Top trends in MLOps implementation, 2023

The MLOps market is predicted to reach a market value of $5.9 billion by 2027, growing at a compound annual growth rate (CAGR) of over 41%*. With the latest adoption trends, MLOps is no longer a luxury but is a necessity for any business that wants to scale its ML adoption. Enterprises to small and medium-sized businesses are adopting MLOps practices to streamline their ML workflows and improve the efficiency of their AI applications.

Here are some key trends to watch out for in MLOps adoption.

1. Automated ML Pipelines: End–to–end process automation from deployment to managing the ML models, leveraging tools across each stage, will help organizations to productionise their ML models & derive business value from their ML adoption.
2. Adaptable & affordable infrastructure: While infrastructure in the past has been a limitation for small businesses to scale their ML adoption, infrastructure advancements such as containers provide scalable and portable ML infrastructure. The availability of more affordable GPUs on the Cloud has made the adoption of more complex ML algorithms widespread.
3. Deriving value from ML models with continuous monitoring & management: Companies earlier focused on proving ML adoption to management by building smaller, short-lived use cases. However, the focus now is scaling these use cases & driving business ROI from them. Therefore, monitoring deployed ML models continuously to detect performance degradation, data drift, and concept drift and taking proactive actions for model maintenance is critical.
4. Increasing need for understanding models with Explainable AI: As businesses start adapting ML on a large scale, making it essential to critical processes, interpretation of these by utilizing techniques such as model explainability, interpretability frameworks like SHAP and LIME, and rule-based decision systems, will become crucial, driving the need for Explainable AI in MLOps.
5. A paradigm shift in Automation processes, the convergence of MLOps & DevSecOps: With ML adoption becoming mainstream for businesses, a collaboration between data scientists, ML engineers, and operations teams will be crucial, leading to the convergence of DevOps principles and practices with MLOps. Security being a significant concern for MLOps adoption, coupling with DevSecOps will result in a paradigm shift in how businesses implement their processes around intelligent automation.
6. More focus on Compliance and Ethical considerations to make MLOps mainstream: As businesses include ML as part of their mainstream processes, they’ll need to start focusing on model explainability, bias detection, and fairness assessment. Compliance with regulations like GDPR and CCPA, incorporating privacy-preserving techniques, will need to be embedded into the core of MLOps implementation.
7. MLOps process will need to mature to manage ML for Edge Computing: As businesses start to differentiate their offerings, they’ll need to focus on real-time processing, reduced latency, and improved data privacy for their ML models. In addition, to manage ML deployed on edge nodes, MLOps will need to mature to handle model deployment, monitoring, and updates in resource-constrained environments.
8. MLOps will need to focus on leveraging diversified data: With complex & varied use cases being explored, businesses will need to start leveraging federated learning techniques to train ML models on distributed data sources without centralizing the data, ensuring privacy and security while benefiting from diverse data sources.
9. To build scale, businesses will need robust model versioning and experiment tracking: Businesses will need to ensure reproducibility & model iteration while building better collaboration within the teams, for which they will need to implement robust versioning and tracking mechanisms for ML models and experiments.
10. Rapid advancements in cloud-native technologies like AWS SageMaker, Google Cloud AI Platform, and Azure Machine Learning to simplify and streamline the end-to-end MLOps process. Businesses will need to develop Cloud expertise to scale to derive the maximum out of their ML journey; they will need to leverage multi-cloud & hybrid cloud for MLOps to optimize costs & gain scalability. At the same time, they set up cloud-agnostic practices to manage the ever-growing complexity.
11. Adoption of MLOps Platforms and Frameworks: Utilizing specialized MLOps platforms and frameworks such as TFX (TensorFlow Extended), MLflow, and Seldon for end-to-end ML lifecycle management, including data preparation, model training, deployment, and monitoring, will be crucial for businesses to take the next step in their ML journey.

A report from Deloitte states that companies that implemented MLOps successfully were 10 – 20% more productive in their ML journey. Also, Gartner predicts that in 2023, 3/4th of the companies successful in developing AI models will adopt MLOps to improve the performance of their AI applications.

Considering the above trends, organizations are bound to increase their investments in MLOps tools, platforms & talent. And this is not limited to large organizations, Small and medium-sized businesses are also adopting MLOps practices to streamline their ML workflows and improve the efficiency of their AI applications. Google Cloud AI platform, their MLOps platform, saw 350% growth in 2021, while there has been a 200% increase in the no of MLOps job postings in the last two years.

At goML, we help businesses scale their MLOps efforts by 10X with our Speed & Efficiency enablers. Set up a 30-minute call to understand how we could help scale your MLOps journey. https://calendly.com/d/z7d-sqp-c3x/30min?month=2023-06

*(https://www.marketsandmarkets.com/PressReleases/mlops.asp).