Enterprises in quest of full business benefits of AI look forward to MLOps implementation. That’s why the need for MLOps and its market size is just growing and growing. IDC predicts that nearly 60% of enterprises will have operationalized MLOps by 2024 to streamline ML workflows. With the advent of large language models (LLMs) such as GPT3 and BERT, MLOps serves its specific use case, LLMOps. So there is no question that we need MLOps. But the question is, how do we implement it? Whether organisations get MLOps right?
While implementing MLOps, one needs to address sustainability measures to drive long-term returns.This blog discusses the sustainability aspects of MLOps, ways to achieve it, and best practices. We have also touched upon large language model operations sustainability at the end of this blog.
MLOps - A Little Background Story
Now we can see ML is almost everywhere. But a decade ago, machine learning was in the experimental stage, and only a few brands attained AI success by productionalizing their models. The transition from research-PoC to development and deployment wasn’t easy, with limited computing abilities, lack of standardisation, and repetitive tasks.
Deloitte defines this phase as the era of artisanal AI marked by the lack of scalable patterns, tooling, and practices. The tech and DS community considered DevOps their initial inspiration to get AI success. However, DevOps could serve as a partial approach to streamlining ML projects, as software and ML development are fundamentally different. What next? Enter MLOps, and that stole the entire show in the AI space.
MLOps, as its name shows, combines two terms - machine learning and operations. It brings a set of practices to deploy and manage ML models reliably and efficiently. These practices help unify the ML development process, streamline and standardise the continuous delivery of performant models, and enable better team collaboration.
With the advent of large language models such as ChatGPT, Bard, GPT4, and BERT, we need another specialised and brand-new discipline of MLOps, LLMOps. It is evolving yet, and much research is required on its sustainable and effective implementation.
Sustainable MLOps - A Quick Introduction
Sustainable software development is familiar to us. Although the scope for software development is not limited, MLOps sustainability takes you beyond these traditional approaches. And the reason is ML sustainability is not just about building a model but operationalizing a complete ML system by implementing regular iterations and improvement strategies.
Damian A. Tamburri, in his research paper Sustainable MLOps: Trends and Challenges, defines MLOps sustainability and a research roadmap to achieve it. He addresses the sustainability of AI system as the ability to continuously perform while meeting these three operational objectives:
- Ability to maintain operationality
- Ability to improve ML solutions with organisational structure
- Ability to enhance intended social function without any friction
Why Do We Need Sustainable MLOps?
MLOps is a good start for your AI initiatives. With its adoption, you can leverage the power of sophisticated algorithms with sustainable processes around their use. It's an instrumental tool that helps you democratise ML, empower teams, and as a result, elevate business impact. In short, MLOps has no choice while aligning and scaling your ML efforts to solve the biggest challenges.
Sustainable MLOps practices are essential for these reasons:
Scalability: As the data volume and the complexity of models grow, it becomes challenging to manage ML models and their associated infrastructure. Adopting innovative MLOps practices ensures the scalability of inference services, related processes, and teams.
Robustness: Sustainable machine learning operations ensure that ML models are reliable, accurate, and performant over time. This is particularly important for critical applications like healthcare and finance, where the stakes are high. Some of the useful approaches are:
- Thorough testing of models to identify any vulnerabilities
- Model monitoring to address issues in real-time
- Ensuring models are robust to changes in data and environment
Transparency: Sustainable practices promote transparency and accountability in developing, deploying, and maintaining ML models. This helps enterprises build trust with stakeholders and comply with regulatory requirements.
Ensuring that the entire lifecycle of ML models is fully documented and tracked brings accountability to your ML projects. Explainable and interpretable models, regular AI audits help your stakeholders understand how the models work, how they are being used, and how they are inferring.
Responsiveness: Sustainable MLOps enables organisations to respond quickly to dynamic business requirements and fluctuating market conditions. You can reduce the time-to-market by automating processes and accelerating iterations. This is essential in today's fast-paced, competitive business landscape.
Efficiency: By integrating sustainable practices, you are empowered to optimise resources and reduce waste. Some examples could be automating repetitive tasks, using cloud infrastructure, and adopting agile development methodologies.
Addressing Sustainability in MLOps
We discussed the need for MLOps and its sustainable implementation. Let's move ahead with the actual plan now.
Damian A. Tamburri highlights ML sustainability on these four frontiers - explainability, fairness, accountability, and sustainability.
Explainability: Model explainability is an essential practice that helps understand ML predictions and explain them in human words. Suppose you are an AI-powered insurer whose model denies a specific claim. Model explainability would help you here to suffice this denial with the right reasons, such as why the claim could be false.
