Following our June 2022 post, let’s dive even deeper into the cloud computing (CC) trends we plan to follow in the coming months.
- CC Market
- Key Services
- Serverless Functions
- DevOps CI/CD
- ML/AI Products
- IoT Technology
- Explore More
- Top Cloud IoT Platforms
- Chicago, Aug. 30, 2022 (GLOBE NEWSWIRE) — Cloud Performance Management Market to grow from USD 1.5 billion in 2022 to USD 3.9 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 17.6% during the forecast period, according to a new report by MarketsandMarkets™.
- Global cloud services spend up 33% to hit $62.3 billion in Q2 2022. The top three vendors in Q2 2022, namely Amazon Web Services (AWS), Microsoft Azure and Google Cloud, together accounted for 63% of global spending in Q2 2022 and collectively grew 42%.
- AWS accounted for 31% of total cloud infrastructure services spend in Q2 2022, making it the leading cloud service provider. It grew 33% on an annual basis. Azure was the second largest cloud service provider in Q2, with a 24% market share after growing 40% annually. Google Cloud grew 45% in the latest quarter and accounted for an 8% market share.
- Canalys VP Alex Smith said: “Cloud remains the strong growth segment in tech. While opportunities abound for providers large and small, the interesting battle remains right at the top between AWS and Microsoft. The race to invest in infrastructure to keep pace with demand will be intense and test the nerves of the companies’ CFOs as both inflation and rising interest rates create cost headwinds.”
- Both AWS and Microsoft are continuing to roll out infrastructure. AWS has plans to launch 24 availability zones across eight regions, while Microsoft plans to launch 10 new regions over the next year. In both cases, the providers are increasing investment outside of the US as they look to capture global demand and ensure they can provide low-latency and high data sovereignty solutions.
Types of CC architecture: public cloud, private cloud, and hybrid cloud.
Types of cloud services: IaaS, PaaS, FaaS, and SaaS.
- With IaaS, you rent IT infrastructure—servers and virtual machines (VMs), storage, networks, operating systems—from a cloud provider on a pay-as-you-go basis.
- PaaS refers to cloud computing services that supply an on-demand environment for developing, testing, delivering, and managing software applications.
- Overlapping with PaaS, FaaS focuses on building app functionality without spending time continually managing the servers and infrastructure required to do so.
- With SaaS, cloud providers host and manage the software application and underlying infrastructure, and handle any maintenance, like software upgrades and security patching.
IaaS delivered improvements in cost, agility, scalability, and reliability.
The diagram above provides a simplified/generalized view of choices we have from a hosting perspective:
- On-premises: represents the traditional model of purchasing/licensing and acquiring software, install them, and manage them in our own data centers
- Hosted: represents the co-location or managed outsourced hosting services. For example, GoGrid, Amazon EC2, etc.
- Cloud: represents cloud fabric that provides higher-level application containers and services. For example, Google App Engine, Amazon S3/SimpleDB/SQS, etc.
FaaS is a type of serverless cloud computing service that allows executing code in response to events without maintaining the complex infrastructure typically associated with building and launching microservices applications. With FaaS, users manage only functions and data while the cloud provider manages the application. This allows developers to get the functions they need without paying for services when code isn’t running. Some popular FaaS examples include: Amazon’s AWS Lambda, Google Cloud Functions and Microsoft Azure Functions.
CC = Heart of Global Digital Transformation
What Serverless Does Not Mean
FaaS executes logic in response to events. All methods are grouped into a single deployable unit – a stateless Function. Scaling is handled automatically.
Traditional vs Serverless Architecture
Microservices: an architectural pattern that breaks down large application structures into smaller, independent services that are not dependent upon a specific coding language; derived from service-oriented architecture (SOA).
Jenkins CI/CD Automation with OpsMX
Jenkins is widely adopted for continuous integration (CI). Many organizations extend Jenkins with scripts and plug-ins to perform continuous delivery (CD) and deployments. Whether deploying to Kubernetes, VM, hybrid or multi-cloud environments, there is a better approach to implementing CD on top of Jenkins than using scripts and manual processes.
