📌 Model Compression Summary
Model compression is the process of making machine learning models smaller and faster without losing too much accuracy. This is done by reducing the number of parameters or simplifying the model’s structure. The goal is to make models easier to use on devices with limited memory or processing power, such as smartphones or embedded systems.
🙋🏻♂️ Explain Model Compression Simply
Imagine you have a huge, heavy textbook but you only need a small summary to remember the main points. Model compression is like creating that summary for a machine learning model, so it is easier to carry around and use. This means the model can still do its job well, but it takes up less space and works faster.
📅 How Can it be used?
Model compression can help deploy AI features on mobile apps where speed and storage are limited.
🗺️ Real World Examples
A company wants to use voice recognition on its smart speakers. By compressing the speech recognition model, the device can process commands locally without sending data to the cloud, making it faster and more private.
A healthcare provider uses compressed deep learning models on portable medical devices, enabling them to analyse patient data in real time during remote visits, even with limited hardware resources.
✅ FAQ
📚 Categories
🔗 External Reference Links
Ready to Transform, and Optimise?
At EfficiencyAI, we don’t just understand technology — we understand how it impacts real business operations. Our consultants have delivered global transformation programmes, run strategic workshops, and helped organisations improve processes, automate workflows, and drive measurable results.
Whether you're exploring AI, automation, or data strategy, we bring the experience to guide you from challenge to solution.
Let’s talk about what’s next for your organisation.
💡Other Useful Knowledge Cards
Token Liquidity Optimization
Token liquidity optimisation is the process of making it easier to buy or sell a digital token without causing big changes in its price. This involves managing the supply, demand, and distribution of tokens across different trading platforms, so that users can trade smoothly and at fair prices. By improving liquidity, projects help ensure their tokens are more attractive to traders and investors, reducing risks like price swings and slippage.
Model Calibration Metrics
Model calibration metrics are tools used to measure how well a machine learning model's predicted probabilities reflect actual outcomes. They help determine if the model's confidence in its predictions matches real-world results. Good calibration means when a model predicts something with 80 percent certainty, it actually happens about 80 percent of the time.
Customer-Facing Process Redesign
Customer-facing process redesign means changing the way businesses interact with their customers to make things easier, faster, or more enjoyable for them. It involves reviewing and improving steps that customers experience directly, such as placing orders, getting support, or making returns. The goal is to remove obstacles, reduce waiting times, and create a more satisfying journey for the customer.
Proof of Elapsed Time
Proof of Elapsed Time, often shortened to PoET, is a consensus mechanism used in blockchain networks to decide who gets to add the next block of transactions. It relies on trusted computing environments to randomly assign wait times to participants. The participant whose wait time finishes first gets to create the next block, which helps ensure fairness and energy efficiency compared to systems that require lots of computing power.
Change Readiness Assessment
A Change Readiness Assessment is a process used to evaluate how prepared an organisation, team, or group of people are for a planned change. It involves identifying strengths, weaknesses, and any potential obstacles that might impact the success of the change. The assessment helps organisations plan support, training, and communication to make the transition smoother and more effective.