The importance of continuous learning from the huge amount of generated data in the face of constant and unpredictable technological changes has never been higher.
Artificial Intelligence (AI), Machine Learning (ML), and data science or data engineering have evolved a lot in the past 15 years, and it’s essential to understand the factors influencing their rapid development. One such factor is DevOps’s evolution, which has steered machine learning and artificial intelligence development toward new heights.
This article will go through several aspects of IT Development Operations (DevOps), Artificial Intelligence for IT Operations (AIOps), Machine Learning Operations (MLOps), etc. Below is a list of topics we will cover, and if you would like to know how your business can benefit from this, please read till the end.
DevOps: Development + Operations
DevOps is a conceptual framework for integrating/re-integrating the development and operations of Information Systems. It combines software development and IT operations to shorten the system development lifecycle and continuously deliver with higher precision and accuracy.
The cost and time for releasing software have decreased drastically due to the software as a service (SaaS) concept. This has led to higher competition, and organizations or businesses that can release software early with a higher frequency of updates can gain a competitive edge.
DevOps tools and methods help organizations to achieve this competitive edge. A significant reason is that DevOps is supported by a culture of automation, information sharing, collaboration, measurement, and web service usage.
DevOps does not just have a positive effect on software development or deployment but also on quality assurance performance. It adds value, quality, continuous innovation, and other vital areas for a business to excel.
Some of the top companies are using DevOps to gain a competitive advantage. For instance, Amazon, with its move to Amazon Web Services, Netflix through its Simian Army, and Target used DevOps for the development of Target Circle Saving, which was previously known as the Cartwheel project. Walmart incorporated OneOps for automating and accelerating development and many other examples.
But wait, you might think these are just big names. Are there any SMEs using this approach? Oh yes, there are. In recent research on adopting DevOps by ECS Digital, UK, over 57% of SMEs reported incorporating DevOps tools and methodology into their daily routine.
This article will explore DevOps’s role in artificial intelligence and machine learning development. This is where terms such as AIOps and MLOps come into the picture.
What are AIOps and MLOps?
AIOps: Artificial Intelligence for IT operations
AIOps for IT development brings together human and artificial intelligence, a blend of machine efficiency, precision, and scalability, with a human touch. It is what AIOps stands for in IT development.
Using Big Data, analytics, and advanced machine learning, an AIOps solution helps to enhance and optimize IT operations, including but not limited to automation (especially for repetitive tasks), monitoring, event correlation, cause determination, anomaly detection, service desk, etc.
Why is this so important, you wonder? Revolutionary AI technologies are changing operations processes and require sophisticated interpretation from various data sources. This growing complexity of different layers of technologies that form the backbone of IT and the increased set of dependencies between other technologies and rapidly growing business services are beyond the point of comprehension for humans. We need a machine to intervene and make it convenient for us.
MLOps: Machine Learning Models Operationalization Management
Machine learning operations, MLOps, or ML Ops, aim to deploy and sustain ML models in the workflow processes with reliability and efficiency. Deploying a Machine Learning model into the workflow requires massive data that would prove nearly impossible for a single person to manage or keep track of. MLOps processes come in handy in this scenario.
MLOps can keep track of tweaked parameters, aid in debugging machine learning model(s) operation, ML lifecycle, and help adapt to the changes to data in real-time. They comprise various components such as security, infrastructure management, governance, model monitoring, model version control, model serving and pipelining (or ML Pipeline), model registry, model training, model validation, and model service catalog for all deployed models.
For business, MLOps tools have a significant role to play in terms of deploying ML solutions efficiently. According to a 2021 report by DataRobot, over 87% of businesses struggle with the lengthy timelines of model deployment. Moreover, for over 64% of businesses in multiple domains, a single model deployment takes at least a month, if not more.
Such staggeringly low efficiencies were observed even with a significant increase in ML budgets of over 86% of organizations in 2021. This scenario is observed because businesses underestimate the complexity and challenges of deploying ML to the workflow. Thus MLOps is a must for efficiently deploying Machine Learning for your business.
AIOps vs. MLOps: essential differences to know
Machine learning is a subfield of Artificial Intelligence. Hence they are closely related and connected. Therefore the challenges faced in their development and the tools used in many cases are similar. When we look for differences between AI and ML, we look into their interconnections. The same goes for AIOps and MLOps. There is a lot of overlap.
In the era when organizations throughout the globe are looking to improve operational efficiency, primarily through automation technologies, AIOps and MLOps are increasingly becoming more and more popular.
AIOps and MLOps are both quite distinct, involving various technologies and procedures. At the same time, both play a vital role in supporting businesses to achieve operational efficiency.
One of the significant differences between AIOps and MLOps is their objective. On the one hand, AIOps aims to provide a consolidated view of what’s happening in the IT environment through data from all available sources. On the other hand, MLOps is more focused on providing predictive analysis and real-time insight by creating machine learning models.
