How MLOps can encourage experimentation and rapid delivery, help enterprises industrialise machine learning.
MLOps movement has moved from a concept to boardroom discussions on implementation. Machine learning (ML) and Artificial Intelligence (AI) are increasingly becoming key drivers of organisational performance, as enterprises are realising the need to shift to engineered performance to more efficiently move ML models from development through to production and management.
Today, AI and ML leaders better understand the MLOps lifecycle and the procedures and technology required for deploying new models into production.
Numbers say it all:
- 50 per cent of AI experts use standard development tools and frameworks to create AI models (McKinsey 2020)
- 73 per cent of business leaders believe that MLOps adoption will keep them competitive (Forrester 2020)
- 24 per cent think MLOps could propel them to the industry leader pedestal (Forrester 2020)
- $4 billion In annual revenue is projected to be generated by MLOps platforms by 2025 (Deloitte 2021)
- On average, 40 per cent of companies said it takes more than a month to deploy an ML model into production, 28 per cent do so in eight to 30 days, while only 14 per cent could do so in seven days or less. (2020 State of Enterprise ML)
But many organisations are weakened in their efforts in experimentation and collaboration between product teams, operational staff, and data scientists. IDC reports 28 per cent of AI/ML projects fail, with a lack of necessary expertise, production-ready data, and integrated development environments cited as the primary reasons for failure.
Some are constrained by artisanal development and deployment techniques. Typically, these models are developed and deployed using manual, customised processes that are unscalable.
To realise the broader, transformative benefits of AI and ML, MLOps, also known ML DevOps, is imperative. Back in the day, DevOps transformed the way IT teams manage software, enabling them to dramatically improve development efficiency, delivery schedules, and software quality. Today, it’s AI’s turn for the DevOps treatment.
MLOps is an approach that automates ML model development and operations, aiming to accelerate the entire model life cycle process.
Like DevOps, MLOps features automated pipelines, processes, and tools that streamline all steps of model construction. Through continuous development, testing, deployment, monitoring, and retraining, MLOps can improve collaboration among teams and shorten development life cycles, enabling faster, more reliable, and more efficient model deployment, operations, and maintenance.
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Encourage experimentation
With automation and standardised processes, MLOps can encourage experimentation and rapid delivery, helping enterprises industrialise machine learning. New techniques and approaches, supported by better data organisation for use by machines, can reduce process time of customising and adjusting the way models learn to generate accurate outcomes, known as model tuning. To help ensure that the best processes are industrialised and scaled, teams can reevaluate and automate existing processes for creating, managing, and curating the data, algorithms, and models at the heart of machine-driven decision-making.
Once models have been deployed to production and begin encountering more data, monitoring their performance can help ensure they continue to deliver business value.
MLOps helps organisations monitor model performance and manage model drift’s predictive inaccuracies by helping standardise processes for maintaining alignment of AI models with evolving business and customer data, according to Deloitte Tech Trends 2021 report.
Human ML experts can monitor production models, observe how they change and behave as they scale, and decide when they need to be retrained or replaced. As a result of this planning and monitoring, model drift is diminished, and development and deployment become more flexible and responsive.
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Scale model development and deployment
Bringing the discipline of DevOps to machine learning can help AI adopters scale model development and deployment, but they must also tackle a significant skills gap. In a recent Deloitte study, 68 per cent of executives surveyed described their organisation’s skills gap as “moderate-to-extreme,” with 27 per cent rating it as “major” or “extreme.” Typically, enterprises rely on a small number of highly skilled data scientists and analysts to develop and test complex ML models and then deploy them to a production setting.
But relying on a few experts has limits. As machine learning permeates the enterprise, a more scalable, efficient, and faster approach is needed to improve development resilience, reduce production bottlenecks, and increase the reach of ML projects. MLOps practices encourage communication between expanded development and production teams. It’s a deeply collaborative approach, enabling larger teams to work more efficiently in a standardised manner.
MLOps helps address emerging challenges
Machine learning spawns complex, data-related issues such as accountability and transparency, regulation and compliance, and AI ethics. For example, ML models often make predictions that drive decisions related to loan applications and other consequential matters. These require model and algorithm transparency to shed light on how and why these decisions are made. There may also be privacy and consent issues related to both training and production data sets. And because ML systems often use sensitive personal information, data protection may further need to meet regulatory compliance standards, such as HIPAA, PCI, or GDPR.
Another challenge is the use of biased data, which sometimes developers can unintentionally build their own biases into algorithms and models.
MLOps can help organisations manage such dilemmas by establishing and enforcing program-level guardrails that can drive accountability as a baseline requirement.
Within a robust MLOps framework, development and deployment teams will find it easier to adhere to governance and compliance protocols and privacy and security regulations.
MLOps tools can automatically record and store information about how data is used, when models were deployed and recalibrated and by whom, and why changes were made.
As enterprises seek to scale AI development capacity from dozens to hundreds or even thousands of ML models, MLOps can help automate manual, inefficient workflows and streamline all steps of model construction and management, but organisations likely will also need to infuse AI teams with fresh talent, to extend focus from model building to operationalisation.
When armed with MLOps tools and processes, these expanded AI teams likely will be better able to address challenges related to accountability and transparency, regulation and compliance, AI ethics, and other issues related to managing and organising data for machine-driven decision-making.
As a bonus, this approach enables data scientists to focus on experimenting and innovating with new AI technologies that go beyond core techniques, enabling organisations not only to scale ML initiatives but to be more operationally resilient and agile in the face of technological change.
