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Operationally industrializing AI is the number one key success factor that enables:
I’m going to share what we’ve seen on the journey from AI promise to practical reality in Rakuten Mobile and elsewhere, the strategies that are successfully overcoming common hurdles and actual results from initial deployments.
Deploying AI-driven analytics means confronting tooling and data management alongside appropriate compute and memory resources. Orchestrating data-intensive analytics pipelines is a skill.
We in telecom need to do this while we are under unprecedented pressure to navigate data sovereignty, security regulations, budget pressures and new geopolitical uncertainties that seem to shift by the day.
Against this backdrop, many are reconsidering previous strategies.
The preference is growing to run workloads on private, on-premises telco cloud platforms that are better suited to stricter security requirements, localized data residency and cost-effective operations.
While private telco clouds alleviate some issues, they introduce others with the need of sophisticated automation and lifecycle management capabilities.
Out-of-the-box, cost effective solutions that manage both AI tooling and underlying infrastructure management are essential for reducing complexity and accelerating time to outcomes. This is the true purpose of the AI workloads.
One thing is certain: the less complex the operational approach, the quicker the telco cloud can deliver tangible AI benefits.
A tier-one operator’s early deployment of cloud-based AI analytics spotlights how complexity can be confronted and overcome.
The multi-service operator serving Africa and the Middle East faced considerable operational hurdles deploying data analytics apps like Spark, Kubeflow and TensorFlow. In particular, manual processes kept thwarting rapid provisioning and agile workload management.
Attempts to scale analytics environments became more about managing operational complexity than extracting insights.
First step to addressing these challenges was standardizing on a cloud-native analytics-as-a-service model tailored specifically to the operator’s operational and data science teams.
Importantly, this wasn’t viewed as a tech exercise. Rather, the operator underwent a fundamental rethink of how analytics applications were deployed, scaled and operationally managed.
Buoyed by a more simplified approach, teams began taking advantage of self-service capabilities that gave data scientists the ability to rapidly provision resources without the delays previously experienced. Lifecycle management became automated, significantly reducing operational complexity.
Positive results followed with the simplified approach yielding clear, measurable benefits:
Zooming out, the operator began to see faster time-to-value, reduced complexity in day-to-day operations and substantial operational savings compared to the previous approach.
As we've seen from real-world deployments like the above, the right platform architecture is the foundation upon which all AI success is built. But what exactly should you look for in a solution?
Let’s break down the critical elements that any robust AI implementation should include.
The most effective AI implementations start with a unified data and AI workbench that encompasses the entire data lifecycle. This isn't just about technology, it's about creating a foundation that empowers both your data scientists and IT teams.
Your solution must be platform-agnostic, providing flexibility to deploy on-premises or in the cloud depending on your organization's specific requirements. Look for solutions offering comprehensive data governance with secure columnar masking, federated query capabilities and seamless storage integration (i.e., object storage, RDBMS, OLAP).
The most successful telco deployments are embracing platforms with comprehensive automation and real-time data discovery. These capabilities drastically reduce operational overhead while enabling your data teams to focus on insights rather than infrastructure management. When evaluating potential solutions, prioritize those with AI-monitored platforms that can proactively identify issues before they impact operations.
A well-organized AI/ML pipeline is the backbone of effective implementation. As telcos face increasing pressure to deliver faster insights, the pipeline architecture becomes the critical factor in reducing time-to-value from months to days.
The pipeline must seamlessly handle both batch and streaming data, with each stage optimized for specific functions. From initial data ingestion through storage, processing, transformation and ultimately to model deployment and serving, your pipeline needs to maintain data integrity while providing the flexibility data scientists require.
Look for solutions where data discovery is integrated across the entire pipeline, not siloed in individual components. This comprehensive approach ensures your teams can easily locate, access and utilize relevant datasets regardless of where they sit in your organization
When evaluating AI platforms, focus on these four essential modules that provide the functional foundation for success:
The platform you choose must simplify complexity, not add to it.
When these essential elements come together in a cohesive solution, your organization will experience the same transformative benefits we've seen in pioneering deployments, including dramatically accelerated deployment times, improved hardware utilization, reduced maintenance demands and a genuinely cloud-like experience for your data scientists.
Are you incorporating these essential elements in your AI implementation strategy? Which areas present the greatest challenge for your organization? Share your thoughts in the comments. Tag Rakuten Cloud SVP and Global Head of Service Provider & Telco Business Vivek Chadha to start a conversation.
