Why AIOps and MLOps lead the road to AI Maturity

Both artificial intelligence (AI) and machine learning (ML) promise to revolutionise the way we work and how companies operate. Many companies and IT departments are investing considerable sums in AI solutions and platforms to increase automation, make data easier to work with, and more. Yet many challenges remain when developing, testing, deploying, and ultimately using AI/ML solutions. The popular DevOps methodology, however, may help increase efficiency, scaling, and time to deployment.

Over the last 15 years, DevOps has emerged as a solution for combining development-oriented operations (the “Dev”) with IT activities (the “Ops”). By using DevOps, teams can maintain and increase efficiency while ensuring high-quality results. DevOps also helps accelerate the development cycle while maximising output.

The DevOps approach centres around Agile software development - a leading approach to developing software, adding functionalities, sourcing feedback and input. DevOps merges Agile with continuous, collaborative, and ultimately iterative cycles, while also leveraging automation and self-service configuration. This leads to the continuous building and integration of new solutions coupled with automated release management and on-going, typically incremental, testing.

As for the DevOps toolbox, solutions include tools for coding, development communication, version and release control, and log monitoring. Processes, artefacts, tasks/milestones, and testing are all managed as well. DevOps also establishes an infrastructure for provisioning software and overseeing deployments. Add it all up, and DevOps allows teams to rapidly build and develop software. DevOps also supports high-quality and transparent testing, deployment, and management.

Ultimately, DevOps makes the entire software development and deployment cycle more efficient and robust while also shortening timelines. Many software development and engineering firms now incorporate DevOps methods and approaches into their core business processes.

With machine learning and artificial intelligence now advancing at a rapid rate, DevOps may prove just as useful in emerging technology development.

Using DevOps Principles to empower AI and ML development

Both machine learning and artificial intelligence development diverge considerably from traditional software engineering and development. Of course, there are some similarities, but the differences between the fields and corresponding approaches are vital to consider. Anyone looking to apply DevOps to ML or AI must keep this in mind.

First, it is essential to understand what ML and AI are currently used. While the future possibilities for applying AI or ML are nearly endless, current applications often focus on a few key areas, such as identifying patterns and extracting insights from data.

Many organisations and teams within organisations are now trying to extract more value and knowledge from data. However, the quantities of data collected are so vast that it’s difficult, if not impossible, for humans to sort through the info in a timely and efficient manner.

DevOps can help companies put ML and AI to work and aid engineers trying to build robust applications. Still, the model must be adjusted to the specifics of these advanced fields and must also take into consideration the assortment of stakeholders in both ML and AI.

Consider that some individuals will come from scientific backgrounds, others from data and statistics, and still others from business operations. Add in software engineers, IT support, end-users, and there to take into

Let’s dive deeper into the main differences.

Defining MLOps and AIOps

Artificial intelligence and machine learning are complicated fields, and any application of the DevOps methodology must be tailored to these technologies.

MLOps refers to using DevOps to bring a machine learning solution to production. The tools, culture, and processes that interconnect everything must be applied to the nuances of machine learning. This includes but is not limited to CI/CD (continuous integration/continuous development), data reliability and integrity, decoupling, monitoring, and scaling. Ideally, MLOps will lead to rapid deployment and robust systems. MLOps focuses exclusively on the machine learning operational pipeline.

AIOps continues the DevOps revolution. In many ways, DevOps was driven by cloud services and the increasing orchestration of software. AIOps allows companies to bring machine learning and AI to bear, solving Ops challenges, such as capacity management and reliability. AIOps also increases automation—already an essential part of DevOps. In sum, AIOps can improve IT operations.

Both MLOps and AIOps are essential for engineers. AIOps, in particular, can leverage the spectrum of revolutionary AI technologies to solve IT challenges and also make working with data more manageable. For example, AIOps can detect data anomalies, increase storage and computing capacity, or even assist more mundane tasks, such as securely resetting passwords.

AIOps is especially crucial given that IT departments are often short on resources (if not starving for them). Even as technologies proliferate, it’s difficult finding the labour-power and skills needed to put various technologies to use. It’s hard even for experts to stay on top of new technologies and applications, such as cloud services, NoSQL databases, microservices, and new architectural approaches. AIOps can increase efficiency and also achieve scale by equipping IT departments with the tools and resources they need to excel.

AIOps is coming at the perfect time. With complex multi-cloud and hybrid systems proliferating, IT teams need to put every resource into productive use. Through continuous automation, risk assessment, and predictive analysis, IT teams can reduce logjams and even prevent problems before they arise.

Still, there remains a sevfurthermotreere gap between the potential of artificial intelligence, machine learning, and the reality of current platforms and solutions. As an emerging field, AI and ML will both continue to advance, with more powerful, and flexible solutions coming into the market. Through the near future, however, challenges will remain considerable, but rarely insurmountable.