Looking at their applications, AIOps are used for anomaly detection, application monitoring, automated resolution, root cause analysis, IT systems and operation automation, and managing and processing large amounts of data. MLOps control source code and multi-source data consumption standardizes the ML system development process and streamlines collaboration between teams and stakeholders.
Summing up the differences and providing an overview, we can say that AIOps automates machines, whereas MLOps standardizes processes.
Influence of DevOps on ML/AI development
AI and ML have extended the capabilities of software apps that we use today in our day-to-day life. From digital assistants to computer vision to generate image alt text, we have AI and ML solutions all around us.
Technology has advanced a lot, and today the main challenge to integrating AI and ML into an application is not about the technology itself. Instead, the challenge is to deploy these models in a production environment while keeping them operational and supportable.
On the one hand, we have expert teams capable of delivering business applications. On the other, we have AI/ML experts who can develop algorithms and models that can help add value to these applications for businesses. The most important is implementing both, with specific automation and good practices. It is where DevOps plays an important role.
AI and ML development have their methodologies thanks to the DevOps concepts, AIOps, and MLOps. This creates a perfect environment for incorporating the operational and deployment practices that AI/ML projects require.
AIOps and MLOps use predictive analytics to locate, examine, and create data. It may boost client loyalty and lifetime value by providing a customized experience and recommendations. It also gets simpler to spot ineffective practices, eliminate them, and manage time and resources well. Hence the benefits of integrating DevOps in AI and ML go beyond just the development or deployment.
Due to the numerous advantages of deploying AI and ML in key areas, such as helping businesses operate more quickly by making production faster, effectively, and at scale, managers can now address complex problems that call for a different strategy. The need for AI and ML solutions is rising, and many enterprises seek a better understanding of enterprise applications in different environments.
A Gartner survey of almost 200 businesses conducted in 2020 found that 66% left their AI investments alone during the epidemic, while 30% chose to raise their AI funding. Therefore, compared to other cutting-edge technologies, AI/ML is more relevant and can offer higher-quality solutions. Consequently, it lowers a company’s total costs, especially related to the operations team.
Small but significant advances in business might be made by AI and ML, possibly changing the global economy. These advances include technology that combines big data with other solutions, deep learning, machine learning pipelines, MLflow models, MLflow model registry, model building, model training, model monitoring, model validation, continuous delivery and integration (CI-CD), continuous automation, big data applications, and more.
Every aspect of society has been impacted by ML, including healthcare, transportation, and education. It is anticipated that the ML market will take off shortly, and key metrics for success will also evolve with it. By 2030, the ML market is expected to grow to USD 183.89 billion at a CAGR of 44.1 per cent, according to EIN News.
How is the DevOps approach different from traditional ML/AI Development?
Before the advent of DevOps, both development and operations used to work in complete isolation. The only exception is the software release. Since the introduction and use of the DevOps approach in ML/AI development, this culture has changed. No longer should developers wait long for a release before adding new features. Instead, continuous integration and delivery provide new features every day.
An analysis by the Digital Enterprise Journal found that during 2018, there was an 83 per cent growth in the number of businesses employing or contemplating adopting MLOps and AIOps capabilities. AI/ML may enable DevOps teams to concentrate on creativity and innovation by eliminating inefficiencies throughout the operational life cycle and helping teams to handle the amount, velocity, and diversity of data.
Below are some key points that show why the -Ops approach is better than the traditional one:
Culture of COLLABORATION
Within a company, the -Ops approach influences the organizational culture. It has been found by several studies in the science and practice that organizations where the -Ops approach is followed strive to break down the cultural divide between the different departments and promote collaboration.
The -Ops approach helps development teams to incorporate customer requirements more efficiently and effectively. Teams might gain from working in an agile or iterative environment using the -Ops strategy. Development teams have improved in agility and productivity during the past ten years owing to the -Ops, much to the benefit of the customers.
DevOps can be utilized for all aspects of a business. However, it is most frequently linked to development and operations. Before the program is constructed, specifications must be made, and customer expectations must be defined. Once it’s built and deployed, validation, customer education, and giving developers feedback are all crucial.
In light of how quickly a platform gets developed, -Ops makes it easier for teams to handle complex problems like linear trends, massive databases, questionnaire refining, and continuously coming up with new ideas. Thus providing complete accountability.
How do AIOps and MLOps tools benefit software development?
AIOps and MLOPs, two critical DevOps components, make projects run more smoothly and efficiently. MLOps is mainly utilized for machine learning projects and pipelines, whereas AIOps aids in the automation of the whole development process.
AIOps automates incident resolution by learning from and adapting to problems as they arise. Consequently, AIOps may launch particular activities to provide remediation and, sometimes, prevention, while MLOps provides seamless rollout. AI and machine learning improve the performance of DevOps teams by automating repetitive procedures and eliminating inefficiencies throughout the SDLC. According to Markets-and-Markets, the global AIOps platform market will increase at a 34.0 per cent CAGR from $2.55 billion in 2018 to $11.02 billion in 2023.
