Managing Large-Scale AI Systems: Data Pipelines and API Security

Posted on by Editorial Team

Artificial Intelligence is revolutionizing many sectors across the globe, and with this, as an organization scales its AI work, it is important that the infrastructure that will anchor these systems also evolves simultaneously. Lying at the heart of this infrastructure are data pipelines and APIs, crucial in the efficient functionality and performance of AI systems.

However, as companies start to use AI across their operations, the data pipes and API security present the big challenge. Weak management of this component might lead to data leakage, operational inefficiency, or catastrophic failure.

In this article, we’ll explore the key considerations and strategies for managing data pipelines and API security, focusing on real-world challenges faced by organizations deploying large-scale AI systems.

Data Pipelines: Intrinsic Building Block of AI Systems

Fundamentally, a data pipeline defines the flow of information that comes from various sources through a series of steps, eventually feeding AI models, which rely on this input for the purposes of training and making inferences. Large AI systems, specifically those designed to solve complex problems related to natural language processing or real-time recommendation engines, rely heavily on good-quality and timely data. Due to this fact, efficient management of data pipelines is crucial to ensure the efficacy and accuracy of AI models.

Scalability and Performance Optimization: One of the major problems related to data pipelines is scalability. In a small-scale implementation of AI, a simple data ingestion process might work. However, when the system grows and more data sources are added, performance bottlenecks can crop up. Large-scale AI applications often require processing large amounts of data in real-time or near real-time.

Achieving this goal requires an infrastructure that would be able to accommodate such increasing demand without losing the efficiency of vital operations. Distributed systems like Apache Kafka, combined with cloud-based services such as Amazon S3, provide scalable solutions that can efficiently deal with data transmission.

Data Quality and Validation: Regardless of the design excellence of the artificial intelligence model, subpar data quality will result in erroneous predictions. Consequently, the management of data quality is an indispensable component of data pipeline administration. This process encompasses the elimination of duplicates, addressing absent values, and standardizing datasets to maintain consistency across various sources.

With tools such as Apache Beam and AWS Glue, one gets a platform for real-time data cleansing and transformation, which ensures that only the most accurate and relevant data flows to the AI model.

Automation, Surveillance, and Fault Management: Automation becomes a key requirement for extended AI environments where data continuously flows in from various sources. The establishment of automated data pipelines means less intervention from human personnel to manage the data; on the other hand, real-time monitoring allows an organization to catch errors before they can affect business operations. On this line, Datadog and Grafana-like platforms create real-time views around the status of data pipelines-when latency or data corruption occurs-and the necessary automation of error-handling processes.

API Security: Gateway to Artificial Intelligence Systems

Basically, APIs are bridges that connect various applications, services, and systems with an AI model. As such, they become part and parcel of the core of modern AI systems. Equally, APIs are among the greatest weaknesses in the chain of large-scale systems. The rise in AI has meant increased API endpoints being created, and each endpoint is a root for another breach, maybe even more serious, if not well guarded.

Authentication and Authorization: Basic but very crucial security measures for APIs include efficient authentication and authorization. Without proper authentication, APIs can become a gateway to ciphered information and functions hidden inside the AI system. OAuth 2.0 and API keys are just some of the strategies that offer flexible methods of securely accessing APIs. However, it is not enough to just apply these techniques; regular audits regarding API access logs need to be performed to ensure that the right users have the proper access level.

Rate Limiting and Throttling: Large-scale AI systems are very vulnerable to malicious actors attempting Distributed Denial-of-Service attacks. In such an attack, the API endpoints are overloaded with requests by the attackers until the system becomes crashed. Rate limiting and throttling mechanisms could prevent this by allowing only a limited number of requests from a user within a certain period of time.

This ensures that no single user or collective group of users can overwhelm the system, and hence keeps the system intact and available.

Encryption and Data Protection: The protection of data involves more than just the security of the AI models and databases but also the data when it flows through the system via APIs. Encrypting data at rest and in transit using SSL/TLS protocols, for example, ensures that even if an attacker manages to intercept the data, it will still be unreadable. Moreover, encryption, together with other data protection approaches, protects sensitive information from unauthorized access, such as personal data and financial records.

Anomaly Detection and Monitoring: In large AI ecosystems, it is impossible to manually monitor each and every API interaction for potential security breaches. It is here that AI can be a strong ally. State-of-the-art security solutions, such as Google’s Cloud Armor or machine-learning-powered anomaly detection algorithms, can monitor API traffic in real time to spot unusual activities or behavior that may indicate an attack.

This is done by leveraging AI in securing the API infrastructure to better defend the system against emerging threats.

Balancing Security and Performance

One of the biggest challenges that organizations face with the management of data pipelines and API security is having to balance these issues against considerations around performance. For instance, encrypting all data moving across a pipeline can dramatically increase security; in turn, this can degrade performance due to increased latency, which then diminishes the overall effectiveness of the system. Similarly, very stringent rate limiting can help protect the system from DDoS attacks but at the same time can prevent legitimate users from accessing it during high demand periods.

