This is an article about the hosting of automation and apps in the public cloud versus the private datacenters given that source code repository and pipelines are not part of the cloud. When it comes to hosting automation and apps, there are some key differences between the public cloud and private datacenters. Let's break it down: 1. Infrastructure Ownership: o Public Cloud: In the public cloud, the infrastructure is owned and managed by a third-party provider like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Users can access and use this shared infrastructure on a payas-you-go basis. o Private Datacenters: Private datacenters, on the other hand, are owned and operated by the organization itself. This means that the organization has full control over the infrastructure and can customize it based on their specific requirements. 2. Scalability: o Public Cloud: Public cloud providers offer virtually unlimited scalability, allowing users to easily scale their resources up or down based on demand. This elasticity enables organizations to handle spikes in traffic or resource requirements without having to invest in additional hardware. o Private Datacenters: Private datacenters typically have finite resources, and scaling can be more complex and time-consuming. Organizations need to plan and provision resources in advance, which may require upfront investments. 3. Maintenance and Management: o Public Cloud: Public cloud providers handle the maintenance and management of the underlying infrastructure, including server hardware, networking, and security. This allows organizations to focus on developing and deploying their applications without worrying about infrastructure management. o Private Datacenters: In private datacenters, the organization is responsible for maintaining and managing the infrastructure, which includes tasks like hardware maintenance, security, and software updates. This requires dedicated IT personnel and can be more resource-intensive. 4. Security and Compliance: o Public Cloud: Public cloud providers invest heavily in security and compliance measures to protect customer data. They offer various security services and certifications, making it easier for organizations to achieve compliance with industry standards and regulations. o Private Datacenters: With private datacenters, organizations have more control over security measures and can implement their own security protocols. However, this also means they are solely responsible for ensuring compliance and maintaining a secure environment. It's worth mentioning that while the hosting of automation and apps can be done in the public cloud or private datacenters, the source code repository and pipelines are not inherently part of the cloud infrastructure. These components are typically managed separately, either on-premises or using cloud-based services like GitLab, GitHub, or Bitbucket. Ultimately, the choice between public cloud and private datacenters depends on factors such as scalability needs, control requirements, security considerations, and budgetary constraints. Organizations often opt for a hybrid cloud approach, leveraging both public and private infrastructure to achieve a balance between flexibility, control, and cost-effectiveness Sample automation: https://github.com/raja0034/booksonsoftwarets 

 This is a summary of the book “Anatomy of  a Breakthrough – How to get unstuck when it matters the most” written by Adam Alter and published by Simon and Schuster in 2023. This book is about the framework for getting unstuck given that everyone can get stuck including celebrities like Brie Larson, Brian Chesky and Jeff Bezos. It is full of psychological research, anecdotes and practical tips. His roadmap to getting energized for the long run positions us for our breakthrough. This involves not giving up too quickly, identifying and solving problems now, focusing on reducing anxiety, challenging ourselves in the right increments, simplifying complex problems, and remaining curious and questioning our views. We regain momentum by getting active and boosting our motivation.

Everyone gets stuck at some point in life, whether in a job, hobby, relationship, creatively or personally. Even successful individuals, such as Brie Larson, Airbnb founders, Amazon founder Jeff Bezos, and Game of Thrones fans, have experienced long periods of stuckness. People often feel isolated and believe they are victims of terrible luck, not realizing the universal experience. Psychological phenomena underpin this skewed perception, as people tend to focus on their own difficulties while overlooking those facing others. Headwinds/tailwinds asymmetry is a phenomenon that causes people to overestimate their hardships and underestimate their good fortune.

People usually get stuck in the middle of a project or when they reach a plateau. To avoid a midcourse slump, eliminate the middle as much as possible by using narrow bracketing techniques. However, be cautious of relying on these techniques for too long, as they can lose their power over time. Success takes time and effort, and people's best ideas usually come late in the process.

Identify and solve problems early to avoid trapping you later. Three common traps can hinder problem-solving: failing to see a problem exists, assuming a problem is too small to need attention, and believing the problem is too remote to matter. To avoid these traps, slow down, notice snags, and challenge assumptions. Perform frequent reviews to prevent small problems from growing too large. Focus on reducing anxiety to move beyond paralysis and avoid perfectionism. Instead of aiming for perfection, strive for excellence and set achievable standards. Avoid the expectation of complete originality and focus on finding optimally distinct ideas. Challenge yourself in the right increments, not too much too fast. Researchers found a sweet spot in the ratio of success to failure, where one failure out of every five or six attempts is optimal. Accept failure as a natural part of progress and view it as a signal for stepping out of your comfort zone and engaging with challenges that foster learning.