Automating CD processes using AI can increase the velocity and accuracy of releases, improve the productivity of DevOps teams and eliminate manual Jenkins deployment scripts. In addition, many organizations face challenges with achieving regulatory and security compliance and performing audits. Automating CD with AI and deploying a central policy engine can help to enforce regulatory and security requirements and enable organizations to conduct audits easily.
Modern CD solutions—those that incorporate AI and include open tool integration layers—can allow developers and DevOps teams to continue using the tools they know and love. Deep toolchain integrations provide real-time insights and diagnostics correlated with pipelines, which speeds approvals, verifications and triage activities. Modernizing your Jenkins CI/CD processing with CD automation offers key capabilities your teams need for software delivery success.
Codefresh vs Jenkins CI/CD tooling:
Codefresh has a bigger scope than Jenkins. In fact, one of the most crucial points of the comparison is that Jenkins is only a Continuous Integration (CI) solution, while Codefresh covers both Continuous Integration and Continuous Delivery.
Cloud Modernization with CircleCI
GKE Autopilot is a new mode of operation in Google Kubernetes Engine (GKE)
designed to reduce operational costs around managing clusters, optimizing production time and driving higher workload availability. As a Google Cloud Platform (GCP) partner, CircleCI makes it easy to integrate CI/CD workflows with GCP and utilize modes of operation like Autopilot.
GitOps = IaC + MRs + CI/CD
- IaC – GitOps uses a Git repository as the single source of truth for infrastructure
definitions. A Git repository is a .git folder in a project that tracks all changes
made to files in a project over time. Infrastructure as code (IaC) is the practice
of keeping all infrastructure configuration stored as code.
- MRs – GitOps uses merge requests (MRs) as the change mechanism for all
infrastructure updates. The MR is where teams can collaborate via reviews and
comments and where formal approvals take place. A merge commits to your
master (or trunk) branch and serves as a changelog for auditing
- CI/CD – GitOps automates infrastructure updates using a Git workflow with
continuous integration and continuous delivery (CI/CD). When new code
is merged, the CI/CD pipeline enacts the change in the environment. Any
configuration drift, such as manual changes or errors, is overwritten by GitOps
automation so the environment converges on the desired state defined in Git.
GitLab uses CI/CD pipelines to manage and implement GitOps automation, but
other forms of automation such as definitions operators can be used as well.
- The Azure Machine Learning Studio
- Intelligent Search using Amazon Kendra
- Semantic Segmentation using Amazon Sagemaker
- Image Classification & object detection using AWS GluonCV and Rekognition
- Stock Forecast using Amazon Forecast
- GCP AutoML enables developers with limited ML expertise to train AI models.
MLOps is a key aspect of ML engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others.
DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks usually associated with separate application development and IT operations teams.
|Technology Deployment Stages||DevOps||MLOps|
|Development||Usually, the code creates an interface or application. |
The code is wrapped into an executable or artifact before being deployed and tested with a set of checks.
Ideally, this automated cycle will continue until the final product is ready.
|The code enables the team to build or train machine learning models. |
The output artifacts include serialized files that can receive data inputs to generate inferences.
Validation involves checking the trained model’s performance based on the test data.
This cycle should also continue until the model reaches a specified performance threshold.
|Version Control||Version control typically only tracks changes to code and artifacts.|
There are few metrics to track.
|MLOps pipelines usually have more factors to track. Building and training an ML model involves an iterative experimentation cycle, requiring tracking of various metrics and components for each experiment (essential for later audits). |
Additional components to track include training datasets, model building code and model artifacts.
Metrics include hyperparameters and model performance indicators, such as error rates.
|Reusability||DevOps pipelines focus on repeatable processes.|
Teams can mix and match processes without following a specific workflow.
|MLOps pipelines repeatedly apply the same workflows. The common framework across projects helps improve consistency and allows teams to progress faster because they start with familiar processes. |
Project templates offer structure, enabling customization to address the unique requirements of each use case.
Uses centralized data management to consolidate the organization’s data to accelerate the discovery and training processes. Common approaches to centralization include a single source of truth and data warehouses.
|Continuous Monitoring||Site reliability engineering (SRE) has been trending over the past few years, emphasizing the need for monitoring software from development through to production deployment. |
The software does not degrade in the way an ML model does.
|Machine learning models can degrade quickly, requiring constant monitoring and updating.|
Conditions in the production environment affect the model’s accuracy. After deployment to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance.