MlOps also improves the administration of ML projects. It integrates machine learning into systems’ development, design, and maintenance. MLOps accelerate the product life cycle and generate actionable insights. According to Statista, third-party MLOps solutions have lower yearly infrastructure costs than organizations that construct their own. A third-party solution will cost roughly 4.5 million US dollars, but creating the key from scratch will cost around 5.5 million US dollars.
AIOps and DevOps work together to improve interaction and efficiency across development, production, and operations teams while keeping the customer in mind. According to a 2020 research performed by 451 Research, more than half of all DevOps professionals now use AIOps and MLOps.
What do machine learning and data scientists have to say about it?
As AIOps and MLOps are still evolving, we thought of giving you a brief prospect about what the scientific literature has to say about it. In the following two sections, you will see some critical extracts from recent studies that are important to know from a business perspective.
According to Lena Reiter (FH Wedel, Wedel, Germany), the three main categories of challenges about AIOps that are highlighted in the literature: are data management, human interaction with AIOps, and implementation of artificial intelligence.
Several scientists, such as Martin Andenmatten, have forecasted that AIOps will be used increasingly in the coming years. Businesses should start as soon as possible to adopt them to maintain an edge over their competitors.
Anton Gulenko and colleagues have forecasted that businesses cannot cope with future challenges without using AI solutions.
Paulo Notaro and colleagues found fault detection is one of the major applications for which AIOps is being used.
Yangguang Li and colleagues have predicted that self-healing would be the next big focus for AIOps.
Several Scientists such as Zhuangbin Chen, Adnan Masood, and their colleagues have highlighted that with AIOps, there is a significant improvement in error handling, a significant reduction in error rate, and the service-level agreements are shortened.
Anna Levin and colleagues have found empirical proof that the AIOps approach increases performance, availability, stability, service quality, engineering productivity, and customer satisfaction and also results in cost reduction
Ioannis Karamitsos and colleagues found that the software versions arising from continuous integration with upgrades and new features reach end users more quickly.
Phillipp Ruf and colleagues emphasize that MLOps is much more sophisticated and incorporates additional data and multiple model procedure(s).
In their book, Sridhar Alla and Suman Kalyan Adari highlight the importance of custom MLOps solutions for businesses to extract the maximum possible advantage.
Georgios Symeonidis and colleagues concluded that a fully mature ML Ops system with continuous training could lead to more efficient and realistic machine learning algorithm(s) or models and use ML models in production. Furthermore, selecting the appropriate equipment for each activity is critical.
Nipuni Hewage and Dulani Meedeniya found that although there are various ML Ops platforms, most have constraints in completing ML life-cycle phases and giving an automated framework. Hence a customer MLOps solution is advisable.
Recent developments in AI Ops and ML Ops
Some of the most prominent recent AIOps and MLOps include:
- From Siloed to Collaborative and Continuous MLOps: A shift from developing models in siloed research environments to a more collaborative and continuous MLOps approach exists. This involves automating tasks like data integration, application logic, security, and continuous upgrades, making the process more efficient and effective.
- Adoption of AutoMLOps: Enterprises are increasingly adopting AutoMLOps, which automates various aspects of the ML lifecycle, like parameter injection, workload distribution, and security hardening. Open-source solutions like MLRun and Nuclio play a significant role in supporting this transition.
- Centralised MLOps Platforms: Large companies are realizing the need for centralized MLOps platforms to scale Data Science across their business. This involves automating deployment processes, training, serving, and monitoring, to streamline operations and focus on data science.
- Building MLOps Skillsets: There is a growing realization of the need for specific skill sets, like those of ML Engineers, to implement effective MLOps strategies. This is in response to the gap between required skills and available talent in the field.
- Google’s Vertex AI: Vertex AI contributes significantly by offering various solutions for complex aspects of MLOps. It also emphasizes auxiliary and forward-thinking solutions such as explainable AI and responsible AI.
What can you do for your business?
As the uproar over AI/ML rises, businesses seek ways to employ technology to enhance productivity. Despite more significant research and implementation costs, AI/ML is more profitable to a corporation by lowering overall production costs and increasing profitability. However, there needs to be caution here.
Spanish philosopher George Santayana said, ‘Those who cannot remember the past are condemned to repeat it. Several businesses have already made the mistake of adopting a ready-made solution and faced problems in the adoption base. Hence it’s essential to understand the needs of your business before deploying models or solutions that are already available. At DAC.digital, we always begin with a discovery workshop to help curate our business clients’ and collaborators’ needs and requirements.
Data scientists or a data science team, data engineers or data analytics specialists with the skills needed for AI solutions, deploying MLOps and AIOps solutions, AI development or a machine learning solution are hard to find. Science and practice both support that more and more businesses, including large enterprises, prefer outsourcing. It would be beneficial for your business to explore the same.
Reach out to us for an optimal solution for your business.
Dr. Yash Chawla
R&D Communications Advisor, DAC.digital | Asst. Professor, Wroclaw Tech.
Yash is a researcher, academician, and consultant in the field of marketing and innovation management, with keen interest in marketing communications, creative thinking, and sustianable development. He strives to make a positive difference for students, professionals and the society.