![deploying deep learning models with docker and kubernetes deploying deep learning models with docker and kubernetes](https://miro.medium.com/max/1022/1*gvTnFoP2v3qE0tDE3y_vRA.png)
- DEPLOYING DEEP LEARNING MODELS WITH DOCKER AND KUBERNETES HOW TO
- DEPLOYING DEEP LEARNING MODELS WITH DOCKER AND KUBERNETES UPDATE
Sample = [raw_sample.culmenLength, raw_sample.culmenDepth, raw_sample.flipperLength, \ Self.model = load('./models/logistic_regression.joblib')ĭef prepare_sample(self, raw_sample: PenguinSample): Self.model = load('./models/random_forest.joblib') Self.model = load('./models/decision_tree.joblib')
DEPLOYING DEEP LEARNING MODELS WITH DOCKER AND KUBERNETES HOW TO
That is done using the Python framework Flask.įrom sklearn.preprocessing import StandardScalerįrom sklearn.model_selection import train_test_splitĭef _init_(self, test_size = 0.2, scale = True):ĭata = self._feature_engineering_pipeline(data) These engineers don’t have to know only how to apply different Machine Learning and Deep Learning models to a. We want to build API using which the client-side of the application can get predictions from the model.
![deploying deep learning models with docker and kubernetes deploying deep learning models with docker and kubernetes](https://image.slidesharecdn.com/deepcloudarchitecture-161107003403/95/deploying-deep-learning-models-with-docker-and-kubernetes-34-638.jpg)
What we want to create in this article is the Web server, which serves a model for Iris predictions. The Body – This section contains information that the client sends to the server.List of valid headers on MDN’s HTTP Headers Reference. The Headers – The headers are used to provide additional information to both client and server in a form of property-values pairs.
DEPLOYING DEEP LEARNING MODELS WITH DOCKER AND KUBERNETES UPDATE
![deploying deep learning models with docker and kubernetes deploying deep learning models with docker and kubernetes](https://blog.ovhcloud.com/wp-content/uploads/2020/04/548C09AD-B622-411D-B02A-644C7AECDDAB.jpeg)