January 20, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
If you ask today’s CIOs why some applications won’t move to the cloud, they will mention issues such as a lack of systems that can deal with compliance, or vertically oriented applications and data that are too important to entrust to the cloud. Cloud providers now offer or will soon offer prebuilt, industry-specific features and services that typically will be better than anything companies could build and maintain themselves. The coming industry-specific world of cloud will move the needle enough for many enterprises to commit their critical data and applications to the cloud. The cloud providers understand this paradigm, and in many cases, the development and deployment of industry-specific cloud services may be a loss leader that will drive more adoptions. It’s important that we understand the likely motivations of the cloud providers before we adopt any cloud services, and I’ve made some educated assumptions here. There is always risk when you become too coupled to any cloud services because they will all go away at some point in time.?
Complexity in frontend web development is something that Gross has been attempting to address for nearly a decade now, having first created the intercooler.js alternative frontend library back in 2013, which came with the tagline “AJAX With Attributes: There is no need to be complex”. Recently, intercooler.js hit version 2.0 and became htmx, which the GithHub description says “allows you to access AJAX, CSS Transitions, WebSockets and Server Sent Events directly in HTML, using attributes, so you can build modern user interfaces with the simplicity and power of hypertext”. ... More simply, Gross described htmx as attempting “to stay within the original model of the web, using HTML and Hypermedia concepts, rather than asking developers to write a lot of JavaScript.” Somewhat amusingly for this discussion, htmx is a JavaScript library — but in keeping with this simplistic approach, it is dependency-free and frontend developers using htmx do not need to write JavaScript to achieve a similar functionality.?
When building a solution it is possible to do it all in the AWS Console. That's how I started my AWS journey. The issue is that it is not controllable, manageable or scalable. If you want to copy this setup to another account (separate dev and prod accounts) you have to remember all the steps you've done. Working with multiple team members can get messy. That is why it's helpful to use a framework to allow us to write Infrastructure-as-Code (IaC). This allows us to use Git for version control. This makes working as a team much easier, enables multi-environment deployments, even continuous integration and deployment. All things that are required when running production workloads ... I would recommend starting with a personal project that you use just for practicing using new services in. That way you don't have to worry about breaking things and you can focus on how the service is working. You can now start using it in production apps and this is where you'll learn a lot about the details of a service.?
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A microservices-oriented application (MOA) addresses the shortcomings inherent in the monolithic application design. As described earlier, monolithic applications are hard to maintain and upgrade. Due to the tight coupling that's implicit in a monolithic application's construction, even making a small change can create unforeseen problems that can cascade throughout the application stack. On the other hand, MOAs are loosely coupled, some say to an extreme. According to the five principles described in the previous article of this series, a microservice is an entirely independent unit of computing. It has a distinct presence on the network and carries its own data. It's completely and independently responsible for its own well-being. This means that as long as changes in its public interface do not affect current consumers of the service, an MOA can be changed independently of any other microservice in the MOA. Figure 4 illustrates an MOA that is a transformation of the monolithic application described previously. Notice that each microservice has its own IP address and port assignment.
There have been multiple efforts to shrink deep neural networks to a size that fits on small-memory computing devices. However, most of these efforts are focused on reducing the number of parameters in the deep learning model. For example, “pruning,” a popular class of optimization algorithms, compress neural networks by removing the parameters that are not significant in the model’s output. The problem with pruning methods is that they don’t address the memory bottleneck of the neural networks. Standard implementations of deep learning libraries require an entire network layer and activation maps to be loaded into memory. Unfortunately, classic optimization methods don’t make any significant changes to the early layers of the network, especially in convolutional neural networks. This causes an imbalance in the size of different layers of the network and results in a “memory-peak” problem: Even though the network becomes lighter after pruning, the device running it must have as much memory as the largest layer.?
The third generation of security analytics technologies brings us to the current day, where machine learning, behavioral analysis and customization are driving innovation. There are now SIEM products that allow organizations to use their existing data lakes, rather than forcing customers to use proprietary ones. And some solutions have opened their analytics, enrichment, and machine learning models so users can better understand them and modify as needed. Today, powerful algorithms find patterns in data, set baselines and identify outliers. There’s also a greater focus on identifying anomalous behavior (a user taking suspicious actions) and on prioritizing and ranking the risk of alerts based on contextual information like data from Sharepoint or IAM systems. For example, a user accessing source code with legitimate credentials might be a low-priority alert at best, but that user doing so in the middle of the night for the first time in weeks from a suspicious location should trigger a high-priority alert.