Why is public cloud essential to AI/ML projects?
- AWS, Microsoft Azure and GCP offer plenty of pre-built machine learning components. This helps projects to build AI/ML solutions without requiring a deep understanding of ML theory, knowledge of AI or PhD level data scientists.
- Public cloud is built for workloads which need peaking CPU/IO performance. This lets you pay for an unlimited amount of computing power on a per-minute basis instead of investing millions into your own data centres.
- Rapid innovation/prototyping is possible using public cloud – you can test and deploy early and scale up in the production if needed.
Public cloud: the superpower of AI
Across many types of projects, AI capabilities are being democratised. Public cloud vendors deliver products, like Sagemaker or CloudML, that allow you to build AI capabilities for your products without a deep theoretical understanding. This means that soon a shortage of AI/ML scientists won’t be your biggest challenge. Projects can use existing AI tools to build world-class solutions such as customer support, fraud detection, and business intelligence.
My recommendation is that you should head towards data enablement. First invest in data pipelines, data quality, integrations, and cloud-based data warehouses/data lakes. So rather than using over-skilled AI/ML scientists, build up the essential twin pillars – cloud ops and skilled team of data engineers.
Enablement – not enforcement
In my experience, many organisations have been struggling to transition to public cloud due to data confidentiality and classification issues. Business units have been driving the adoption of modern AI-based technology. IT organisations have been pushing back due to security concerns. After plenty of heated debate we have been able to find a way forward. The benefits of using public cloud components in advanced data processing have been so huge that IT has to find ways to enable the use of public cloud.
The solution for this challenge has proven to be proper data classification and the use of private on-premises facilities to support operations in public cloud. Data location should be defined based on the data classification. Solita has been building secure but flexible automated cloud governance controls. These enable business requests but keep the control in your hands, as well as meeting the requirements usually defined by a company’s chief information security officer (CISO). Modern cloud governance is built on automation and enablement – rather than enforcing policies.
- The pathway to effective AI adoption usually begins by kickstarting or boosting the public cloud journey and competence within the company.
- Our recommendation – the public cloud journey should start with proper analyses and planning.
- Solita is able to help with data confidentiality issues: classification, hybrid/private cloud usage and transformation.
- Build cloud governance based on enablement and automation rather than enforcement.