In this post, I will explain how to use AWS Sagemaker. Even if you do not have experience with this AWS service, this guide will help understand AWS Sagemaker step by step.

What is AWS Sagemaker?

AWS Sagemaker is a new web service that AWS offers. It helps to build, train and deploy machine learning models at any scale.  Basically, Sagemaker does the heavy lifting of machine learning and as a developer or data scientist, you can focus on building and training your model.

Major Benefits of AWS Sagemaker

• You can easily fetch or store data from other AWS Services
• Highly Scalable. This again relates to my earlier point by being able to connect to other AWS Services.
• Does heavy lifting of ML algorithms – Fast training

Details Of Machine Learning and Sagemaker

Machine learning is literally machine learning about something. Nevertheless, that something can be anything that humans are usually good at or bad at. Machine Learning provides an ability for systems to learn and improve from experience.

In another way, you can say a system with a feedback loop. A system performs functions, gathers data along the way, uses that data to improve the functions it is performing.

Building a model

Sagemaker makes it easy to connect with AWS Services like S3, Database. Sagemaker also includes Juypter notebooks. These notebooks make it easier to visualize data.

Sagemaker also offers a set of algorithms pre-installed. Sagemaker also comes in with preconfigured TensorFlow or Apache MXNet.

Training and Deploying a model

I will show later in this post how we can train a model in Sagemaker with a single click. The important thing to note here is that you can easily train a model for petabyte-scale in Sagemaker. With continuous improvement, Sagemaker can also improve the performance of the model.

Once you train and tune the model in Sagemaker, it is easy to deploy the model in production. Sagemaker deploys the model on an auto-scaling cluster of EC2 instances.

A simple example of using AWS Sagemaker

1. Once, you log in to the AWS console, access Sagemaker service. Select Notebook Instances and create a Jupyter Notebook instance as shown below:

2. On the next page, keep the most default settings as shown. You will need to create an IAM role for S3 bucket creation. If you don’t have that role, you can create it while selecting the role.

3.  Once you select the role, click “create a notebook instance” and it will create a notebook instance. It will take few minutes before it will show it is running. Once the notebook instance is running, click “open” and it will open Jupyter notebook in another tab.

4. Select notebook environment as conda_python3 or anything that you want to use.

Once you have the notebook opened, you can use python or the language of your choice to build a model. For the model, you can easily fetch data from S3 or relational databases from AWS service.

I will not be showing that part in this post. But if you want to refer to a good example, you can visit this post here.

Conclusion

In this post, I showed how one can use AWS Sagemaker to build and train the model for machine learning.

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In this post, I will show how we can use the API Gateway pattern with Spring Cloud. With microservice architecture becoming more and more useful, it has become equally complex how to handle calls to the microservices.

The purpose of microservices is to decouple the application into loosely coupled microservices that can interact with clients and with each other easily.

Importantly, the ease of development and deployment make microservices easier to design based on specific needs.

API Gateway Design Pattern

When the enterprise architecture scales, it becomes complicated with the number of microservices. Clients can directly call these microservices, but there are a few challenges

1. Each client has to make a request to the exposed microservice API. In many cases, it might have to make multiple server round trips. As a result of this, it increases network latency.
2. Every microservice has to implement common functionalities like authentication, logging, and authorization.
3. It becomes harder to change microservice without affecting the client. In reality, the client doesn’t need to know microservice and its implementation behind.

To address these issues, the architecture now contains another layer between the client and the microservices. This is API Gateway.

API Gateway acts like a proxy that routes the request to the appropriate microservices and returns a response to the client. Microservices can also interact with each other through this Gateway.

Usage of API Gateway

There are a few functionalities that API Gateway provides.

Routing

The major usage of API Gateway is routing the request from the client to the appropriate server or microservice. Particularly, API Gateway hides the implementation of API from the client.

Common Functionalities

API Gateway can also implement extra common functionalities and in-process reducing the load from microservices.  These common functionalities include logging, authentication, authorization, load balancing, response caching, retry policies, circuit breakers, rate limiter.

Different API Gateways

There are a number of API Gateways available and one can use any of these based on the needs.

• Netflix API Gateway (Zuul)
• Amazon API Gateway
• Mulesoft
• Kong API Gateway
• Azure API Gateway

Overall, which API Gateway to use will depend on your use case. But the most of these gateways provide options to scale, flexibility and support.

In this demo, I will be showing how to use spring-cloud-starter-netflix-zuul library for Netflix API Gateway.

Example of API Gateway with Spring Cloud

However, we will develop two microservices. We will also build an API Gateway using Spring Cloud. This API Gateway will act as a reverse proxy to route to either of the microservices.

So let’s create the first microservice. This will contain a CustomerController like below:

@RestController
@RequestMapping("/customer")
public class CustomerController
{
    @GetMapping("/total")
    public List customers()
    {
        List list = new ArrayList<>();
        list.add("Microsoft");
        list.add("Amazon");
        list.add("Apple");
        return list;
    }
}

This microservice will be running on port 8081. server.port=8081.

Now, let’s create another microservice. This will contain VendorController like below:

@RestController
@RequestMapping("/vendor")
public class VendorController
{
    @GetMapping("/total")
    public List vendors()
    {
        List list = new ArrayList<>();
        list.add("CJI Consultants");
        list.add("Signature Consultants");
        list.add("Deloitte");
        return list;
    }
}

This microservice will be running on port 8082. server.port=8082

API Gateway with Spring Cloud

After all, we will create an API Gateway using Spring Cloud. We need to include the following dependencies:

dependencies {
	implementation 'org.springframework.boot:spring-boot-starter-thymeleaf'
	implementation 'org.springframework.boot:spring-boot-starter-webflux'
	implementation 'org.springframework.cloud:spring-cloud-starter-gateway'
	testImplementation 'org.springframework.boot:spring-boot-starter-test'
	testImplementation 'io.projectreactor:reactor-test'
}

Note the dependency of spring-cloud-starter-gateway. Nevertheless, we will need a RouteLocator type bean to route our requests. This is where we add the configuration in our Api Gateway.

package com.betterjavacode.apigatewaydemo.config;


import org.springframework.cloud.gateway.route.RouteLocator;
import org.springframework.cloud.gateway.route.builder.RouteLocatorBuilder;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class SpringCloudConfig
{
    @Bean
    public RouteLocator gatewayRoutes(RouteLocatorBuilder routeLocatorBuilder)
    {
        return routeLocatorBuilder.routes()
                .route("customerModule", rt -> rt.path("/customer/**")
                        .uri("http://localhost:8081/"))
                .route("vendorModule", rt -> rt.path("/vendor/**")
                        .uri("http://localhost:8082/"))
                .build();

    }
}

As shown above, this configuration bean builds a RouteLocator to route requests related to two modules. Also, note that our gateway service is running at port 8080. If a request is initiated with a gateway address, the API gateway will route it to the appropriate service.

Demo

Let’s start out our microservices and API Gateway service. Two microservices are running on ports 8081 and 8082. The API gateway service is running on port 8080.

Now if I access http://localhost:8080/vendor/total, I will get the list of vendors as follows:

If I access http://localhost:8080/customer/total, I will get the list of customers as follows:

 

Conclusion

Conclusively, I showed how to use API Gateway with Spring Cloud. API Gateway is an important design concept. With an increasing number of microservices, it becomes important to have a common pattern that can handle a lot of the common workload of these services, and API Gateway helps with that.

My book Simplifying Spring Security is currently on discount sale if you are interested.