Shreya Joshi

Shreya Joshi

1572756292

Building Your Machine Learning Models with Kubernetes

You’re an AI expert. A deep learning Ninja. A master of machine learning. You’ve just completed another iteration of training your awesome model. This new model is the most accurate you have ever created, and it’s guaranteed to bring a lot of value to your company.

But…
You reach a road block, holding back your models potential. You have full control of the model throughout the process. You have the capabilities of training it, you can tweak it, and you can even verify it using the test set. But, time and time again, you reach the point where your model is ready for production and your progress must take a stop. You need to communicate with DevOps, who likely has a list of tasks to the floor that hold priority over your model. You patiently wait your turn, until you become unbearingly restless in your spinning chair. You have every right to be restless. You know that your model has the potential to produce record breaking results for your company. Why waste any more time?

There is another way…

Publish your models on Kubernetes. Kubernetes is quickly becoming the cloud standard. Once you know how to deploy your model on kubernetes you can do it anywhere ([Google cloud](https://con
AWS
sole.cloud.google.com/projectselector/kubernetes “Google cloud”) or (https://con
AWS
)

How to deploy models to production using Kubernetes

You’ll never believe how simple deploying models can be. All you need is to wrap your code a little bit. Soon you’ll be able to build and control your machine learning models from research to production. Here’s how:

Step 1- your predict code

Since you have already trained your model, it means you already have predict code. The predict code takes a single sample, fits the model with the sample and returns a prediction.

Below you’ll see a sample code that takes a sentence as an input, and returns a number that represents the sentence sentiment as predicted by the model. In this example, an IMDB dataset was used to train a model to predict the sentiment of a sentence.

import keras
model = keras.models.load_model("./sentiment2.model.h5")

def predict(sentence):
    encoded = encode_sentence(sentence)
    pred = np.array([encoded])
    pred = vectorize_sequences(pred)
    a = model.predict(pred)
    return a[0][0]

def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results

Note : To make deploying even easier, make sure to track all of your code dependencies in a requirements file.

Step 2- flask server

After we have a working example of the predict code, we need to start speaking HTTP instead of Python.

The way to achieve this is to spawn a flask server that will accept the input as arguments to its requests, and return the model’s prediction in its responses.


from flask import Flask, request, jsonify
import predict

app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def run():
    data = request.get_json(force=True)
    input_params = data['input']
    result =  predict.predict(input_params)
    return jsonify({'prediction': result})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

In this small snippet we import flask and define a route it should listen to. Once a request is sent to the server to the route /predict it will take the request argument and send them to the predict function we wrote in the first layer. The function return value is sent back to the client via the HTTP response.

Step 3 — Kubernetes Deployment

And now, on to the final layer! Using kubernetes we can declare our deployment in a YAML file. This methodology is called Infrastructure as code, and it enables us to define the command we want to run in a single text file.


apiVersion: apps/v1 
kind: Deployment
metadata:
  name: predict-imdb 
spec:
  replicas: 1 
  template:
    spec:
      containers:
      - name: app
        image: tensorflow/tensorflow:latest-devel-py3
        command: ["/bin/sh", "-c"]
        args:
         - git clone https://github.com/itayariel/imdb_keras;
           cd imdb_keras;
           pip install -r requirements.txt;
           python server.py;
        ports:
        - containerPort: 8080


You can see in the file that we declared a Deployment with a single replica. Its image is based off of the tensorflow docker image, and then runs a set of four commands in order to trigger the server.

In this command, it clones the code from Github, installed the requirements and spins up the flask server written.

*Note: feel free to change the clone command to suit your needs.

Additionally, it’s important to add a service that will expose deployment outside of kubernetes cluster. Be sure to check your cluster networking settings via your cloud provider

apiVersion: v1
kind: Service
metadata:
  name: predict-imdb-service
  labels:
    app: imdb-server
spec:
  ports:
    - port: 8080
  selector:
    app: imdb-server
  type: NodePort

Send it to the cloud

Now that we have all files set, it’s time to send the code to the Cloud.

