : four steps to realizing the value of data science through model operations (First edition.). O’Reilly Media. ML Ops: Operationalizing Data Science | ML Ops: Operationalizing Data Science ( 
  15. Symeonidis, G., Nerantzis, E., Kazakis, A., & Papakostas, G. A. (2022, January). MLOpsdefinitions, tools and challenges. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0453-0460). IEEE. Testi, 
  16. Ballabio, M., Frontoni, E., Iannello, G., Moccia, S., Soda, P., & Vessio, G. (2022). MLOps: A Taxonomy and a Methodology. IEEE Access, 10, 63606–63618.
To Top

Pin It on Pinterest

Share This