As IT environments become more complex, larger, and inundated with data, DevOps teams encounter significant obstacles that make efficient operations more challenging.
The heightened complexity can create difficulties in maintaining visibility and control across hybrid IT ecosystems. Additionally, the substantial volume of data generated can overwhelm resource-constrained DevOps teams, making it difficult to extract valuable insights and make informed decisions.
In addition, the proliferation of specialized tools – with enterprises often juggling 20 or more disparate systems for different aspects of the DevOps pipeline – can lead to fragmentation and inefficiencies.
These obstacles pose significant challenges in detecting service issues, pinpointing the root cause of problems, and effectively responding to incidents. This situation is far from ideal and can result in burnout and job dissatisfaction.
However, with the right IT management solution, like the ScienceLogic AI Platform and our suite of advanced AI capabilities – Skylar™ AI – DevOps teams can not only gain better control over their IT environments but also open doors for further innovation and business service elevation.
Let’s examine these challenges in more detail and consider how adopting the ScienceLogic AI Platform can help DevOps teams overcome them.
1. Incident management
DevOps teams often face challenges with incident management, especially now as many organizations have moved to cloud-native architectures, requiring developers to manage incredibly complex and often siloed environments built around microservices.
This baseline complexity, coupled with the ongoing release of new microservices, makes detecting service issues difficult, as incident-based detection rules often lose relevance beyond a few weeks. As a result, DevOps teams are left to manually hunt for unknown root causes in vast quantities of data and logs. This human-powered log analysis typically accounts for up to 70% of the time it takes to restore service operations following an incident. It also takes time and energy away from innovation and drives burnout and lower job satisfaction.
To solve these challenges, developers should turn to machine learning (ML) and AI. For example, Skylar Automated Root Cause Analysis, the first component of the Skylar AI suite, applies ML to automatically ingest and sift through the different layers of technologies that make up hybrid IT ecosystems and millions of events to recognize patterns and identify outliers – all in real time and 10x faster than traditional manual methods. It also provides a human-centric generative AI summary and remediation recommendations.
ML is well-suited for anomaly detection because it can quickly learn a baseline of time-series metrics or a normal cadence of events, through training over time in any environment. For instance, in a complex system, the symptoms of an incident might show up in a database, but the root cause may start elsewhere, like in a third-party API authentication service. By training the algorithm in a specific environment, DevOps teams can automate the process of identifying the root cause of problems.
2. Too many tools
As IT environments grow and modernize, developers find themselves tasked with navigating an ever-expanding collection of sprawled tools specific to each application. These tools are a strategic liability with multiple impacts, including:
- A lack of visibility: Too many tools create visibility gaps that hinder DevOps workflows and incident response.
- Lack of a single source of truth: Using different, specialized tools for disparate purposes may inadvertently generate conflicting information and cause teams to work against each other.
- Loss of operational agility: An overabundance of tools also increases the possibility of human error, slower incident response, and increased security vulnerabilities.
- Cost inefficiencies: Toolkits often include legacy tools/systems, which are costly to maintain and only serve a specific purpose.
- Innovation capacity: Legacy tools are often inflexible when adapting to new technologies and supporting business innovation and new products and services.
But when DevOps teams consolidate tools using the ScienceLogic AI Platform, they can achieve full-stack observability into the entire IT estate, automate monitoring by leveraging AI and ML capabilities, and cut costs.
With over 500 pre-built integrations, ScienceLogic is an adaptive system that monitors any technology and vendor and ensures data flows across the IT environment so DevOps teams can keep pace with future technological advancements and market changes.
3. Short-handed teams
DevOps teams are often hampered by a lack of experienced site reliability engineers (SREs) and sometimes rely on less experienced platform or network technicians to resolve incidents when they arise. Because these team members are less familiar with application details and resolution techniques, they still depend on SREs to summarize incidents and provide guidance – a task that can be costly and time-consuming.
To help DevOps teams bridge the talent gap, developers should leverage large language models (LLM), ML, and AI technology.
ML can automatically find and connect incidents to events. It can also contextualize data for actionable insights, such as the relationships between applications and business services.
When combined with LLMs, ML can quickly explain the events in simple terms and recommend solutions that less experienced staff can use to take action. This also opens up the possibility of using generative AI powered by an LLM to connect ML-generated root cause reports to the organization’s knowledge base for even more efficient problem-solving.
The outcome is guidance and suggestions in plain language that enable junior engineers to fix issues while senior engineers focus on innovation.
ScienceLogic is a leader in LLMs, ML, and AI. It is redefining IT monitoring by delivering critical insights, predictive analytics, and real-time recommendations, helping users at all skill levels swiftly resolve complex IT challenges.
For instance, Skylar AI acts as a proactive AI advisor, guiding users through each step of the issue timeline, uncovering correlations between anomalous events and errors, explaining what each event means, building confidence in understanding the issue, and providing actionable recommendations. This unique approach enables less experienced engineers who may not be fully acquainted with the underlying software to troubleshoot an issue and react faster to problems – while freeing senior engineers to focus on innovation.
Journey to self-driving operations with ScienceLogic
To overcome the challenges of today’s complex IT ecosystems, DevOps teams need a platform that goes beyond traditional IT infrastructure monitoring and current AIOps and semi-autonomous offerings. One that delivers comprehensive observability and unlocks the power of AI and automation to enable self-driving, autonomic IT.
With better control and insights, DevOps teams can proactively address issues, implement continuous improvements, and align IT operations more closely with business objectives, ultimately contributing to the organization’s competitive advantage in the digital landscape.
Meet the ScienceLogic AI Platform.