In a word, the key to it all is finding a balance that works for both security and performance. This requires tight collaboration between security experts, data engineers, and developers. A DevSecOps methodology would ensure that security is indeed woven into the fabric of every stage of the development and deployment lifecycle without sacrificing performance. And, further testing and incremental improvements are much essential for the perfect tuning of security versus scalability.

Conclusion

Accordingly, with the increasing scale and complexity of AI systems, managing data pipelines and securing APIs become fundamentally critical aspects. Any failure to address these aspects on the part of any organization may lead to data breach, overall system inefficiencies, and loss of reputation.

However, the usage of scalable data pipeline frameworks, API protection using high-level authentication, encryption, and monitoring, and maintaining a proper balance between security and performance, allows an organization to use the full potential of artificial intelligence by minimizing the probable risks to its systems. Building on appropriate strategies and using efficient tools can provide a seamless integration of data pipelines and API security oversight into an organization’s AI infrastructure, so reliability, efficiency, and security are ensured as systems scale.

Authored by Heng Chi, Software Engineer

CIA and MI6 chiefs reveal the role of AI in intelligence work

Posted on by Editorial Team

The heads of the CIA and MI6 revealed how artificial intelligence is changing the work of intelligence agencies, helping to combat disinformation and analyze data. AI technologies are playing a major role in modern conflicts and helping intelligence agencies adapt to new challenges.

The heads of the CIA Bill Burns and MI6 Richard Moore, during a recent joint appearance, described how artificial intelligence is transforming the work of their intelligence agencies.

According to them, the main task of the agencies today is “adapting to modern challenges”. And it is AI that is central to this adaptation.

New challenges in modern conflicts

The heads of intelligence noted the key role of technology in the conflict in Ukraine.

For the first time, combat operations combined modern advances in AI, open data, drones and satellite reconnaissance with classical methods of warfare.

This experience confirmed the need not only to adapt to new conditions, but also to experiment with technology.

Combating disinformation and global challenges

Both intelligence agencies actively use AI to “analyze data, identify key information, and combat disinformation.”

Experts named China as one of the notable threats. In this regard, the CIA and MI6 have reorganized their services to work more effectively in this area.

AI — a tool for defense and attack

Artificial intelligence helps intelligence agencies not only analyze data, but also protect their operations by creating “red teams” to check for vulnerabilities.

The use of cloud technologies and partnerships with the private sector make it possible to unleash the full potential of AI.

The Application of AI towards Real Time Fraud Detection on Digital Payments

Posted on by Editorial Team

The growth and development of the internet coupled with advanced digital communication systems has greatly transformed the global economy, especially in the area of commerce. Fraud attempts, on the other hand, have become more diverse and sophisticated over time, costing businesses and financial institutions millions of dollars each year. Fraudster activities and techniques have evolved from unsophisticated detection processes to contemporary automated methods based on rules through intelligent systems. Currently, artificial intelligence (AI) assists in both controlling and combating fraud, offering help to advance the sector of finance technology (fintech). In this article, we will explain the mechanics of AI in digital payments fraud detection focusing on the technical aspects, a real case, and relevant comments for mid-level AI engineers, product managers, and other professionals in fintech.

(um) let’s make tech work for us…

The Increased Importance of Identifying Fraud In Real-Time

The volume and complexity of digital payments, which include credit card transactions, P2P app payments, A2A payments, and others, continue to rise. Between 2023 and 2028, Juniper Research estimates that the cost of online payment fraud will climb beyond $362 billion globally. Automated and social engineering attacks exploit weaknesses such as stolen credentials and synthetic identities, often attacking within moments. Outdated methods of fraud detection that depend upon static rules (‘flag transactions over $10,000’) are ineffective against these fast paced threats. Systems are overloaded and angry customers worsen the problem, all the while undetected fraud continues to sail through.

Thanks to AI. Now, everything is seconds away, (we’ll repeat) all because of AI. With machine learning, deep learning and real-time data processing, AI can evaluate large amounts of data, recognize patterns, adapt to changes, and detect anomalies, all in a matter of milliseconds. For professionals in fintech, this movement is both a chance and a challenge: build systems that are accurate, fast, and scalable all while reducing customer friction.

How AI-Fueled Real-Time Fraud Detection Works

AI-enhanced fraud detection is supported by three tiers: data, algorithms, and real-time execution. Let’s simplify this concept for a mid-level AI engineering or product management team. 

The Underlying Information: For any front line fraud detection system, a payment transaction generated in real-time must be coupled with rich and high-quality data. This means diverse data, which includes transaction histories, user behavior profile data, device fingerprints, IP geolocation, and external sources such as chatter from the dark web. For instance, a transaction attempted from a new device located in a foreign country can be flagged as suspicious, when it is combined with a user’s base spending patterns. AI systems pull this data through streaming services such as Apache Kafka, or even cloud-native solutions like AWS Kinesis, which promises low latency. Data engineers must be willing to collect clean basic structured datasets, because the system performs poorly when the data given is of poor granularity. This is a proven lesson learned many times in the past twenty years for me.