Challenges can come with hardships, such as discomfort or fear. To increase your capacity to undertake challenges, apply the "hardship inoculation" approach, which involves a small dose of a virus before full exposure to the disease. This approach can help overcome discomforts like fear and disappointment.

Simplify complex problems by conducting a "friction audit," which helps identify friction points that cause bottlenecks, struggles, waste, or failure. By removing or simplifying these points, you can move towards a solution.

Remaining curious and questioning your views can reduce the chances of getting stuck. Experimenting to reveal new techniques and strategies can lead to breakthroughs. Questioning and wondering can help revive curiosity and help you learn.

Regain momentum by becoming active and boosting motivation. Focus on taking actions where you excel, such as observing, identifying problems, and collecting data. By applying these strategies, you can overcome obstacles and move forward in life.

 

Integer Arrays A and B are grid line co-ordinates along x and y-axis respectively bounded by a rectangle of size X * Y. If the sizes of the sub-rectangles withing the bounding rectangle were arranged in the descending order, return the size of the Kth rectangle.                          

   

   public static int getKthRectangle(int X, int Y, int K, int [] A, int[] B){
        int[] width = calculateSideLength(A, X);
        int[] height = calculateSideLength(B, Y);

      Arrays.sort(width);
        Arrays.sort(height);
        int start = 1;
        int end = width[width.length-1] * height[height.length-1];
        int result = 0;
        while (start <= end) {
            int mid = (start + end)/2;
            if (greater(X, Y, mid, width, height) >= K) {
                start = mid+1;
                result = mid;
            } else {
                end = mid - 1;
            }
        }
        return result;
    }

    public static int greater(int X, int Y, int mid, int[] width, int[] height){
        int N = width.length;
        int result = 0;
        int j = N-1;
        for (int i = 0; i < N; i++){
            while (j >= 0 && width[i] * height[j] >= mid) {
                j--;
            }
            result += N-1-j;
        }
        return result;
    }

  public static int[] calculateSideLength(int[] X, int M) {
    int[] length = new int[X.length+1];
    for (int i = 0; i < X.length; i++){
        if ( i == 0) {
            length[i] = X[i] - 0;
        } else {
            length[i] = X[i] - X[i-1];
        }
    }
    length[X.length] = M - X[X.length-1];
    return length;
}

A: 1 3  X: 6

B: 1 5  Y: 7

width: 1 2 3

height: 1 4 2

K’th rectangle size: 6
The calculation of the side length remains the same for both X-axis and Y-axis.

 

Generative Artificial Intelligence (AI) refers to a subset of AI algorithms and models that can generate new and original content, such as images, text, music, or even entire virtual worlds. Unlike other AI models that rely on pre-existing data to make predictions or classifications, generative AI models create new content based on patterns and information they have learned from training data.

One of the most well-known examples of generative AI is Generative Adversarial Networks (GANs). GANs consist of two neural networks: a generator and a discriminator. The generator network creates new content, while the discriminator network evaluates the content and provides feedback to the generator. Through an iterative process, both networks learn and improve their performance, resulting in the generation of more realistic and high-quality content.

Generative AI has made significant advancements in various domains. In the field of computer vision, generative models can create realistic images or even generate entirely new images based on certain prompts or conditions. In natural language processing, generative models can generate coherent and contextually relevant text, making them useful for tasks like text summarization, translation, or even creative writing.

However, it is important to note that generative AI models can sometimes produce biased or inappropriate content, as they learn from the data they are trained on, which may contain inherent biases. Ensuring ethical and responsible use of generative AI is an ongoing challenge in the field.

Generative AI also presents exciting opportunities for creative industries. Artists can use generative models as tools to inspire their work or create new forms of art. Musicians can leverage generative AI models to compose music or generate novel melodies.

Overall, generative AI holds great potential for innovation and creativity, but it also raises important ethical considerations that need to be addressed to ensure its responsible and beneficial use in various domains.

