The MLOps Strategy

The MLOps Strategy

A clear MLOps strategy can allow organisations to extract much business utility from Machine Learning. Let’s find out how.

The increased application of Artificial Intelligence in business processes over the course of the past few years (and accelerated heavily by the COVID-19 pandemic) has given birth to several promising new technologies for optimising business performance for organisations across the globe. Among these, for firms employing machine learning algorithms at scale, perhaps none look as propitious as the novel MLOps technology.

No Ops like MLOps

A compound of machine learning and IT operations, MLOps is a rather nascent discipline aiming to productise machine learning algorithms for businesses based on collaboration between IT professionals and data scientists. According to research from US-based Cognilytica, the market for aforementioned solutions could rise from $350 million to a mammoth $4 billion by 2025. To truly realise its business value, however, one must recognise how exactly MLOps, “born at the intersection of DevOps, data engineering, and machine learning”, differs from traditional DevOps in terms of execution.

Combining a wide array of skillset specialisations brought forth by (i) data scientists in mathematics, algorithms, developer tools and simulations and (ii) operations administrators in production deployments, upgrades, security and resource and data management, MLOps primarily aims to deliver new algorithms and models seamlessly, at scale and without incurring any downtime.

In fact, tech conglomerate VentureBeat opines: “A robust data strategy enables enterprises to respond to changing circumstances, in part by frequently building and testing machine learning technologies and releasing them into production. MLOps essentially aims to capture and expand on previous operational practices while extending these practices to manage the unique challenges of machine learning.” This is especially useful given that production data can change rather swiftly, and ML algorithms can continuously retrain on new data based on previously seen scenarios. This can, in effect, make all the difference between a suboptimal and an optimal prediction.

Take the use of MLOps in Tech giant Nvidia, for example: “developers running jobs on internal infrastructure must perform checks to guarantee they’re adhering to MLOps best practices. First, everything must run in a container to consolidate the libraries and runtimes necessary for AI apps. Jobs must also launch containers with an approved mechanism and run across multiple servers, as well as showing performance data to expose potential bottlenecks.”

The ML Pipeline

Given the range of elements involved with MLOps, roadblocks are often imminent and need to be met head on: such as in data scientists having to re-tweak various model features (like models and parameters), managing codebases for reproducible results, engaging in model validation, unit testing as well as integration testing.

Therefore, it is rather necessary for organisations to have their strategy and business objectives in order before setting up their MLOps framework. These objectives, VentureBeat writes, “typically come in the form of KPIs, can have certain performance measures, budgets, technical requirements, and so on.” The next step is to identify data sources and model selection, followed by data preparation (such as cleaning) and processing (such as feature extraction). The data-prep process, is of course, of utmost importance, given the results of a recent survey which found that almost 87% of AI professionals felt that inherent biases in their data led to discriminatory results and biases in the analyses.

The preparation stage is followed by model training and experimentation through testing across distributed architectures. “Once machine learning pipelines are built and automated, deployment into production can proceed, followed by the monitoring, optimization, and maintenance of models.” Governance is crucial at this juncture to ensure quality controls and ethical AI norms.

In effect, MLOps governs almost every aspect of the Machine learning life cycle, allowing business interests to come to the fore and data scientists to work with ‘clear directions and measurable benchmarks.

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