The learning curve of a Cloud Service Specialist at Solita

Tommi Ritvanen is part of Cloud Continuous Services Team in Solita. The team consists of dozens of specialist who ensure that customers' cloud solutions are running smoothly. Now Tommi shares his experiences of learning path, culture and team collaboration.

I’ve been working at Solita for six months as a Cloud Service Specialist. I’m part of the cloud continuous services team, where we take care of our ongoing customers and ensure that their cloud solutions are running smoothly. After our colleagues have delivered a project, we basically take over and continue supporting the customer with the next steps.

What I like about my job is that every day is different. I get to work and learn from different technologies; we work with all the major cloud platforms such as AWS, Microsoft Azure and Google Cloud Platform. What also brings variety in our days is that we have different types of customers that we serve and support. The requests we get are multiple, so there is no boring day in this line of work.

What inspires me the most in my role is that I’m able to work with new topics and develop my skills in areas I haven’t worked on before. I wanted to work with public cloud, and now I’m doing it. I like the way we exchange ideas and share knowledge in the team. This way, we can find ways to improve and work smarter.

We have this mentality of challenging the status quo positively. Also, the fact that the industry is changing quickly brings a nice challenge; to be good at your job, you need to be aware of what is going on. Solita also has an attractive client portfolio and a track record of building very impactful solutions, so it’s exciting to be part of all that too.

I got responsibility from day one

Our team has grown a lot which means that we have people with different perspectives and visions. It’s a nice mix of seniors and juniors, which creates a good environment for learning. I think the collaboration in the team works well, even though we are located around Finland in different offices. While we take care of our tasks independently, there is always support available from other members of the cloud team. Sometimes we go through things together to share knowledge and spread the expertise within the team.

The overall culture at Solita supports learning and growth, there is a really low barrier to ask questions, and you can ask for help from anyone, even people outside of your team. I joined Solita with very little cloud experience, but I’ve learned so much during the past six months. I’ve got responsibility from the beginning and learned while doing, which is the best way of learning for me.

From day one, I got the freedom to decide which direction I wanted to take in my learning path, including the technologies. We have study groups and flexible opportunities to get certified in the technologies we find interesting.

As part of the onboarding process, I did this practical training project executed in a sandbox environment. We started from scratch, built the architecture, and drew the process like we would do in a real-life situation, navigating the environment and practising the technologies we needed. The process itself and the support we got from more senior colleagues was highly useful.

Being professional doesn’t mean being serious

The culture at Solita is very people-focused. I’ve felt welcome from the beginning, and regardless of the fact that I’m the only member of the cloud continuous services team here in Oulu, people have adopted me as part of the office crew. The atmosphere is casual, and people are allowed to have fun at work. Being professional doesn’t mean being serious.

People here want to improve and go the extra mile in delivering great results to our customers. This means that to be successful in this environment, you need to have the courage to ask questions and look for help if you don’t know something. The culture is inclusive, but you need to show up to be part of the community. There are many opportunities to get to know people, coffee breaks and social activities. We also share stories from our personal lives, which makes me feel that I can be my authentic self.

We are constantly looking for new colleagues in our Cloud and Connectivity Community! Check out our open positions here!

No public cloud? Then kiss AI goodbye

What’s the crucial enabling factor that’s often missing from the debate about the myriad uses of AI? The fact that there is no AI without a proper backend for data (cloud data warehouses/data lakes) or without pre-built components. Examples of this are Cloud Machine Learning (ML) in Google Cloud Platform (GCP) and Sagemaker in Amazon Web Services (AWS). In this cloud blog I will explain why public cloud offers the optimum solution for machine learning (ML) and AI environments.

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.

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