MLOps & AIOps - How DevOps Has Influenced AI/ML Development

Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized how we work. And why not? They make our lives convenient, after all. From personal assistants to data analysis systems, AI/ML is everywhere. DevOps, a set of practices that combines software development and IT operations, had a vital role to play in AI/ML development.  

DevOps is a collection of procedures that helps a team build, test, and release new software faster and with fewer defects. It is an umbrella term for a set of methods, technology, and cultural values. It automates and unifies the activities of software development and IT teams. In this article, we highlight the role played by DevOps in AI/ML development. 

MLOps & AIOps

AIOps is artificial intelligence for IT operations. AIOps platforms utilize big data, machine learning, and other technologies to enhance IT operations. 

On the other hand, MLOps is machine learning for IT operations, that involves the core of machine learning engineering. The major function of MLOps is to streamline the process and maintain and operate them. 

With respect to the applications, they are quite varied from each other, due to their distinct capabilities: 

AIOps applications

MLOps applications

How AIOps, and MLOps tools benefit Software Engineering

AIOps and MLOPs make project functioning smooth and more effortless. MLOps and AIOps are crucial components of DevOps. While MLOps is mainly used for machine learning projects and pipelines, AIOps helps automate the entire development process. 

AIOps automates incident remediation by learning from and adjusting to issues as they occur. As a result, AIOps may initiate specific operations to provide remediation and, in certain situations, even prevent it while MLOps ensures the smooth deployment. AI and ML increase DevOps teams' effectiveness by automating repetitive processes and removing inefficiencies throughout the SDLC. According to MarketsandMarkets, the worldwide AIOps platform market will develop at a 34.0 percent compound annual growth rate (CAGR) from $2.55 billion in 2018 to $11.02 billion in 2023. 

MLOps enable better management of ML projects. It aligns machine learning into the building, designing, and maintaining systems. MLOps boost product life cycle and drive effective insights that can be used immediately. According to Statista, the annual infrastructure cost for third-party MLOps solution is significantly less than the companies building their own. A third-party solution will cost around a 4.5million US Dollars, but increases to 5.5million US dollars on building the solution from scratch. 

AIOps and DevOps collaborate to enable development, production, and operations teams to interact and work more efficiently while keeping the customer in mind. In a 2020 poll conducted by 451 Research, over half of all DevOps professionals claimed they currently utilize AIOps and MLOps.  

AIOps Tools

Splunk is an analytics and infrastructure monitoring platform that is one of the industry leaders in AIOps technologies. Splunk provides various software tools and services for finding, monitoring, and analyzing data created by machines. Application performance monitoring (APM), security analytics, compliance, and infrastructure monitoring.

Moogsoft is a well-known AIOps platform that provides services to assist IT operations run more smoothly. Moogsoft is well-known for its monitoring solutions, which enable teams to prioritize problems, assure uptime, and swiftly resolve issues, resulting in enhanced agility and lower risks.

Datadog is a cloud application monitoring platform. This AIOps platform, which is available as a SaaS, provides end-to-end traces and monitoring of servers, databases, tools, and services. It guarantees that your apps, infrastructure, and systems are always available, give the most incredible user experience, and can be monitored from any angle.  

Dynatrace is a leading cloud monitoring company. It is a technology firm located in the United States that develops AI-based solutions for monitoring and optimizing application performance, operations, infrastructure, and user experience.  

BigPanda uses AIOps to provide event correlation and automation solutions. Its domain-agnostic AIOps help teams produce better results by avoiding outages and recognizing performance issues.  

MLOps Tools

Metaflow is a Python/R-based open-source application created by Netflix that makes building and managing business Data Science projects simple. Additionally, Metaflow unifies Python-based Machine Learning, Deep Learning, and Big Data frameworks.  

Core Seldon is an open-source MLOps platform for logging, advanced monitoring, testing, scaling, and converting models into production microservices in Machine Learning workflows. Seldon has several high-level capabilities that make it simple to containerize machine learning models, evaluate their usability and security, and make them fully auditable by interacting with several services.  

Neptune.ai is an MLOps metadata repository designed for teams that conduct many experiments. All model-building metadata may be logged, stored, shown, organized, compared, and queried in one location. The MLOps platform is also used for experiment tracking, model registry, and live monitoring of machine learning processes.  

Polyaxon is an MLOps tool for experimentation and automation. Large-scale deep learning applications may be built, trained, and monitored with the aid of the platform. Polyaxon manages workloads using clever container and node management, making deep learning application development faster, simpler, and more efficient.  

Valohai is mostly interested in models, coding, and data. It enables users to run powerful cloud machines with only a single click (UI) or command (CLI & API). It may be set up on any cloud vendor or on-premise to orchestrate machines automatically.  

How is the -Ops approach better than traditional AI/ML development?

Before introducing DevOps, Development, and Operations operated in complete isolation, the collaborations happened only during the time of the software release. But with the introduction of DevOps, this culture has transformed. Developers no longer need to wait for a Release to implement new features. Instead, new features are delivered daily utilizing Continuous Integration and Delivery. 

According to a Digital Enterprise Journal study, the number of firms using or considering deploying AIOps capabilities has increased by 83 percent since 2018. By removing inefficiencies across the operational life cycle and enabling teams to manage the volume, pace, and variety of data, AI/ML may help DevOps teams focus on creativity and innovation.  

Below are some key points that shows why the -Ops approach is better than the traditional one: 

Customer Oriented

The -Ops approach allows teams to benefit from working in an agile or iterative setting. Over the last decade, development teams have become more agile and have begun producing faster. 

End-to-end Accountability

While DevOps is most commonly associated with development and operations, it can and should be used for all elements of your organization. Specifications must be established, and client expectations must be set before the software can be developed. Validation, client education, and providing feedback to developers are all vital once it's built and delivered.  

This makes it simpler for teams to deal with complex challenges like linear trends, vast databases, questionnaire refinement, and continually finding new ideas at the speed with which their platform is being implemented. 

How does -the Ops approach influence the cost of AI/ML-based software development?

AI and ML enable organizations to work more swiftly, efficiently, and effectively -all at scale- allowing managers to solve multi-dimensional challenges that demand a different approach. Costs can be reduced by automating robotics processes. New business models may be launched by automating operations with computer vision, natural language processing, and speech recognition. AI and machine learning automate system operations, track business efficiency, and identify fraud faster, among other things.  

AIOps and MLOps use predictive analytics to find, analyze, and produce data. By delivering a tailored experience and suggestions, you can increase customer loyalty and lifetime value. It also becomes easy to identify and remove inefficient procedures and effectively manage time and resources. 

According to a Gartner poll of roughly 200 enterprises done in 2020, 66 percent of corporations did not adjust their AI investments throughout the pandemic, while 30 percent opted even to increase their AI financing during the pandemic. As a result, AI/ML is seen as more significant than other cutting-edge technologies. It results in a reduction in a company's overall costs. AI and ML have the potential to make small but significant improvements in business, potentially altering the global economy. ML has touched all the segments of society, be it education, transport, healthcare services, and many more. The machine learning market is expected to take wings very soon. According to EIN News, the ML market size is estimated to reach USD 183.89B at a CAGR of 44.1% by 2030. 

To Sum Up

As the unrest around AI/ML grows, organizations are looking for ways to use it to boost their productivity. AI/ML is more valuable for a business in reducing total production costs and creating higher profits, despite higher research and implementation expenses. For any queries, drop a comment below or contact us, and we’ll be happy to answer them.