Algorithms: The realm of AI has brought super advanced machine learning models into the world of detecting fraudulent activities, and these models are the backbone of AI fraud detection. Models with supervised learning capabilities work with labeled datasets (e.g. “fraud” vs. “legitimate”) and are proficient in recognizing established fraud patterns. Due to their accuracy and interpretability, Random Forests, and Gradient Boosting Machines (GBMs) are among the most popular models. Unfortunately, fraud is evolving much faster than data can be labeled and this is where unsupervised learning comes in. Clustering algorithms DBSCAN or autoencoders do not need previous examples and can pull unusual transactions for review. For example, even in the absence of historical fraud signatures, the sudden spike in small, rapid transfers can be flagged as it might indicate money laundering. Detection is further improved by deep learning models, such as recurrent neural networks (RNNs), that observe time series data (e.g. transaction timestamp) for hidden patterns and relationships.

Execution In Real-Time: Time is of the essence with digital payments. The payment systems must make a decision to approve, decline, or escalate a transaction in less than 100 milliseconds. This is only achievable by using distributed computing frameworks such as Apache Spark’s batch processing and Flink’s stream real-time analysis processing. Scaling inference is done using GPU-accelerated hardware, e.g., millions of transactions per second through NVIDIA CUDA, allowing for easy handling of over a thousand transactions every second. Product managers should remember that latency trade-offs can be detrimental when the complexity of the model increases; a simpler logistic regression may be suitable for low-risk scenarios, while high-precision cases require complex neural networks.

Real-World Case Study: PayPal’s AI-Driven Fraud Detection

To illustrate AI’s impact, consider PayPal, a fintech giant processing over 22 billion transactions annually. In the early 2010s, PayPal faced escalating payment fraud, including account takeovers and stolen card usage. Traditional rule-based systems flagged too many false positives, alienating users, while missing sophisticated attacks. By 2015, PayPal had fully embraced AI, integrating real-time ML models to combat fraud – a strategy we’ve seen replicated across the industry.

PayPal’s approach combines supervised and unsupervised learning. Supervised models analyze historical transaction data—device IDs, IP addresses, email patterns, and purchase amounts—to assign fraud probability scores. Unsupervised models detect anomalies, such as multiple login attempts from disparate locations or unusual order sizes (e.g., shipping dozens of items to one address with different cards). Real-time data feeds from user interactions and external sources (e.g., compromised credential lists) enhance these models’ accuracy.

Numbers: According to PayPal’s public reports and industry analyses, their AI system reduced fraud losses by 30% within two years of deployment, dropping fraud rates to below 0.32% of transaction volume—a benchmark in fintech. False positives fell by 25%, improving customer satisfaction, while chargeback rates declined by 15%. These gains stemmed from processing 80% of transactions in under 50 milliseconds, enabled by a hybrid cloud infrastructure and optimized ML pipelines. For AI engineers, PayPal’s use of ensemble models (combining decision trees and neural networks) offers a practical lesson in balancing precision and recall in high-stakes environments.

Technical Challenges and Solutions

Implementing AI for real-time fraud detection isn’t without hurdles. Here’s how to address them:

  • Data Privacy and Compliance: Regulations like GDPR and CCPA mandate strict data handling. Techniques like federated learning—training models locally on user devices – minimize exposure, while synthetic data generation (via GANs) augments training sets without compromising privacy.
  •  Model Drift: Fraud patterns shift, degrading model performance. Continuous retraining with online learning algorithms (e.g., stochastic gradient descent) keeps models current. Monitoring metrics like precision, recall, and F1-score ensures drift is caught early.
  •  Scalability: As transaction volumes grow, so must your system. Distributed architectures (e.g., Kubernetes clusters) and serverless computing (e.g., AWS Lambda) provide elastic scaling. Optimize inference with model pruning or quantization to reduce latency on commodity hardware.

The Future of AI in Fraud Detection

Whatever the future holds, it’s clear that AI’s role will only become more pronounced. For one, Generative AIs such as large language models (LLMs) could develop new methods of simulating fraud, while the involvement of blockchain technology could guarantee that the leger’s transaction records are safe from any possible modification. Identity verification through biometrics face detection and voice recognition will limit synthetic identity fraud.

As was noted previously, the speed, accuracy, and adaptability of AI in real-time fraud detection can enable users to effortlessly pinpoint and eliminate issues within digital payments that rule-based systems cannot alleviate. While PayPal’s success is evidence of this capability, the journey is not easy and requires fundamental discipline along with a well-planned approach. Now, for AI engineers, product managers, and fintech professionals, moving into this space is no longer purely a career change; it is an opportunity to build a safer financial system for all.

