That advantage is gained through the development of business intelligence, predictive analytics, and other data-driven insights that enable better decision making and inform the creation of innovative new products and services. For that reason, many technology transformation projects include ML because of its aspirational potential. Problem is, according to Gartner, 85% of AI and ML projects fail because, too often, AI and ML are used tactically, and not as part of a strategic initiative. As a result, Gartner reports that only 53% of AI and ML projects ever make the leap from proof-of-concept to production. To address this challenge, machine learning operations—known as MLOps—has emerged as a vital approach to help organizations maximize the success of their ML investments, and to provide a framework for that important technology to mature into an integral part of business operations.

What is MLOps and why does it matter?

MLOps is an approach for adopting and applying machine learning applications in enterprise production environments so that they can be easily integrated across business and IT operations. When mapped to an organization’s business model and objectives for developing business intelligence and predictive analytics, MLOps establishes a framework of best practices and centralized management that allows the organization to automate process that are vital to the successful deployment of ML tools and applications.

That approach is important because, without MLOps, results will mirror those observed by Gartner and fail to meet expectations. Those failures will be because of inconsistent and inaccurate outcomes, or improvements that are merely incremental and not significant enough to justify additional investments and full production.

What are the benefits of MLOps?

ML should be a transformational technology, and MLOps helps to assure organizations that it will be. With a strategy that is mapped to an organization’s needs, MLOps can ensure that investments in machine learning tools deliver measurable results through faster, more efficient data and analytics operations, and greater accuracy of outcomes for data-dependent processes like business intelligence development, predictive analytics, and process automation.

Ultimately, organizations that use MLOps as the framework for deploying, integrating, and using ML tools will find that their ROI is accelerated. Those results will be realized through operational efficiencies and competitive advantages associated with better, more timely decision-making based on more precise BI. And as more ML tools are added, lessons learned through use of and experimentation with new tools and process under an MLOps strategy will allow organizations to integrate those new tools more quickly.

MLOps Best Practices

Because machine learning is a technology that evolves and matures in unique ways for each organization when used strategically, and that will help solve problems and deliver insights specific to its operational environment, there is no rigid MLOps template. However, when formulating an MLOps framework, there are some best practices to keep in mind.

• Choose tools strategically: As with any technology transformation, your MLOps strategy should take a crawl-walk-run approach that focuses on early wins and confidence building, and the ML tools used should reflect this ideal. Before jumping into ML adoption, consider things like the size of your organization, capability of your IT operations team, and industry segment.
• Document everything: As you begin to implement your MLOps program and deploy new tools and techniques, make sure to document every aspect of your experience—both wins and losses. There is likely to be a steep learning curve with lots of experimentation and innovation taking place and capturing those processes so that successes can be easily replicated, and disappointments not repeated is essential.
• Communicate cross-organizationally: Today’s enterprises are complex and interconnected. The goal of an MLOps program is to support all aspects of the organization and to use ML to its best effect at all levels of operation. Clear communications of goals and expectations is important to maintaining unity of purpose, setting reasonable and achievable expectations, and ensuring management, end users, and IT operations are in synch.
• Validate, validate, validate: In any data-driven operation—and especially when working with machine learning tools—accuracy and consistency are of the utmost importance. Poor data quality and poor code quality will have a detrimental effect on the performance of your data models. As a part of your MLOps strategy, be sure to establish a “single source of truth” with data that is accurate and up to date; discover and validate all your sources; test your models’ performance, and; confirm results before going into production.
• Track costs: When using third party resources, like storage, compute, and data cloud services, be sure you understand your cost structure and track usage so that you operate within your budget. This is especially important in the early phases of MLOps implementation when experimentation is taking place.
• Monitor performance and recalibrate regularly: Changes to the IT estate, updated business strategies, and insights gained using analytics as your MLOps program matures will all affect the needs and outcomes of an MLOps program. While your goal is to automate as much of your operations as possible using ML tools, a key part of an ongoing MLOps strategy is to regularly monitor the performance of your processes and adjust them as necessary.

MLOps vs. DevOps

Although MLOps is not DevOps, the two practices do share similarities, and they do complement each other. In DevOps, the goal is to accelerate development of applications and services in a consistent manner, and too quickly make whatever changes are needed once the new application is deployed. While DevOps has a narrow focus on building applications, MLOps takes a broader view of operations in recognition that all the data created and collected by the organization plays a role in informing ML models, and that those models may depend on data from a wide variety of sources.

Ultimately, the insights gained through MLOps and analytics will have value for DevOps teams as they build and modify their applications. Those applications will play an important role in generating data that is used in the training of ML models as a part of the feedback loop that generates, collects, and analyzes data, creating insights that are operationalized in services and applications that, once deployed, will generate and collect more data for deeper analysis. Lather. Rinse. Repeat.

MLOps vs. AIOps

ScienceLogic defines AIOps or artificial intelligence for IT Operations, as the use of advanced analytics in the form of machine learning and artificial to automate IT operations. While both MLOps and AIOps are used to increase efficiency on productivity, there are a few differences:

• AIOps is used to automate ITOps and systems. MLOps is used to standardize the ML system development process.
• AIOps is used to automate manual or repetitive processes such as incident management, anomaly detection, and predictive remediation. MLOps is used to automate ML experiments and mitigate risks and biases in model validation.

Machine learning has a key role to play in the success of an organization’s technology transformation. Even if some tech investments include an element of machine learning, for ML to have a major and positive influence on both IT and business operations, ML must be integral to the IT estate.

Now that you know all about MLOps, learn more about AIOps»