during inference, inference latency, etc. Using the sensitive tweet classifier example, during the deployment phase the user will be concerned about the inference efficiency and should be aware of what is expenditure on their data-centers, hence any efficiency gains are very significant. Enabling On-Device Deployment With the advent of smartphones, Internet-of-Things (IoT) devices (refer to Figure 1-5 for the deployed, to learn from a larger more accurate model (teacher) which might not be suitable for deployment. The larger model is used to generate soft labels on the training data, and the student model learns

Now, let’s say that the performance threshold for a given model to be considered feasible for deployment is a classification accuracy of 80%. Any additional improvement is not required, and we would prefer acceptable for deployment (it meets 80% accuracy target). Whereas, among the models with the learning techniques, four models with the smallest being the 150 KB model are valid deployment candidates. Learning performance when compared to a single model, but now we have multiple models which also multiplies our deployment costs. Hinton et al.18, in their seminal work explored how smaller student networks can be taught

machine learning framework that accelerates the path from research prototyping to production deployment. Learn more at PyTorch

embedding Classification Matching Wordpiece Keras tokenizer Jieba Hanlp Model Saving Deployment Testing Vocab Sequence labeling Huawei tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful

在线机器学习-模型服务部署 • 模型评估 • 模型上线部署前指标评估 • 周期使用验证样本进行点击率预估 • 待部署模型与线上模型进行指标对比，评估是否满足上线条件 • 一键部署 • 基于K8S的deployment模式，一键端口分配与模型服务部署 • 基于ZK的服务发现，一键进行流量灰度与发布 • 性能优化 • 通信优化：特征请求与模型计算单元化，在线样本格式压缩 • 计算优化：基于SSE／AVX

Figure 6-7: An example of few-shot learning with a large language model. One of the prominent deployment of such models is the GitHub’s Copilot software9 where GPT-3 is used for auto-completing code snippets

happens that after training a model with decent accuracy, the environmental constraints restrict the deployment of the solution for practical purposes. What we want for the reader to take away after reading

and bigger models. Although they improved the quality of the solutions, the bigger models posed deployment challenges. What good is a model that cannot be deployed in practical applications! Efficient