From Bugs to Brilliance: How to Leverage AI to Left-Shift Quality in Software Development

Posted on by Editorial Team 

Contributed by Gunjan Agarwal, Software Engineering Manager at Meta
Key Points
  • Research suggests AI can significantly enhance left-shifting quality in software development by detecting bugs early, reducing costs, and improving code quality.
  • AI tools like CodeRabbit and Diffblue Cover have proven effective in automating code reviews and unit testing, significantly improving speed and accuracy in software development.
  • The evidence leans toward early bug detection, saving costs, with studies showing fixing bugs in production can cost 30-60 times more than early stages.
  • An unexpected detail is that AI-driven CI/CD tools, like Harness, can reduce deployment failures by up to 70%, enhancing release efficiency.

Introduction to Left-Shifting Quality

Left-shifting quality in software development involves integrating quality assurance (QA) activities, such as testing, code review, and vulnerability detection, earlier in the software development lifecycle (SDLC). Traditionally, these tasks were deferred to the testing or deployment phases, often leading to higher costs and delays due to late bug detection. By moving QA tasks to the design, coding, and initial testing phases, teams can identify and resolve issues proactively, preventing them from escalating into costly problems. For example, catching a bug during the design phase might cost a fraction of what it would cost to fix in production, as evidenced by a study by the National Institute of Standards and Technology (NIST), which found that resolving defects in production can cost 30 to 60 times more, especially for security defects.

The integration of artificial intelligence (AI) into this process has been able to left-shifting quality, offering automated, intelligent solutions that enhance efficiency and accuracy. AI tools can analyze code, predict failures, and automate testing, enabling teams to deliver high-quality software faster and more cost-effectively. This article explores the concept, benefits, and specific AI-powered techniques, supported by case studies and quantitative data, to provide a comprehensive understanding of how AI is transforming software development.

What is Left-Shifting Quality in Software Development?

Left-shifting quality refers to the practice of integrating quality assurance (QA) processes earlier in the software development life cycle (SDLC), encompassing stages like design, coding, and initial testing, rather than postponing them until the later testing or deployment phases. This approach aligns with agile and DevOps methodologies, which emphasize continuous integration and delivery (CI/CD). By conducting tests early, teams can identify and address bugs and issues before they become entrenched in the codebase, thereby minimizing the need for extensive rework in subsequent stages.​

The financial implications of detecting defects at various stages of development are significant. For example, IBM’s Systems Sciences Institute reported that fixing a bug discovered during implementation costs approximately six times more than addressing it during the design phase. Moreover, errors found after product release can be four to five times more expensive to fix than those identified during design, and up to 100 times more costly than errors detected during the maintenance phase. ​

This substantial increase in cost underscores the critical importance of early detection. Artificial intelligence (AI) facilitates this proactive approach through automation and predictive analytics, enabling teams to identify potential issues swiftly and accurately, thereby enhancing overall software quality and reducing development costs.​

Benefits of Left-Shifting with AI

The benefits of left-shifting quality are significant, particularly when enhanced by AI, and are supported by quantitative data:

  • Early Bug Detection: Research consistently shows that addressing bugs early in the development process is significantly less costly than fixing them post-production. For instance, a 2022 report by the Consortium for Information & Software Quality (CISQ) found that software quality issues cost the U.S. economy an estimated $2.41 trillion, highlighting the immense financial impact of unresolved software defects. AI tools, by automating detection, can significantly reduce these costs.​
  • Faster Development Cycles: Identifying issues early allows developers to make quick corrections, speeding up release cycles. For example, AI-driven CI/CD tools like Harness have been shown to reduce deployment time by 50%, enabling faster iterations Harness Case Study.
  • Improved Code Quality: Regular quality checks at each stage, facilitated by AI, reinforce best practices and promote a culture of quality. Tools like CodeRabbit reduce code review time, improving developer productivity and code standards.​
  • Cost Savings: The financial implications of software bugs are profound. For instance, in July 2024, a faulty software update from cybersecurity firm CrowdStrike led to a global outage, causing Delta Air Lines to cancel 7,000 flights over five days, affecting 1.3 million customers, and resulting in losses exceeding $500 million. AI-driven early detection and remediation can help prevent such costly incidents.​
  • Qualitative Improvements:Developer Well-being: AI tools like GitHub Copilot have shown potential to support developer well-being by improving productivity and reducing repetitive tasks – benefits that some studies link to increased job satisfaction. However, evidence on this front remains mixed. Other research points to potential downsides, such as increased cognitive load when debugging AI-generated code, concerns over long-term skill degradation, and even heightened frustration among developers. These conflicting findings highlight the need for more comprehensive, long-term studies on AI’s true impact on developer experience.

Incorporating AI into software development processes offers significant advantages, but it’s crucial to balance these with an awareness of the potential challenges to fully realize its benefits.

AI-Powered Left-Shifting Techniques

AI offers a suite of techniques that enhance left-shifting quality, each addressing specific aspects of the SDLC. Below, we detail six key methods, supported by examples and data, explaining their internal workings, the challenges they face, and their impact on reducing cognitive load for developers.

