2022-10-28    Share on: Twitter | Facebook | HackerNews | Reddit

MLOps roles of the future

Discover the future of MLOps specializations, including Explainable AI/MLOps, Federated Learning/Edge MLOps, Reinforcement Learning/MLOps, AI/ML in IoT and IIoT, Model Explainability and Fairness.

As the field of MLOps is still relatively new and evolving, there are likely to be new specializations that will emerge in the future. Here are a few potential areas of specialization that may become more prominent in the future:

1. Explainable AI/MLOps

As the use of machine learning models in critical decision-making applications increases, the need for explainable AI and interpretability will become more important. MLOps engineers with expertise in this area will be responsible for ensuring that models are transparent and accountable, and that their decisions can be understood and explained.

2. Federated Learning/Edge MLOps

Federated learning is a technique that allows multiple devices to train a model simultaneously while keeping data on device. Edge computing is becoming more prevalent, and as a result, MLOps engineers with expertise in federated learning and edge computing will be in high demand to ensure that models can be deployed and managed in these environments.

3. Reinforcement Learning/MLOps

Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions and observing the rewards/consequences. It is likely that more companies will start to use reinforcement learning in their products, so MLOps engineers with expertise in this area will be needed to ensure that these models can be deployed, monitored and maintained in a production environment.

4. AI/ML in IoT and IIoT

Internet of things (IoT) and Industrial internet of things (IIoT) are growing rapidly. With this, there is a growing need to deploy and manage machine learning models on edge devices and gateways in order to analyze and act on data generated by connected devices. MLOps engineers with expertise in this area will be needed to ensure that models can be deployed and managed in these environments.

5. Model Explainability and Fairness

With the growing concern about bias and fairness in AI models, there is an increasing need for MLOps engineers with expertise in model explainability and fairness. They will be responsible for developing and implementing techniques to ensure that models are fair and transparent, and for identifying and mitigating sources of bias in the data and model.

Any comments or suggestions? Let me know.