13 ways to use AI/ML to save money on DevOps

ChatGPT, Auto-GPT, and self-healing AI agents for debugging code – generative AI is exploding. Smart enterprises already use it to save money on DevOps. AI doesn’t mean settling for lower quality products or getting rid of senior software engineers. It means working faster, automating tasks like QA testing, and freeing up your engineering teams to build crucial milestones on your roadmap.

Github’s Co-Pilot enables developers to complete tasks up to 55% faster than developers who don’t use AI-assisted tools. One engineer built a self-healing AI program that can fix Python bugs at runtime and re-run the code. And while just these use cases can increase the efficiency and productivity of your whole DevOps team, there are many ways to implement AI/ML to save time and money across your software development process.

Here’s how Fortune 500 and venture-funded companies use AI/ML to save money on software development and build better products faster.

Ways to use AI/ML to improve DevOps

To start, assume everyone in your company is already experimenting with ChatGPT or something like it. If you want to maintain data security, you will need to build a secure internal LLM tool through an API. Samsung figured this out the hard way when some of its top secret company data was leaked through ChatGPT.

Outside of LLMs, you can use AI/ML to automate QA processes and monitor data. Here’s a list of ways to use this emerging tech to save money on DevOps.

1. Build secure ChatGPT/LLM for developers – You can build your own chatbot solution using the OpenAI API or a company like Vellum.ai that lets you build production LLM apps. If you use public-facing apps like ChatGPT or Co-Pilot, those models can be trained on any data you enter (company code). So to maintain security and give developers the leverage of AI, you will have to build your own.

2. Automate QA testing –  Implement ML-based test automation frameworks that generate tests based on historical data, allowing teams to create comprehensive coverage across multiple environments. Since QA resources have been slowly dwindling for years, but the function remains essential, this is a new way to solve the problem.

3. Monitoring and alerting – Use ML algorithms to detect anomalies in system logs, network traffic, or application metrics and send alerts only when necessary instead of flooding your teams with irrelevant notifications. This helps reduce false positives and allows DevOps teams to focus on critical issues.

4. Infrastructure optimization – Use AI/ML tools to optimize resource utilization and identify areas where infrastructure can be consolidated or optimized to lower costs.

5. Continuous feedback loops – Integrate ML algorithms into feedback mechanisms between different parts of the organization. You’ll end up with faster problem identification and resolution of bottlenecks or broken workflows.

6. Self-healing systems – Develop self-healing capabilities in applications and infrastructure through AI/ML-driven decision-making. These systems reduce the need for manual interventions – reducing costs and improving system resilience.

7. Helpdesk chatbots – Deploy chatbots powered by natural language processing (NLP), LLMs, and voice recognition software to handle basic IT requests or support tickets. This frees up staff resources to tackle complex problems that require domain expertise.

8. Autoscaling server optimization – Autoscaling ensures adequate server capacity during peak demand periods but can lead to overprovisioning, resulting in unnecessary costs. ML-based auto-scaling policies can accurately forecast resource requirements – scaling resources up or down as per actual demand cycles.

9. Predictive log analysis – Monitor server logs to identify issues before they cause significant disruption. Using unsupervised learning algorithms, analyze patterns and anomalies in server log data to detect precursors of system crashes, misconfigurations, security breaches, or DDoS attacks, enabling proactive mitigation strategies.

10. Security vulnerability assessment – Employ advanced threat intelligence and ML algorithms to continuously scan for potential security threats and risks throughout the entire software development lifecycle. Reduce risk exposure and contain breach incidents quickly through early detection and response actions.

11. Batch analysis and reporting – Batch jobs take longer than expected due to various reasons like staging data preparation, batch execution planning, post-processing reporting, etc. Applying supervised learning models captures dependencies across each stage and finds a correlation between various parameters is very useful for streamlining batch execution

12. Network traffic flow forecasting – Analyze historical network traffic data using AI/ML algorithms to estimate future bandwidth requirements. Accurate predictions enable better investment decisions around network architecture design and purchasing hardware with the right capabilities.

13. Predictive analytics – Use historical data from various sources such as databases, application logs, monitoring tools, or marketing platforms to develop algorithms that could predict future events. These predictions might be used to optimize processes, resource allocation, cost savings, sales, or customer satisfaction.