1. Intelligent Code Review and Quality Analysis

Intelligent code review tools use AI to analyze code for quality, readability, and adherence to best practices, detecting issues like bugs, security vulnerabilities, and inefficiencies. Tools like CodeRabbit employ large language models (LLMs), such as GPT-4, to understand and analyze code changes in pull requests (PRs). Internally, CodeRabbit’s AI architecture is designed for context-aware analysis, integrating with static analysis tools like Semgrep for security checks and ESLint for style enforcement. The tool learns from team practices over time, adapting its recommendations to align with specific coding standards and preferences.

Challenges: A significant challenge is the potential for AI to misinterpret non-trivial business logic due to its lack of domain-specific knowledge. For instance, while CodeRabbit can detect syntax errors or common vulnerabilities, it may struggle with complex business rules or edge cases that require human understanding. Additionally, integrating such tools into existing workflows may require initial setup and adjustment, though CodeRabbit claims instant setup with no complex configuration.

Impact: By automating code reviews, tools like CodeRabbit reduce manual review time by up to 50%, allowing developers to focus on higher-level tasks. This not only saves time but also reduces cognitive load, as developers no longer need to manually scan through large PRs. A GitLab survey highlighted that manual code reviews are a top cause of developer burnout due to delays and inconsistent feedback. AI tools mitigate this by providing consistent, actionable feedback, improving productivity and reducing mental strain.

Case Study: At KeyValue Software Systems, implementing CodeRabbit reduced code review time by 90% for their Golang and Python projects, allowing developers to focus on feature development rather than repetitive review tasks.

2. Automated Unit Test Generation

Unit testing ensures that individual code components function correctly, but writing these tests manually can be time-consuming. AI tools automate this process by generating comprehensive test suites. Diffblue Cover, for example, uses reinforcement learning to create unit tests for Java code. Internally, Diffblue’s reinforcement learning agents interact with the code, learning to write tests that maximize coverage and reflect every behavior of methods. These agents are trained to understand method functionality and generate tests autonomously, even for complex scenarios.

Challenges: Handling large, complex codebases with numerous dependencies remains a challenge. Additionally, ensuring that generated tests are meaningful and not just covering trivial cases requires sophisticated algorithms. For instance, Diffblue Cover must balance test coverage with test relevance to avoid generating unnecessary or redundant tests.

Impact: Automated test generation saves developers significant time – Diffblue Cover claims to generate tests 250x faster than manual methods, increasing code coverage by 20%. This allows developers to focus on writing new code or fixing bugs rather than repetitive testing tasks. By reducing the need for manual test writing, these tools lower cognitive load, as developers can rely on AI to handle the tedious aspects of testing. A Diffblue case study showed a 90% reduction in test writing time, enabling teams to focus on higher-value tasks.

Case Study: A financial services firm using Diffblue Cover reported a 30% increase in test coverage and a 50% reduction in regression bugs within six months, significantly reducing the mental burden on developers during code changes.

3. Behavioral Testing and Automated UI Testing

Behavioral testing ensures software behaves as expected, while UI testing verifies functionality and appearance across devices and browsers. AI automates these processes, enhancing scalability and efficiency. Applitools, for instance, uses Visual AI to detect visual regressions by comparing screenshots of the UI with predefined baselines. Internally, Applitools captures screenshots and uses AI to analyze visual differences, identifying issues like layout shifts or color inconsistencies. It can handle dynamic content and supports cross-browser and cross-device testing.

Challenges: One challenge is handling dynamic UI elements that change based on user interactions or data. Ensuring that the AI correctly identifies meaningful visual differences while ignoring irrelevant ones, such as anti-aliasing or minor layout shifts, is crucial. Additionally, maintaining accurate baselines as the UI evolves can be resource-intensive.

Impact: Automated UI testing reduces manual testing effort by up to 50%, allowing QA teams to test more scenarios in less time. This leads to faster release cycles and reduces cognitive load on developers, as they can rely on automated tests to catch visual regressions.

Case Study: An e-commerce platform using Applitools reported a noticeable reduction in UI-related bugs post-release, as developers could confidently make UI changes without fear of introducing visual regressions.

4. Continuous Integration and Continuous Deployment (CI/CD) Automation

CI/CD pipelines automate the build, test, and deployment processes. AI enhances these pipelines by predicting failures and optimizing workflows. Harness, for example, uses AI to predict deployment failures based on historical data. Internally, Harness collects logs, metrics, and outcomes from previous deployments to train machine learning models that analyze patterns and predict potential issues. These models can identify risky deployments before they reach production.

Challenges: Ensuring access to high-quality labeled data is essential, as deployments can be complex with multiple failure modes. Additionally, models must be updated regularly to account for changes in the codebase and environments. False positives or missed critical issues can undermine trust in the system.

Impact: By predicting deployment failures, Harness reduces deployment failures by up to 70%, saving time and resources. This reduces cognitive load on DevOps teams, as they no longer need to constantly monitor deployments and react to failures. Automated CI/CD pipelines also enable faster feedback loops, allowing developers to iterate more rapidly.

