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A short class with Sarah and her students. The other ways are under More.
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Let's wrap up by discussing the overall importance of deployment in IoT. Why is it vital?
Because it’s the phase where insights from data are transformed into actionable decisions.
Exactly! Real-time actions can prevent failures and improve efficiency. Can you think of what the future may hold for deployment in IoT?
Improved algorithms that require less computational power for edge deployment!
Yes! As technology advances, the efficiency of both cloud and edge deployments will enhance, making IoT smarter and more responsive.
And better data handling will help with accuracy!
Absolutely! Innovations in deployment will shape the effectiveness of IoT systems in the years to come.
Large, compute-heavy models run on cloud servers, where they can analyse long histories and data from many devices at once.
Small, optimised models run on devices or gateways, so decisions happen locally in milliseconds, even when the network is slow or down.
Weigh latency, bandwidth, connectivity, privacy and the device's computing power when deciding where a model runs.
Many IoT systems combine both, with the edge reacting instantly and the cloud handling heavy analysis and retraining.
Changing conditions slowly make a deployed model less accurate, even though the model itself has not changed.
Track a deployed model's accuracy against real outcomes, retrain it with fresh data when accuracy drops, then validate and roll out the update.
Factory motor protection: a small model on the machine's gateway stops a motor the moment its vibration looks abnormal, while a larger cloud model studies months of vibration data from every machine to plan maintenance.
Smart irrigation: a soil-moisture node decides on the spot whether to open a valve and sends only a daily summary to the cloud, where the model is retrained at the end of each growing season.
Wearable heart monitor: the device flags an irregular heartbeat within seconds and uploads only the alert and a short recording, which saves bandwidth and keeps most raw health data private.
Smart building climate control: a model trained on winter data starts making poor predictions in summer; monitoring spots the drop in accuracy, so the model is retrained on recent data and pushed back to the building's controllers.
Heavy thinking in the cloud, quick reactions on the ground; keep on watching for the drift, and retrain when it's found.
A farm's smart pump used to wait for the cloud before switching on. One day the internet went down during a heatwave and the crops began to wilt. The farmer moved a small model onto the pump so it could decide by itself, kept the big forecasting model in the cloud, and set up monthly checks so the model was retrained as the seasons changed.
Brain and Reflex - the cloud is the brain that thinks deeply but slowly, and the edge is the reflex that reacts instantly without waiting for the brain.
D.E.A.L. - Deploy where the decision is needed, Edge for speed, Analyse heavy data in the cloud, Look out for drift.
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What happens in the deployment phase of the ML pipeline?
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When is cloud deployment the better choice?
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When is edge deployment the better choice?
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What is concept drift?
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Why must deployed models be monitored?
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Putting a trained and validated model into a live system so that it can make predictions on new data as the data arrives.
Running a trained model on fresh input, such as a new sensor reading, to produce a prediction or a decision.
Hosting a model on remote cloud servers, which suits large models and analysis that combines data from many devices.
Running a smaller, optimised model on the IoT device itself or on a nearby gateway, so that it can act locally.
A local device that connects nearby sensors to the network and can run edge models on their behalf.
The delay between a reading being captured and the system acting on it; a round trip to the cloud adds latency.
The amount of data a network link can carry; edge processing saves bandwidth by sending only results or alerts.
The gradual loss of a deployed model's accuracy because the real-world patterns it learned have changed.
The deployment phase in the ML pipeline is crucial for implementing machine learning models in IoT systems, ensuring they make real-time decisions either locally on devices or through the cloud.
In the deployment phase of the ML pipeline for IoT, models are launched to operate either in the cloud for heavy computations or at the edge for real-time decision-making. Each approach has its advantages, such as reduced latency and bandwidth use for edge models, while continuous monitoring is essential to maintain model accuracy over time.
In this section on deployment within the Machine Learning (ML) pipeline for IoT systems, we explore how models transition from training to action. Two primary deployment methods are highlighted: Cloud Deployment and Edge Deployment.
Large ML models that require significant computational resources are executed in the cloud. This setup is beneficial for processing extensive datasets and performing complex calculations.
Conversely, smaller models are deployed directly onto IoT devices or gateways, enabling immediate, localized actions, such as shutting down a malfunctioning machine based on abnormal sensor readings. This approach minimizes latency and conserves bandwidth, facilitating real-time responses that are critical in IoT applications.
After deployment, continuous monitoring is necessary to combat 'concept drift', where the model's accuracy may degrade over time as environment conditions change. Retraining the model with current data is often required to ensure it remains effective.
In summary, deployment is a vital step in the ML pipeline that determines how well IoT systems can leverage collected data to trigger smart actions efficiently.