Artificial Intelligence (AI) is everywhere around us, no doubt about that. Machine Learning (ML), computer vision, natural language processing, cognitive automation… and similar terms have been present in technology-related conversations for a long time. AI is already mature in many areas – from various consumer scenarios to specialized niche applications.
It is also becoming more and more present in enterprise business use cases. To be more precise, the number of opportunities for improving business processes by implementing AI is growing every day. And that is a common feeling we get from talking to various clients in various markets. Most of them are either already implementing AI or seriously considering starting. As with any other major technology shift, AI brings its own challenges for any enterprise adopting it. Just to name a few – How do you identify the right business scenarios for AI applications? How do you deal with the underlying data? Do you have the right skills within your organization? What about the technology platform? How do you deal with the AI lifecycle (think labeling, training, deployment, validation, retraining,…)? It is obvious that many enterprise organizations need support going through these kinds of challenges.
Helping clients achieve better business outcomes out of technology shifts has always been CROZ’s mission. So, several years ago we started our journey towards including artificial intelligence techniques into our services portfolio. Over the years, we experimented with many different aspects of AI – for example, using natural language processing (NLP) in sentiment analysis, building various chatbots for conversational scenarios, using computer vision techniques for understanding documents, or using NLP to extract metadata from various types of documents. We also spent a lot of time in collaboration and joint projects with our friends and experts from the University of Zagreb, which provided an additional layer of foundation and structure to our AI knowledge.
And now we believe that the time is right to bring all this knowledge to the market through a specific and focused offering. To do that, we decided to establish a new business brand – CROZ AI. Leveraging all the existing knowledge and capabilities of CROZ, CROZ AI will focus on helping enterprise clients bring more cognitive automation based on AI into their business processes.
CROZ AI Services
Building AI capabilities into enterprise software systems
At the core of its offering, CROZ AI will provide services focused on building AI capabilities into enterprise software systems. Effectively, it means building custom ML-driven systems to embed intelligence and cognitive automation into the core business processes of our clients. We believe that this is the area in which the use of AI will grow in the following years. The constant need for more efficiency, more automation, more adaptability, and better and faster decision-making has a direct impact on how we need to build software today.
For example, a bank may want to improve its customer onboarding processes by using AI to analyze customer behavior and to react at the right time to increase engagement. Within the same onboarding process, a bank may also want to use computer vision and NLP to streamline document processing and improve customer experience. Or a telco may want to improve its sales process by using machine learning models to create targeted sales offers for specific customers, based on their preferences and previous behavior. And we could go on.
We are ready to work with our clients in identifying opportunities like these for applying AI within their processes. In the same way, our teams will tackle designing and building the right ML-based technical solutions, understanding and providing all the necessary data and integrations along the way.
AIOps
The second part of the CROZ AI services portfolio is AIOps, the concept of using AI in IT operations. AIOps combines big data and machine learning to transform IT operations processes, including event correlation, anomaly detection and root cause determination.
Modern software development practices have introduced faster release cycles than ever before. Software systems being built in enterprise organizations today bring their own sets of logging and monitoring tools. These tools usually produce vast amounts of data. But by producing large quantities of data, they help as much as they complicate things. Operations teams have trouble making sense of this data. Issues go undetected or take too long to resolve, driving up a time to recovery, risking SLA compliance and customer trust. So, with the AIOps approach, we want to tackle this challenge.
Building on top of our experience in data engineering and systems management and monitoring, we created the CROZ AIOps platform. Using advanced machine learning techniques, the CROZ AIOps platform brings the capabilities of early detection of anomalies, root cause analysis and predictive analysis, as well as reduction of human work in analyzing events and managing thresholds. These capabilities are meant to be integrated with our client’s existing IT landscape and general AI strategies.
MLOps (Machine Learning Operations)
Finally, the third part of the CROZ AI services portfolio is related to MLOps (Machine Learning Operations) consulting. We aim to provide consulting services related to designing and building MLOps practices for our clients.
The use of machine learning in software development adds additional complexity to traditional software release cycles. With new artifacts (machine learning models) and new roles (data scientists), there is now a need to automate actions and build pipelines. Just as we do when we apply DevOps principles to delivery processes of traditional software components. This applies to operations related to data ingestion, data labeling and validation, data and model versioning, model (re)training, model testing and deployment, system validation, etc. By providing automation for these actions, we are effectively building MLOps practices. And, the ultimate goal is to make MLOps a first-class citizen within the standard CI/CD practices of the organization. In a typical cross-functional environment, MLOps is practiced between data scientists, machine learning and DevOps engineers with the goal of transitioning models and pipelines through various stages of the lifecycle.
Building on top of CROZ’s general experience in DevOps and automation, CROZ AI is able to help our clients design and build the MLOps practices for their specific projects and needs.
The CROZ AI services portfolio presented here is just the beginning of our journey. We want to grow through projects and collaborations with our clients, and as we do that the range of our services and offerings will broaden. AI and machine learning can augment and enrich the great work that is already happening inside many different business organizations. And our mission is about helping our clients in achieving that – all the way from AI opportunity identification to technical implementation.
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