Case Study: A tech startup using Harness reported a 50% reduction in deployment-related incidents and a 30% increase in deployment frequency, as AI-driven predictions prevented problematic releases.

5. Intelligent Bug Tracking and Prioritization

Bug tracking is critical, but manual prioritization can be inefficient. AI automates detection and prioritization, enhancing resolution speed. Bugasura, for instance, uses AI to classify and prioritize bugs based on severity and impact. Internally, Bugasura likely employs machine learning models trained on historical bug data to classify new bugs and assign priorities. It may also use natural language processing to extract relevant information from bug reports.

Challenges: Accurately classifying bugs, especially in complex systems with multiple causes or symptoms, is a significant challenge. Avoiding false positives and ensuring critical issues are not overlooked is crucial. Additionally, integrating with existing project management tools can introduce compatibility issues.

Impact: Intelligent bug tracking reduces the time spent on manual triage by up to 40%, allowing developers to focus on fixing the most critical issues first. This leads to faster resolution times and improved software quality. By automating prioritization, these tools reduce cognitive load, as developers no longer need to manually sort through bug reports.

Case Study: A SaaS company using Bugasura reduced their bug resolution time by 30% and improved customer satisfaction scores by 15%, as critical bugs were addressed more quickly.

6. Dependency Management and Vulnerability Detection

Managing dependencies and detecting vulnerabilities early is crucial for security. AI tools scan for risks and outdated dependencies without deploying agents. Wiz, for example, uses AI to analyze cloud environments for vulnerabilities. Internally, Wiz collects data from various cloud services (e.g., AWS, Azure, GCP) and uses machine learning models to identify misconfigurations, outdated software, and other security weaknesses. It analyzes relationships between components to uncover potential attack paths.

Challenges: Keeping up with the rapidly evolving cloud environments and constant updates to cloud services is a major challenge. Minimizing false positives while ensuring all critical vulnerabilities are detected is also important. Additionally, ensuring compliance with security standards across diverse environments can be complex.

Impact: Automated vulnerability detection reduces manual scanning efforts, allowing security teams to focus on remediation. By providing prioritized lists of vulnerabilities, these tools help manage workload effectively, reducing cognitive load. Wiz claims to reduce vulnerability identification time by 30%, enhancing overall security posture.

Case Study: A fintech firm using Wiz identified and patched 50% more critical vulnerabilities in their cloud environment compared to traditional methods, reducing their risk exposure significantly.

Conclusion

Left-shifting quality, enhanced by AI, is a critical strategy for modern software development, reducing costs, improving quality, and accelerating delivery. AI-powered tools automate and optimize QA processes, from code review to vulnerability detection, enabling teams to catch issues early and deliver brilliance. As AI continues to evolve, with trends like generative AI for test generation and predictive analytics, the future promises even greater efficiency. Organizations adopting these techniques can transform their development processes, achieving both speed and excellence.

What is LLMOps, MLOps for large language models, and their purpose

Posted on by Editorial Team

Why manage transfer learning of large language models and what is included in this management: getting acquainted with the MLOps extension for LLM called LLMOps.

(um) let’s make tech work for us…

How did LLMOps come to be? 

Large language models, embodied in generative neural networks (ChatGPT and other analogues), have become the main technology of the outgoing year, which is already actively used in practice by both individuals and large companies. However, the process of training LLM (Large Language Model) and their implementation in industrial use must be managed in the same way as any other ML system. A good practice for this has become the MLOps concept, aimed at eliminating organizational and technological gaps between all participants in the development, deployment and operation of machine learning systems.

As the popularity of GPT networks and their implementation in various application solutions grows, there is a need to adapt the principles and technologies of MLOps to transfer learning used in generative models. This is because language models are becoming increasingly large and complex to maintain and manage manually, which increases costs and reduces productivity. To avoid this, LLMOps, a type of MLOps that oversees the LLM lifecycle from training to maintenance using innovative tools and methodologies, can help.

LLMOps focuses on the operational capabilities and infrastructure required to fine-tune existing base models and deploy these improved models as part of a product. Because base language models are huge, such as GPT-3, which has 175 billion parameters, they require a huge amount of data to train, as well as time to map the computations. For example, it would take over 350 years to train GPT-3 on a single NVIDIA Tesla V100 GPU. Therefore, an infrastructure that can run GPU machines in parallel and process huge data sets is essential. LLM inference is also much more resource-intensive than more traditional machine learning, as it is not a single model, but a chain of models.

LLMOps provides developers with the necessary tools and best practices for managing the LLM development lifecycle. While the ideas behind LLMOps are largely the same as MLOps, large base language models require new methods, guidelines, and tools. For example, Apache Spark in Databricks works great for traditional machine learning, but it is not suitable for fine-tuning LLMs.