Some examples of text generation models include ChatGPT, Copilot, Gemini, and LLaMA which are often collectively referred to as chatbots. They generate human-like responses to queries. Image generation models include Stable Diffusion, Midjourney, and DALL-E which create images from textual descriptions. Video generation models include Sora which can produce videos based on prompts. Other domains where Generative AI finds applications are in software development, healthcare, finance, entertainment, customer service, sales, marketing, art, writing, fashion, and product design.

In Azure, a copilot can be developed with no code using Azure OpenAI studio. We just need to instantiate a studio, associate a model, add the data sources, and allow the model to train. The models differ in syntactic or semantic search. The latter uses a concept called embedding that discovers the latent meaning behind the occurrences of tokens in the given data. So, it is more inclusive than the former. A search for time will specifically search for that keyword with the GPT-3 but a search for clock will include the references to time with a model that leverages embeddings. Either way, a search service is required to create an index over the dataset because it facilitates fast retrieval. A database such as Azure Cosmos DB can be used to assist with vector search.

 This is a continuation of previous articles on IaC shortcomings and resolutions. In this section, we discuss the IP restrictions between sender and receiver in cloud resources. Let us take the example of an application gateway and app services behind the gateway.  As a layer 7 resource, http proxy and http request rewrite capabilities, the gateway routes traffic by path to different app services. These app services can allow promiscuous web traffic or specify origin ip restrictions via ip restriction rules. With the rules specifying origin as gateway, there is some hardening but there is no restrictions to who the caller are behind the gateway and app services have the potential to determine that as well.

This is where some co-operation is needed between the application gateway and the app services. The gateway automatically adds additional headers to indicate the source ip of the traffic. The app services would have a rule for the gateway indicating the source ip block for the gateway typically in an ip-address-with-/32 prefix notation and additional filters for matching the values of anticipated source ip ranges behind the gateway. There are four headers in all out of which the x_forwarded_host and x_forwarded_for bear the hostnames and the ip ranges. 

The x_forwarded_host seldom works when the gateway hostname is specified. This is not a shortcoming but header is used to identify the original host requested by the client in the Host HTTP request header and since name to ip resolutions might involve several layers of resolution, the values specified in the header for the clients might not agree. It is useful in the case where the reverse proxies such as load balancers or CDNs are involved, the host names and ports may differ from the origin server handling the request. With SSL termination, the source IP becomes that of the gateway and so this header preserves the original url’s location. Leaving this header blank and using the other header works in most deployments.

The X-Forwarded-For header provides information about the client’s IP address (or a chain of proxy IP addresses) that initiated the request. When requests pass through multiple proxies or load balancers, each proxy adds its own IP address to the X-Forwarded-For header. Azure App Services can use this header to determine the original client IP address, even if the request came through intermediate proxies. Specifying the CIDR for IP ranges in a comma separated string works in most deployments to match the clients with the requests and allow them selectively through the gateway’s firehose traffic.

Together with source ip restrictions, these headers enable sufficient hardening to the web traffic at the app service.

 This is a continuation of previous articles on IaC shortcomings and resolutions. While IaC code can be used deterministically to repeatedly create, update, and delete cloud resources, there are some dependencies that are managed by the resources themselves and become a concern for the end user when they are not properly cleaned up. Take for instance, the load balancers that compute instances and clusters create when they are provisioned using the Azure Machine Learning Workspaces. These are automatically provisioned. The purpose of this load balancer is to manage traffic even when the compute instance or cluster is stopped. Each compute instance has one load balancer associated with it, and for every 50 nodes in a compute cluster, one standard load balancer is billed. The load balancer ensures that requests are distributed evenly across the available compute resources, improving performance and availability. Each load balancer is billed at approximately $0.33 per day. If we have multiple compute instances, each one will have its own load balancer. For compute clusters, the load balancer cost is based on the total number of nodes in the cluster. One way to avoid load balancer costs on stopped compute instances and clusters, is to delete the compute resources when they are not in use. The IaC can help with the delete of the resources but whether the action is automated or manual, it is contingent on the delete of the load balancers and when delete fails for reasons such as locks on load balancers, then the user is left with a troublesome situation.

