DevOps is more than an automated software development approach and a collaborative culture nowadays. Cloud computing, the internet of things, artificial intelligence, and machine learning are among the cutting-edge technologies used.
Businesses are constantly modernising their operations to increase efficiency and deliver unique client experiences. The digital transformation has accelerated the timeframes for interactions, transactions, and choices.
Companies can benefit from this data by utilising machine learning. Similarly, Machine learning (ML) models can detect patterns in massive volumes of data, allowing them to make choices faster and more correctly than people.
In this week’s Tech Tuesday, we present our pick of DevOps and Machine learning tools to pick for your business.
Akamai’s Linode Managed Database
Linode Managed Database service marks Akamai’s first product in its Cloud Computing line of business with support for MySQL, PostgreSQL, Redis and MongoDB. Launched in April 2022, the service simplifies database deployment, helping developers reduce risk, increase efficiency and minimise the complexity that comes with the manual management of production database clusters. Databases are the essential component of any application, but manually managing them in production is a labour and resource-intensive process.
With Akamai’s Linode Managed Database service, users can defer common deployment and maintenance tasks to Linode and elect high availability configurations to ensure that database performance and uptime are never affected. The result is less hands-on management expertise required to deploy applications and a decreased risk of downtime compared to manual management.
Customers can take advantage of features such as flat-rate pricing, security and recovery measures, flexible deployment options and high-availability cluster options.
The DevOps technology stack is growing rapidly.
With Chef, Progress covers the entire DevSecOps lifecycle: from design to development to operational assurance – including secure hybrid-cloud infrastructure management, full-stack observability and high availability. Chef enables organisations to automate and secure deployments to multi-cloud, hybrid cloud and on-premise environments.
Progress helps facilitate a culture of DevSecOps through collaboration and automation. If an organisation’s policies and intentions are defined, they can be written in human-readable code and automated. With Chef, large and heavily regulated organisations automate security and compliance, freeing their IT teams to collaborate on strategic issues.
Mendix Assist is the first AI-assisted development built into a low-code application development platform. It uses machine learning (ML) analysis of over 5 million application logic flows built with the Mendix platform across 15 industries. This delivers 90 per cent accuracy on next-step suggestions and expert quality, performance and maintenance recommendations for application development. Businesses that leverage Mendix Assist can boost developer productivity, mentor new developers, and decrease defects’ cost and time impact by 100x.
Mendix Assist aims to enable business domain experts while empowering professional developers through abstraction, automation and intelligent assisted development. The capabilities of visual modelling languages for business applications, consistency checks preventing technical errors, 1-click cloud deployment and integrated feedback management are key to enabling the entire spectrum of developers to participate in software development.
Based on these concepts, Mendix Assist has enabled a whole range of people with different backgrounds to create software that successfully delivers actual business value.
GitLab is The DevSecOps Platform that empowers organisations to maximise the overall return on software development by delivering software faster and efficiently with security and compliance built into every stage. With GitLab, every team within an organisation can collaboratively plan, build, secure, and deploy software to drive business outcomes faster, with complete transparency, consistency, and traceability.
For growing businesses, migrating from a complex and costly DevOps toolchain to GitLab can not only help save time from inefficient workloads for small IT teams, it can also result in financial savings too. IT managers can measure potential savings with GitLab’s ROI Calculator, which can help estimate the financial benefits a business could realise by moving to GitLab from their DIY DevOps toolchain.
GitLab’s end-to-end DevSecOps platform can turn IT into a business driver that speeds up software creation, boosting competitiveness and pulling in more revenue while keeping the software supply chain secure.
UiPath AI Center
UiPath AI Center lets organisations deploy, manage, and continuously improve machine learning by providing end-to-end visibility on the usage of machine learning models, machine learning data, model performance, user actions, and pipelines. It also maintains version control and both human-in-the-loop and automatic retraining.
This significantly improves processes across many industries, such as government and finance. For example, Heritage Bank is using this technology to improve the investigation of living expenses when assessing loans, which helps achieve up to 90% process automation with 98 per cent accuracy.
It is a technology that opens up more opportunities for citizen developers through user-friendly pre-built models and templates. The citizen data scientist now has access to machine learning models for discovery and automation robots executing actions such as when a model discovers a fraudulent transaction. The robots then immediately lock that account from further fraudulent activity.
Advanced analytics using machine learning and AI techniques has tremendous promise to help streamline processes and improve customer experiences. Despite considerable investments in data science, many companies find they can only generate value from AI with sustainable deployment in production environments. The challenge is that predictive models differ from other software projects and require a new set of tools for deployment, monitoring, and ongoing management, known as machine learning operations or MLOps.
Dataiku is the platform for Everyday AI, with a complete approach from AI experimentation and design through production, including built-in MLOps for deployment, management, and governance. With Dataiku, analytics teams build and deploy production-ready projects in hours, not weeks. Operations teams have the tools to monitor models for data drift and loss in predictive accuracy. And everyone is working in a collaborative environment that makes it easy to update models and get better-performing model versions up and running fast.
GitHub is a popular DevOps liaison tool. It is used for rapid code iteration. When a source code file is modified, a notification is immediately delivered to the rest of the team. It provides a very simple and dependable mechanism for rolling back software code to stable versions in the event of a mishap.
Azure DevOps is a popular DevOps solution that is available in the SaaS model. It was created by Microsoft and provides a variety of services, such as Azure Boards, Azure Pipeline, and others. You can access it using either your browser or an IDE client.
Jenkins is an open-source DevOps tool that is nifty in building great things & it can automate various elements in your pipeline by fully customising it as per requirements. It supports a vast ecosystem of plugins, making it possible to run it along with other equally productive DevOps tools.
Jenkins is pretty easy to install and configure. It is designed to support distributed workflows for accelerated and transparent builds, tests, and deployments across platforms.
With the Docker engine, you can access the containers that can execute apps in a remote environment. The Docker platform also enables companies to exchange container images, develop applications, and collaborate with users to build programs for components.
It is available as a PaaS technology which uses OS-level virtualisation for producing containerised software. It is an ideal tool for automating the entire procedure from development to deployment.