Explainable AI or XAI benefits all AI-driven sectors but is critical for highly moderated domains like healthcare, finance, and insurance.
Fairness: Hybrid AI White Paper elaborates on AI fairness by combining technical and soft fairness. Technical fairness here refers to mitigating discrimination and bias, using thresholds to a biased model and additional performance metrics during training. Whereas soft fairness arises from jurisdictional and social sciences research, enforcing regulatory policies and social norms—equality and equal opportunity for all data points at any quality level.
Accountability: This is a broader concept. Let's simplify with an example. Suppose your creditworthiness assessment model makes discriminations and denies a particular ethnicity. This might limit the opportunities for qualified candidates of specific races. Accountability helps address this issue, eliminate discrimination, and comply with ethical and legal standards.
Sustainability: Lastly, understanding sustainability as a sustainable MLOps frontier. This is quite conspicuous with the above three frontiers. Once explanations back your ML decisions and help you attain the required fairness, you bring accountability to ML systems.
Collectively all three parameters help you satisfy the MLOps sustainability definition provided in the top section of this blog.
- Improved operational efficiency with better tracking, documenting, and accountability
- Enhanced ML solution with feedback loops, proper explanations, and continuous monitoring
- Improved social function frictionlessly by complying with legal and ethical standards and fairness
Best Practices To Drive MLOps Sustainability
These are some of the crucial practices to attain your MLOps sustainability goal.
Robust data management: Develop a robust data management strategy that includes data cleaning, labeling, and version control. This will ensure that data is accurate, reliable, and up-to-date, which is crucial for building high-quality ML models.
Sustainable data management includes capabilities to extract data from diverse sources, quality and integrity checks, data transformations like aggregations, feature engineering, and more.
Flexible ML pipelines: ML pipeline orchestration is complex and requires consideration of data pipelines, transformations, hyperparameter selection, model versioning, and metadata management.
Infrastructure: Use cloud infrastructure to provision resources and scale workloads dynamically. Cloud infrastructure allows enterprises to manage costs more effectively and avoid overprovisioning or underprovisioning resources.
Automation: Automate repetitive tasks like data preprocessing, model training, and deployment using tools like Jenkins, Airflow, or Kubeflow. Automation saves you time and resources and reduces the risk of human error.
Not just monitoring, complete observability: Set up monitoring and alerting mechanisms to track ML models and detect anomalies in real time. Automated drift detection is essential to plan retraining and redeployments. AI observability platforms such as Censius help you go beyond mere monitoring with a 360-degree view of model performance and capabilities to explain and analyze ML outcomes.
Collaboration: ML development is a team effort. The more you foster team collaboration, the better are the results. How about using agile development methodologies, version control systems like Git, and collaborative tools like Jupyter notebooks?
Continuous improvement: ML practitioners strive for continuous improvement and deployment. Some useful techniques are A/B testing, experimentation, champion-challenger deployments, and feedback loops.
The following table summarises ML operations, recommended practices and tools.
A Note On LLMOps Sustainability
With large language models skyrocketing in ML markets, the need for large language model operations is getting highlighted. We saw transitions from DevOps to MLOps. And now it's time to see the rise in LLMOps.
Enter LLMOps. Its scope would include model tuning and optimization, no-code LLM deployment, GPU access and resource optimization, experimentation, data synthesis, pipelines, and augmentation.
Critical challenges in large language model operationalization are model size, complex dataset management, and the need for continuous monitoring and retraining.
Some of the approaches to bring sustainability to LLMOps include:
- Optimise the training process itself. Try using smaller batch sizes, reducing the number of training iterations, and utilising transfer learning techniques to reduce the required training.
- Sharing pre-trained models can increase sustainability by reducing the need for multiple enterprises to train their models from scratch.
- Trying techniques such as model pruning, quantization, and compression to optimise (and primarily reduce) the model size.
- Choosing low-code or code-first platforms that help you fine-tune, deploy, and version LLMs seamlessly. Proper platform selection allows you to handle infrastructure issues behind the scene.
- You can take other steps towards sustainable LLMOps by using specialised hardware/chips designed explicitly for GPUs & TPUs, and renewable energy sources.
This comprehensive blog discussed the need for sustainable MLOps, ways to achieve it, and the best practices. While concluding this blog, we covered LLMOps sustainability with crucial practices that help in large language model operations.
If you’re looking for MLOps consultation, resources, or expertise, Censius is your one-stop solution for your MLOps, and AI observability needs. Look at our MLOps resources - MLOps wiki, MLOps Guide, and tools.