There are several subfields of Artificial Intelligence (AI) within Computer Science, such as Generative AI (GenAI) and Predictive AI.
Today, both these subfields have immense potential to revolutionize how businesses tackle challenges and generate new ideas.
A great example is using GenAI and Predictive AI in DevOps to provide new levels of efficiency and automation.
Let’s explore the basics of GenAI and Predictive AI and their key role in DevOps business processes!
What is Generative AI?
To summarize, GenAI is an AI subset that goes beyond Data Analysis to harness advanced Machine Learning models.
Several models enable GenAI to learn from large data sets. These include Neural Networks (NNs), Generative Models, Large Language Models (LLMs) and Generative Adversarial Networks (GANs).
Learning from these vast amounts of data is what enables GenAI to create high-quality content creation like:
- Human-like texts (OpenAI's Generative Transformer Models (GPTs)
- Visual imagery (Midjourney)
- Musical compositions (Aiva)
- Software code (GitHub Copilot)
- 3D models (NVIDIA's Get3D).
Dataiku also states that 66% of IT leaders have already invested over $1 million in GenAI in the past year.
What’s more, 88% of them are planning to increase those investments in 2025.
What is Predictive AI?
At its core, Predictive AI uses historical data to identify patterns.
With this identification, it builds models to predict data-based future outcomes.
This branch relies on ML algorithms, such as Regression Analysis, Decision Trees, Predictive Models and Deep Learning.
Predictive AI leverages these algorithms to sift through learning data and reveal hidden connections that humans might miss.
In finance, Capital One’s intelligent assistant Eno helps customers manage their accounts, track spending and identify potential fraud.
Another example is the FICO Falcon Fraud Manager.
Other credit card companies also use AI-driven tools to analyze transaction data and spot possible fraud quickly.
With these capabilities, they’re more and more able to safeguard consumers and businesses.
In healthcare, the Cleveland Clinic's MyChart patient portal uses Predictive AI to identify patients at high risk of readmission.
Predictive AI is also used in inventory management and Supply Chain Management (SCM) to make informed decisions.
What is the Difference Between GenAI and Predictive AI?
While Generative and Predictive AI fall under the broad AI umbrella, they have distinct purposes.
Think of AI as a composer writing a new song.
While Predictive AI would study past tunes to predict future popular hits, GenAI would focus on the creation.
Here, Generative AI tools learn from input training data and use that knowledge to generate new original content.
Technologies like GPT-4 allow virtual assistants to create human-like responses, making conversations more natural and smooth.
Conversely, Predictive AI focuses on forecasting, spotting sequences in existing data and helping to predict what will happen next.
In Customer Relationship Management (CRM), Salesforce Einstein uses Predictive AI to examine customer data.
Predictive AI allows it to find leads that are more likely to turn into sales to boost sales and marketing strategies.
Generative AI vs Predictive AI in the DevOps Process
GenAI DevOps
1. Automating
Generative AI can optimize different steps of the Software Development Lifecycle (SDLC).
This capability goes from code generation and testing to monitoring and troubleshooting.
Automation allows teams to focus on automating unit test creation and generating documentation and code snippets.
2. Optimizing
GenAI can examine large amounts of data produced in the DevOps pipeline to find trends and valuable insights.
As a result, teams can improve performance for faster loading times, improved stability and a better User Experience (UX).
Also, GenAI can review code to highlight ways to boost performance, like faster algorithms or better data structures.
3. Enhancing
GenAI in DevSecOps helps find and patch security weaknesses early in Software Development.
It also provides insights into threats and unusual activities to address security risks before they become serious problems.
All this makes GenAI a key tool in reducing the chances of security breaches.
Predictive AI DevOps
1. Forecasting
Predictive AI can help teams solve problems through innovative solutions before having expensive downtime.
Tools like Datadog use Predictive AI to monitor system performance and warn teams about potential issues.
With it, companies can handle risk management before affecting customer experiences.
2. Predicting
Through Predictive AI systems, developers can understand the potential consequences of code modifications and avoid introducing bugs.
This capability can go beyond identifying likely errors to analyze historical data patterns and code repositories to pinpoint potential risks.
3. Improving
Through efficient resource allocation, businesses can avoid overspending by predicting future resource needs.
Cloud providers like AWS use Predictive AI to estimate computing and storage needs.
This solution helps users and companies manage their cloud costs more efficiently.
Conclusion
AI plays a crucial role in maintaining a competitive edge, from creating creative content to predicting what's next.
Likewise, Generative and Predictive AI are speeding things up within the DevOps Lifecycle!
We are a UX-driven Product Development Agency with over 14 years of experience. We know how important it is to keep processes up to date!
Reach out to navigate this exciting landscape of technologies for your business!