An understanding of the load balancer might help put things in perspective especially when trying to find them to unlock or delete. Many cloud resources and Azure Batch services create load balancers and the ways to distinguish them vary from resource groups, tags, or properties. These load balancers play a crucial role in distributing network traffic evenly across multiple compute resources to optimize performance and ensure high availability, they use various algorithms such as round-robin, least connections, or source IP affinity, to distribute incoming traffic to the available compute resources. This helps in maintaining a balanced workload and preventing any single resource from being overwhelmed. They also contribute to high availability by continuously monitoring the health of the compute resources. If a resource becomes unhealthy or unresponsive, the load balancer automatically redirects traffic to other healthy resources. They can seamlessly handle an increase in traffic by automatically scaling up the number of compute resources. Azure Machine Learning Workspace load balancers can scale up or down based on predefined rules or metrics, ensuring that the resources can handle the workload efficiently. Load balancing rules determine how traffic should be distributed. Rules can be configured based on protocols, ports, or other attributes to ensure that the traffic is routed correctly. Load balancers continuously monitor the health of the compute resources by sending health probes to check their responsiveness. If a resource fails the health probe, it is marked as unhealthy, and traffic is redirected to other healthy resources. Azure Machine Learning Workspace supports both internal and public load balancers. Internal load balancers are used for internal traffic within a virtual network, while public load balancers handle traffic from the internet. They can be seamlessly integrated with other Azure services, such as virtual networks, virtual machines, and container services, to build scalable and highly available machine learning solutions. Overall, load balancers in Azure Machine Learning Workspace play a critical role in optimizing performance, ensuring high availability, and handling increased traffic by evenly distributing it across multiple compute resources.

Creating the compute with node public ip set to false and disabling local auth can prevent load balancers from being created but if endpoints are involved, the Azure Batch Service will create them. Load balancers, public ip addresses and associated dependencies are created in the resource group of the virtual network and not the resource group of the machine learning workspace. Finding the load balancers and taking appropriate action on them can allow the compute resources to be cleaned up. This can be done on an ad hoc basis or scheduled basis.

 This is a continuation of a previous article on IaC shortcomings and resolutions. With regard to Azure Machine Learning Workspace, here is a sample request and response:

1. Go to https://learn.microsoft.com/en-us/rest/api/azureml/compute/create-or-update?view=rest-azureml-2023-10-01&tabs=HTTP#code-try-0 and signin with your secondary account:

Specify the following:

PUT https://management.azure.com/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.MachineLearningServices/workspaces/<ml-workspace-name>/computes/<compute-name>?api-version=2023-10-01

Authorization: Bearer <automatically created access token>

Content-type: application/json

{

  "properties": {

    "properties": {

      "vmSize": "STANDARD_DS11_V2",

      "subnet": {

        "id": "/subscriptions/<subscription-id>/resourceGroups/<rg-vnet-name>/providers/Microsoft.Network/virtualNetworks/<vnet-name>/subnets/<subnet-name>"

      },

      "applicationSharingPolicy": "Shared",

      "computeInstanceAuthorizationType": "personal",

      "enableNodePublicIp": false,

      "disableLocalAuth": true,

      "location": "centralus",

      "scaleSettings": {

        "maxNodeCount": 1,

        "minNodeCount": 0,

        "nodeIdleTimeBeforeScaleDown": "PT60M"

      }

    },

    "computeType": "AmlCompute",

    "disableLocalAuth": true

  },

  "location": "centralus",

  "disableLocalAuth": true

}

2. Check the response code to match as shown:

Response Code: 201

azure-asyncoperation: https://management.azure.com/subscriptions/<subscription-id>/providers/Microsoft.MachineLearningServices/locations/centralus/computeOperationsStatus/f6dcbe07-99cf-4bf7-aa71-0fdcfc542941?api-version=2023-10-01&service=new

cache-control: no-cache

content-length: 1483

content-type: application/json; charset=utf-8

date: Sat, 20 Apr 2024 02:28:50 GMT

expires: -1

pragma: no-cache

request-context: appId=cid-v1:2d2e8e63-272e-4b3c-8598-4ee570a0e70d

strict-transport-security: max-age=31536000; includeSubDomains

x-aml-cluster: vienna-centralus-02

x-content-type-options: nosniff

x-ms-correlation-request-id: f15d6510-5d21-426a-98e5-aa800322da83

x-ms-ratelimit-remaining-subscription-writes: 1199

x-ms-request-id: f15d6510-5d21-426a-98e5-aa800322da83

x-ms-response-type: standard

x-ms-routing-request-id: NORTHCENTRALUS:20240420T022850Z:f15d6510-5d21-426a-98e5-aa800322da83

x-request-time: 0.257