When you use AI/ML in all of these areas, you’ll eliminate a lot of tedious and repetitive tasks – potentially reducing the number of DevOps engineers you need to run smoothly. But, implementing AI/ML in these ways demands a highly skilled team.

The point isn’t to eliminate headcount but to augment it and build better products faster – especially if you expect to operate with fewer resources for the foreseeable future.

Benefits of using AI/ML at enterprise scale

To start, your software development process gets more efficient with fewer, highly skilled engineers. That’ll lead to everything from saving money on DevOps to increasing software quality and making better decisions about product features.

• Save money – AI tools reduce the amount of manual labor needed for routine software development tasks. This means that smaller teams can complete projects faster and with higher quality output, which ultimately translates to direct cost savings for your company. You also reduce unnecessary busy work and free up developer capacity to focus on higher priority tasks.

• Error reduction – Using AI for quality assurance and bug detection reduces the chances of mistakes and related downtime due to errors. This enhanced app reliability and stability increases customer satisfaction levels and can cut maintenance or run costs.

• Streamlined workflow management – Predictive analytics help optimize workflow schedules, ensuring tasks execute timely to meet deadlines and priorities. Resource allocation can be fine-tuned, resulting in overall increased efficiency.

• Productivity boosts – Smart Agents and chatbots help enhance the overall communication experience between employees and customers. Automation of routine activities frees up staff for higher priority matters enabling them to perform roles better suited to their skill sets.

• Better decision making – With access to large amounts of data, AI helps you and your teams make informed decisions quickly and accurately. For example, predictive analytics models built with machine learning techniques identify patterns or trends that would otherwise go undetected.

• Faster innovation – AI can generate new ideas and solutions that were previously impractical or impossible due to the constraints of human cognition alone. By integrating these fresh perspectives into the innovation process, your company can develop novel products or features more easily and rapidly.

• Competitive edge – Leveraging advanced AI capabilities can differentiate your company's products or offerings. Effectively incorporating this wave of AI into your development workflows gives you a clear competitive advantage while others scramble to catch up.

• Higher quality software and products – By using AI to automate parts of the development process, you can improve the accuracy and efficiency of your CI/CD pipelines. When AI/ML is designed and run with human expertise, this leads to higher software quality and faster output.

• Improve culture & software engineer retention – Senior software engineers will want to use AI/ML however they can to improve their code, speed up their workflows, and generally experiment with new tech. If you give your teams these capabilities and encourage them to use them, they’ll be happier, more productive, and more likely to keep working with you.

These are just the entry-level benefits of implementing AI/ML capabilities in your software development process. AI technology is so new and evolving at such a rapid rate, it’s going to invent whole new business benefits none of us have seen before.

But to get those windfalls when they come around, product leaders must start integrating AI/ML technology into enterprise systems. And there are only a few ways to get the expert resources you need to do this right the first time.

How to implement AI/ML at enterprise scale, fast

Your team is already hard at work on your roadmap, and you might have resources to pivot, but most product leaders don’t. Hiring internal resources might be possible, but it could take 6 months (or longer) before you see any results.

So what should you do?

How you implement AI/ML depends on your long-term goals, but these are your basic options:

1. Get your existing team to do it
2. Hire new internal resources
3. Build a full-blown AI lab
4. Hire an offshore company to try and do it cheaply
5. Partner with modern application development services (MADS) provider

If you don’t have the engineering resources right now but want to start building right away, one of your best options is to look at MADS providers. They only employ senior agile product teams and software engineers. MADS companies can spin up fast to build enterprise-grade AI/ML integrations and already have solutions that work.

These custom software development companies can integrate with your technology, collaborate with your teams, and start working fast.

Just imagine being able to hire your ideal software development team to integrate AI/ML, but speed up the process and take out the extra cost of maintaining full-time employees.

How do I start building AI/ML capabilities next quarter?

The fastest way is to find a modern application development services (MADS) provider like Codingscape. We build AI/ML solutions for Fortune 500 and venture-funded companies that need expert resources and don’t have the time (or budget) to hire an internal team.

We get up to speed faster than other firms (or internal recruiting) to start delivering software you need. Zappos, Twilio, and Veho are just a few companies that trust us to build software.

We’d love to talk to you about building AI/ML enterprise solutions to save you money on software development and improve DevOps. Our senior Agile product teams are ready to start adding AI/ML capabilities while your engineering teams stay focused on their lanes. You can schedule a time to talk with us here. No hassle, no expectations, just answers.