LLMOps focuses specifically on fine-tuning base models, since modern LLMs are rarely trained entirely from scratch. Modern LLMs are typically consumed as a service, where a provider such as OpenAI, Google AI, etc. offers an API of the LLM hosted on their infrastructure as a service. However, there is also a custom LLM stack, a broad category of tools for fine-tuning and deploying custom solutions built on top of open-source GPT models. The fine-tuning process starts with an already trained base model, which then needs to be trained on a more specific and smaller dataset to create a custom model. Once this custom model is deployed, queries are sent and the corresponding completion information is returned. Monitoring and retraining a model is essential to ensure its consistent performance, especially for LLM-driven AI systems.

Rapid engineering tools allow contextual training to be performed faster and cheaper than fine-tuning, without requiring sensitive data. In this case, vector databases extract contextually relevant information for specific queries, and prompt queries can optimize and improve model output based on patterns and chaining.

Similarities and differences with MLOps

In summary, LLMOps facilitates the practical application of LLM by incorporating operational management, LLM chaining, monitoring, and observation techniques that are not typically found in conventional MLOps. In particular, prompts are the primary means by which humans interact with LLMs. However, formulating a precise query is not a one-time process, but is typically performed iteratively, over several attempts, to achieve a satisfactory result. LLMOps tools offer features to track and version prompts and their results. This facilitates the evaluation of the overall performance of the model, including operational work with multiple LLMs.

LLM chaining links multiple LLM invocations in a sequential manner to provide a single application function. In this workflow, the output of one LLM invocation serves as the input to another to produce the final result. This design approach represents an innovative approach to developing AI applications by breaking down complex tasks into smaller steps. Chaining removes the inherent limitation on the maximum number of tokens that LLM can process simultaneously. LLMOps simplifies chaining management and combines it with other document retrieval methods, such as vector database access.

LLMOps’s LLM monitoring system collects real-time data points after a model is deployed to detect degradation in its performance. Continuous, real-time monitoring allows you to quickly identify, troubleshoot, and resolve performance issues before they affect end users. Specifically, prompts, tokens and their length, processing time, inference latency, and user metadata are monitored. This allows you to notice overfitting or changing the underlying model before performance actually degrades.

Monitoring models for drift and bias is also critical. While drift is a common problem in traditional machine learning models, as we’ve written about here, monitoring LLM solutions with LLMOps is even more important due to their reliance on underlying models. Bias can arise from the original datasets on which the base model was trained, custom datasets used for fine-tuning, or even from human evaluators judging fast completion. A thorough evaluation and monitoring system is needed to effectively remove bias.

LLM is difficult to evaluate using traditional machine learning metrics because there is often no single “right” answer, whereas traditional MLOps relies on human feedback, incorporating it into testing, monitoring, and collecting data for use in future fine-tuning.

Finally, there are differences in the way LLMOps and MLOps approach application design and development. LLMOps is designed to be fast, whereas traditional MLOps projects are typically iterative, starting with existing proprietary or open-source models and ending with custom fine-tuned or fully trained models on curated data.

Despite these differences, LLMOps is still a subset of MLOps. That’s why the authors of The Big Book of MLOps from Databricks have included the term in the second edition of this collection, which provides guiding principles, design considerations, and reference architectures for MLOps.

UnlockED Hackathon

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Shaping the Future of Education with Technology – February 25-26, 2024

ExpertStack proudly hosted the UnlockED Hackathon, a high-energy innovation marathon focused on transforming education through technology. With over 550 participants from the Netherlands and a distinguished panel of 15 judges from BigTech, the event brought together some of the brightest minds to tackle pressing challenges in EdTech.

The Challenge: Reimagining Education through Tech
Participants were challenged to develop groundbreaking solutions that leverage technology to make education more accessible, engaging, and effective. The hackathon explored critical areas such as:

  • AI-powered personalized learning – Enhancing student experiences with adaptive, data-driven education.
  • Gamification & immersive tech – Using AR/VR and interactive platforms to improve engagement.
  • Bridging the digital divide – Creating tools that ensure equal learning opportunities for all.
  • EdTech for skill-building – Solutions focused on upskilling and reskilling for the digital economy.

For 48 hours, teams brainstormed, designed, and built innovative prototypes, pushing the boundaries of what’s possible in education technology.

And the Winner is… Team OXY!
After an intense round of final presentations, Team OXY took home the top prize with their AI-driven adaptive learning platform that personalizes study plans based on real-time student performance. Their solution impressed judges with its scalability, real-world impact, and seamless integration with existing education systems.

Driving Change in EdTech
The UnlockED Hackathon was more than just a competition—it was a movement toward revolutionizing education through technology. By fostering collaboration between developers, educators, and industry leaders, ExpertStack is committed to shaping a future where learning is smarter, more inclusive, and driven by innovation.

Want to be part of our next hackathon? Stay connected and join us in shaping the future of tech! 🚀

Breathing New Life into Legacy Businesses with AI

Posted on by Editorial Team

Author: Jenn Cunningham, a Go-to-Market Lead, Strategic Alliances at PolyAI. Currently, she leads strategic alliances at PolyAI, where she manages key relationships with AWS and global consulting partners while collaborating closely with the PolyAI cofounders on product expansion and new market entry. Her unique journey from data science beginnings to implementation consulting gives her a front-row seat to how legacy businesses are leveraging AI to evolve and thrive.

