How Kubernetes Can Boost AI Business Value?
The influence of Artificial Intelligence can be seen in most industries. Whether it’s automatic cars or medical diagnoses, AI/ML’s scope is wide. It plays an important role in our lives and the possibilities of how AI-enabled technology will further enhance our lives or improve our workload are endless. According to a study by Stanford University, there has been a 14-times rise in the numbers of AI start-ups since 2000, while AI developers noted a venture capital funding boom of more than 200%. Its potential to change businesses is seemingly limitless!
However, developing AI-enabled applications are not a piece of cake. It is very difficult to design and write large AI programs because there are many types of data involved. Many steps are required at each stage to begin building a robust AI app and each requires different skills. Extracting features, verifying & analysing data collection, and managing machine resource make up most of the codebase to permeates a relatively small subject of the actual ML code. Fortunately, Kubernetes – an open-source platform which automates the deployment and management of containerised apps, including complicated workloads such as AI & ML – can be a great facilitator.
In this blog, we will talk through learn kubernetes online perks and how they can enhance AI market value.
Kubernetes-Based AI/ML Workflow to Help with Pain-Points!
Organizations face challenges, some of which are shifting through a huge raft of data, Silos, application visualization, analysis, and retraining, provisioning of software tooling, inconsistencies and many more. However, a Kubernetes based workflow model can help organizations address and resolve these challenges! Let’s see how –
- Codifying Problem and Metrics
- Data Collection & leaning
- Feature Engineering
- Model Training and Tuning
- Model Validation
- Model Deployment
- Monitoring Validation
All these stages of the workflow go with self-service infrastructure that enables both hardware infrastructure and software tools. These tools have to be provisioned on demand with no bottlenecks from the IT OPs. The automation of the whole workflow is required for fast and safe movements of handoffs and workloads between the parties.
Proven Approach to Contribute to Business Success
Lets’ find out how the Kubernetes workflow mentioned above can address the obstacles faced by businesses –
Kubernetes systems allows developers to easily access strong data management capabilities including Apache Kafka, Apache Spark, and storage capabilities like Ceph. This helps streamline the management of a large number of data from various sources that are needed in Al/ML.
Eliminating Silos and Enhancing Delivery to Production:
Whether it is Data Analysis, IT operations, engineering, science, app development or any other types of business, you can use Kubernetes to break down the barriers and overcome silos. It can boost collaboration between parties via workflow-driven and automation approaches including common storage, CI/CD, and shared images registries.
Analysing, Validating, and Retraining Post-Deployment:
Kubernetes has a number of container-based analytics and visualization tools that offer ongoing feedback as well as verifications of model accuracy/consistency or drift.
Kubernetes is designed to efficiently make the provisioning of hardware resources for Al/ML workloads simple and effective. Memory, CPU, Storage, and GPU are among the tool available or allocated to the technology. These resources are restrained only for the time of the particular workload, whether it’s testing through CI/CD, training a model, or serving the runtime. Then the workloads get freed up and made available to other workloads when they are no longer needed. Due to its on-demand nature and utilization, it leads to great efficiency based on the hardware.
Software & Tool Provisioning:
Extraordinary efficiencies are achieved by data scientists as well as data engineers with self-served data and ML tools. For instance, the removal of IT bottlenecks alongside the optimal use of the accent of an expensive and scarce professional. Kubernetes exposes container catalogs, data backed by operator & Al/ML services that enable fast uptake & consumption.
Inconsistencies in Artificial Learning Tooling:
Using a similar suite of tools & services around the organization will help reduce mismatched versions and inconsistencies associated with them.
Kubernetes can help resolve multiple business challenges, helping organizations move towards greater capitalization by enhancing their problem-solving approaches, customer satisfaction, which ultimately leads to boosted business value.
Kubernetes and Docker, another open-source container-file format for automating the deployment of applications, have grown in popularity among developers. This has resulted in huge demand for Docker and Kubernetes certification, especially Kubernetes online training courses.
Cognixia brings to you a unique bootcamp i.e., Kubernetes certification, providing an immersive learning experience to help you develop skills to meet industry & organizational standards.
Learn Kubernetes online with Cognixia to gain comprehensive training. The course will include –
• Docker Essentials
• Kubernetes, Kubernetes Overview
• Kubernetes Cluster
• Kubernetes Pod Overview
• Kubernetes Client
• Creating and Modifying ConfigMaps and Secrets
• Replication Controller and Replica Set
• Exploring the Kubernetes API & Key Metadata
• Managing Specialized Workloads
• Volumes and Configuration Data
• Monitoring & Logging
• Maintenance & Troubleshooting
• The Ecosystem