Assuming you have a running kubernetes cluster - and you have its kube config file - you should run the following commands:

kubectl apply -f deployment.yml


This command will create our deployment on the cluster.

kubectl apply -f service.yml


Doing this command will create a service that will expose the endpoint to world . In this example, a NodePort service was used - meaning the service will be attached to a port on the cluster nodes.

Use the command kubectl get services to find the service IP and port. Now the model can be called using HTTP with the following curl command:

curl http://node-ip:node-port/predict \-H 'Content-Type: application/json' \-d '{"input_params": "I loved this videoLike, love, amazing!!"}'

Wrapping it up - It’s Aliiiive!

Easy huh? Now you know how to publish models to the internet using Kuberentes. And, with just a few lines of code. It actually gets easier.

cnvrg.io model deployment

cnvrg.io provides an end-to-end platform that allows data scientists to manage, build and automate machine learning from research to production. One of the core features of cnvrg.io is the automation of model deployment. With just a single click, a data scientist can create a production-ready environment that can serve millions of requests to their model.

For every deployment environment, cnvrg.io will set up a Kubernetes cluster with all the tools integrated to help you monitor your models in real-time ( Promotheus , Grafana). It will track models at the system level and your machine learning model health. That way you can keep track of prediction confidence, input/output and basically any parameter you’d like.

Additionally, the cnvrg.io platform has integrated Istio for advanced A/B testing functionalities, webhooks, alerts and more. It’s so easy to use you’ll be surprised this solution wasn’t in your life earlier.

This is image title

So. Go on. Take your own models and deploy away!

Thanks for reading !

#Kubernetes #machinelearning

What is GEEK

Buddha Community

Building Your Machine Learning Models with Kubernetes
Christa  Stehr

Christa Stehr

1602964260

50+ Useful Kubernetes Tools for 2020 - Part 2

Introduction

Last year, we provided a list of Kubernetes tools that proved so popular we have decided to curate another list of some useful additions for working with the platform—among which are many tools that we personally use here at Caylent. Check out the original tools list here in case you missed it.

According to a recent survey done by Stackrox, the dominance Kubernetes enjoys in the market continues to be reinforced, with 86% of respondents using it for container orchestration.

(State of Kubernetes and Container Security, 2020)

And as you can see below, more and more companies are jumping into containerization for their apps. If you’re among them, here are some tools to aid you going forward as Kubernetes continues its rapid growth.

(State of Kubernetes and Container Security, 2020)

#blog #tools #amazon elastic kubernetes service #application security #aws kms #botkube #caylent #cli #container monitoring #container orchestration tools #container security #containers #continuous delivery #continuous deployment #continuous integration #contour #developers #development #developments #draft #eksctl #firewall #gcp #github #harbor #helm #helm charts #helm-2to3 #helm-aws-secret-plugin #helm-docs #helm-operator-get-started #helm-secrets #iam #json #k-rail #k3s #k3sup #k8s #keel.sh #keycloak #kiali #kiam #klum #knative #krew #ksniff #kube #kube-prod-runtime #kube-ps1 #kube-scan #kube-state-metrics #kube2iam #kubeapps #kubebuilder #kubeconfig #kubectl #kubectl-aws-secrets #kubefwd #kubernetes #kubernetes command line tool #kubernetes configuration #kubernetes deployment #kubernetes in development #kubernetes in production #kubernetes ingress #kubernetes interfaces #kubernetes monitoring #kubernetes networking #kubernetes observability #kubernetes plugins #kubernetes secrets #kubernetes security #kubernetes security best practices #kubernetes security vendors #kubernetes service discovery #kubernetic #kubesec #kubeterminal #kubeval #kudo #kuma #microsoft azure key vault #mozilla sops #octant #octarine #open source #palo alto kubernetes security #permission-manager #pgp #rafay #rakess #rancher #rook #secrets operations #serverless function #service mesh #shell-operator #snyk #snyk container #sonobuoy #strongdm #tcpdump #tenkai #testing #tigera #tilt #vert.x #wireshark #yaml

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Nora Joy

1604154094

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
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Education industry

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**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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