MLOps ensures that algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.
CI/CD automation tools
|Deep learning and machine learning frameworks|
Cloud storage for large datasets
GPUs for deep learning and computationally-intensive ML models
The Internet of Things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.
IoT ETL pipeline = data gathering, processing, analytics and ML
IoT = Device + Gateway + Cloud
Top Cloud IoT Platforms
- Thingworx 8 IoT Platform
- Microsoft Azure IoT Suite
- Google Cloud’s IoT Platform
- IBM Watson IoT Platform
- AWS IoT Platform
- Cisco IoT Cloud Connect
- Salesforce IoT Cloud
- Kaa IoT Platform
- Oracle IoT Platform
- Thingspeak IoT Platform
- GE Predix IoT Platform
Top 3 cloud-native IoT vendors
- Dynatrace – Monitor the performance, availability, and health of your IoT devices through a single all-in-one platform powered by AI.
- 2smart – No-code software for bringing smart devices to internet and market.
- Emnify – Intuitive IoT Monitoring Dashboard.
- AT&T Managed Threat Detection and Response (MDR) is a sophisticated managed detection and response service that helps you to detect and respond to advanced threats before they impact your business. It builds on the unified security management (USM) platform for threat detection and response, and AT&T Alien Labs™ threat intelligence.
- Multifactor authentication (MFA) is becoming increasingly standard within software development organizations, with GitHub recently announcing that two-factor authentication (2FA) will be mandatory for all code contributors by the end of 2023.
- Cloud computing platforms are rife with misconfigurations that cybercriminals regularly exploit. Developers using infrastructure-as-code tools simply lack the cybersecurity expertise required to make sure cloud application environments are secure. It’s up to the cybersecurity team to make sure that the policies and guardrails created to secure cloud platforms are observed, especially when it comes to APIs.
- The advent of cloud-native, container-based architecture and microservices-based applications running on platforms like Kubernetes has sharpened the focus on API security and the software supply chain—from both security teams and cyberattackers. Software supply chains and APIs have become the new attack surfaces of choice, and with everyone from the White House to entry-level developers talking SBOMs, open source security and APIs, this is an area that’s getting lots of attention.
- BrightTalk Webinars of Interest
Cut Through Cybersecurity Complexity by Converging Key Capabilities at the Edge
Through the Eyes of the Wolf: Insights into the Chain of Cyber Threats
- Palo Alto Top 3 Reads
5 Signs the World Isn’t Paying Enough Attention to 5G Security
Managing User Identity in a Cloud-First World
How to Launch an Effective Zero Trust Initiative
The Application of ML/AI in Diabetes
MIT MACHINE LEARNING IN BUSINESS
Coursera: Containerized Applications on AWS
If you’re interested in strategies to help 𝗗𝗲𝘁𝗲𝗰𝘁 𝗮𝗻𝗱 𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗧𝗵𝗿𝗲𝗮𝘁𝘀 in the cloud, this AWS Startup Showcase is for you.
How AI and Cloud Computing help enterprises scale up in 2022?
Udemy Course: Serveless Concepts
Edge, Cloud, Core: Building an Affordable Hybrid Cloud
Scale, Build, and Manage Kubernetes Deployments
58 responses to “Cloud-Native Tech Status Update Q3 2022”
Thank you so much as you have been willing to share information with us. We will forever admire all you have done here because you have made my work as easy as
It proved to be Very helpful to me and I am sure to all the commentators here!
I love visiting sites in my free time. I have visited many sites but did not find any site more efficient than yours. Thanks for the nudge! https://lookmovie.studio
I know this is one of the most meaningful information for me. And I’m animated reading your article. But should remark on some general things, the website style is perfect; the articles are great. Thanks for the ton of tangible and attainable help.
Positive site, where did u come up with the information on this posting?I have read a few of the articles on your website now, and I really like your style. Thanks a million and please keep up the effective work https://solarmovies-solarmovies.store