Challenges to AIOps and machine learning

Artificial intelligence is a cutting-edge field and could revolutionise the technology industry. That said, developing, deploying, and utilising AI solutions is easier said than done. Furthermore, many solutions that work in the lab often struggle at scale. More data coming from more sources, more integrations through the cloud, and limited human and technological resources can strain even well-developed, efficient AI solutions.

Add in the fact that IT departments, data scientists, data and software engineers, business users, and AI/ML programming teams often work in different silos. While these many departments may all be working with and on the same solutions, using them day-to-day, communication between silos may be limited.

DevOps methodologies and philosophies can help, but challenges remain. And the challenges continue to pile up. Consider that many organisations share models across the entire organisation, including across more teams and departments. And if third party and outside models are adopted, complexity only increases. Multiple models in various versions stretched across multiple sources make model versioning more complex and harder to manage.

As a result, many companies are struggling to justify or fully realise their return on both AI and ML investments. For many, AI and ML offer excellent opportunities and concepts, but for now, limited real-world impacts.

The problem is, ultimately, two-fold. First, there are issues related to the technologies themselves. An organisation needs a complete, unified platform that ties everything together, from data science and management to production. For this to occur, AI platforms must include powerful, reliable functionalities from deployment to serving, while also allowing for effective monitoring and governing.

Yet the technology tools are not enough by themselves. Companies must also adopt the right mindset, meaning organisations and people need to put in place the processes, oversight, training, and skills to support artificial intelligence and machine learning. This includes everything from basic data practices, to also how organisations and people interpret and use insights and results.

AIOps is certainly a nascent concept, and there will be both failures and successes going forward. However, AI is also likely to emerge as a powerful if not foundational technology that will allow organisations in a wide range of sectors to increase efficiency and output.

Using the IBM Frameworks: Why is ML-Ops important?

According to IBM, ML-Ops are a crucial part of an efficient enterprise and necessary for deploying AI and data science at scale as well as repeatable. Here are IBM’s four dimensions of this process:

Engineer

Having the right platform and architecture to orchestrate and integrate the various components is crucial in building a strong foundation. Quickly setting up and maintaining multiple codebases and collaborate amongst virtual teams avoids frustration, increases efficiency and quality.

These foundations make sure you build CD/CI pipelines you can trust as well provide easy access to logging and tracking changes - adding up to the traceability and transparency of models.

Deploy

Scalable and flexible architecture has come with cloud providers and come in flavours of the pure cloud solutions or on-prem deployed clouds.

Depending on the type of solution you need, for either security or quick scalability, your platform needs to be able to handle and combine it all.

With the ever-growing complexity of these systems and continuous delivery, platforms like the ones from IBM, help you keep it all under control and automate a lot of the repetitive tasks to have your team focus on more valuable tasks.

Monitor

When working with complex Machine Learning models, deployed across multiple departments in organisations, you want to know how these models perform and what results they produce.

We also need to understand how a model scores against our targets, tweak the parameters of a model, and quickly iterate through the process, getting faster results.

Traceability and transparency are essential features a platform needs to offer, for both governance and compliance.

The ability to look-int-the-box and understand how outcomes came to be, helps to build trust in the results of the models used in the business decision-making process.

Trust, Transparency and Fairness

Machine Learning models work based on statistics and generate outcomes with probabilities, in which they are different from traditional programs, which apply just the pure logic of Yes or No, but not a maybe.

Traditional reporting, as an example, a company’s balance sheet, is a mathematical summation and grouping of financial items, always producing the same outcome, which is pretty easy to understand for analytical people.

Since models are based on probabilities and the data they are fed, a lot of parameters define how a model calculates a result.

This complexity and uncertainty require traceability and transparency, the ability to look-into-the-box and understand how outcomes came to be.

For both governance and compliance, these are important features a platform needs to offer.

How to set up your team and infrastructure to move from a POC to a scalable product

IBM’s Data Intelligence Platform is a great example of how a platform should support both proprietary as well as open-source tools, and easily integrate with your current existing technology stack.

We listed a few of them per category, to give an understanding of how important integration between the various tools is. This is just a glimpse out of the supported tools, and is a non-exhaustive list:

• Code repository, for versioning and training code - Git, Bitbucket, and GitLab….
• CI/CD Pipeline - Jenkins, GitLab, Bamboo….
• Container Registry - Sonatype Nexus, GitLab, and git…
• Processing Engine - docker, Dataiku, Kubernetes…
• Data/Model Store - Oracle, Dataiku, and Amazon S3…
• Meta Data Analysis - IBM Watson OpenScale, DataRobot, tableau…
• Inference Service - Kubernetes, Dataiku, IBM Watson Studio…

In conclusion

Embracing an intuitive IT infrastructure gives enterprises a competitive edge now and in the future. With predictive analysis capabilities, scalability, real-time insight, and continuous learning, AIOps and MLOps will continue to prove their worth.