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Introduction: 

I was hired and trained as a data scientist for IBM fresh out of university. Data science and data analytics were the hot topic at the time, as businesses were ready to become more data-driven organizations. I was so excited to unlock new customer insights and inform business strategy, until my first project. After 8 weeks of cleansing data and crunching numbers for 12 hours a day, it was glaringly obvious that I was entirely too extroverted for a pure data science role. I quickly moved into implementation consulting, where I was fortunate to see firsthand how businesses evaluate, implement, and adopt different types of process automation technology as the technology itself continued to evolve.That evolution led me to realize the other data and AI’s capabilities, primarily focusing on what they could do to the operations of businesses labeled legacy – not only for efficiency, but also for improving user service. These companies tend to be branded or perceived as slower to adapt, but they’re full of indisputable value waiting for the right ‘nudge.’

AI is providing that nudge because today, AI does more than automating boring work; it is changing how businesses perceive value. A century-old bank, a global manufacturer, and a regional insurer, these are just a few examples of businesses that are evolving their core AI technologies, improving their internal systems while retaining their rich history. 

This didn’t suddenly happen though, but there were many steps involved, each more groundbreaking than the last. So to truly narrate the current state AI had to evolve in, we need to wind the clocks back to a time when data wasn’t an inevitability but a luxury.

The First Wave: Data as a Project

Back in the infancy of data science within companies, their treatment of data resembled a whiteboard and marker experiment. Businesses seemed lost on what to do with data, and therefore assumed it required a project-like treatment with a start and end, or a PowerPoint presentation in mid — something that showcases interim findings. Along the way, gathering “let’s get a data scientist to look at this” comments became a norm embracing a carefree approach of one-single-to-multi-business domain change. 

While working for IBM, I was fortunate to witness this phenomenon in real time. Shifts were just beginning where organizations were changing from relying on gut feelings to data informed strategies but everything still felt labored. One of my clients from IBM still sits fresh in my head because they took the tin can approach a little too literally, printing out .txt files like word documents containing customer interactions and using scissors to post these documents in a conference room where they would visually calculate key metrics, calculators and highlighters in hand. Data science in its untapped, unrefined glory was radical.

The purpose wasn’t to create sustainable systems. Instead, it was to respond to prompts such as “What’s our churn rate?” or “Was this campaign successful?” These questions, while important in their own right, were evasive at best. Each project felt like a fleeting victory without much future potential. There was no reusable framework, no collaboration across teams, and definitely no foresight for what data could evolve into.

However, this preliminary wave had significance as it allowed companies to recognize the boundaries of instinct-driven decision-making and the usefulness of evidence. Although the work was done in stages, it rarely resulted in foundational changes, and even when insights did materialize, they were not capable of driving widespread change.

The Second Wave: Building a Foundation for Ongoing Innovation

Gradually, a new understanding seemed to surface,  one that moved data from being a tactical resource to a strategic asset. In this second wave, companies sought answers to more advanced inquiries. How do we use data to enable proactive decision-making rather than only responsive actions? How can we incorporate insights into the operational fabric of our company? 

This phase experienced shifts from companies such as Publicis Groupe. Rather than bringing on freelance data scientists on a contractual basis, they transformed their approaches by building internal ecosystems of expertise composed of multidisciplinary teams and fostering a spirit of innovation. Thus, the focus shifted from immediate problem-solving to laying the foundational systems for comprehensive future infrastructure. 

Moreover, data started to shift from the back-office functions to the forefront. Marketing, sales, product, and customer service functions received access to real-time dashboards, AI tools, predictive analytics, and a host of other utilities. Therefore the democratization of data accelerated to bring the power of AI data insights to the decision makers who worked directly with customers and crafted user experiences.

What also became clear during this phase was that not all parts of the organization required the same level of AI maturity at the same time. Some teams were set for complete automation; others just required clean reporting which was perfectly fine. The goal was not standard adoption; rather, it was movement. The most advanced thinking companies understood that the pace of change didn’t have to happen everywhere all at once, it just needed a starting point and careful cultivation.

This was the turning point when data began evolving from a department to a capability; it could now single-handedly drive continuous enhancements instead of relying on project-based wins. That is when the flywheel of innovation had commenced spinning.

The Current Wave: Reimagining Processes with AI

Today, we are experiencing a third and possibly the most impactful wave of change. AI is no longer limited to enhancing analytics and operational efficiency; it now rethinks the very framework of how businesses are structured and run. What was previously regarded as an expenditure is now considered a divisive competitive advantage.  

Consider what PolyAI and Simplyhealth have done. Simplyhealth, a UK health insurer, partnered with PolyAI to implement voice AI within their customer service channels. However, this integration went beyond implementing basic chatbots. The AI was ‘empathetic AI’ since it could understand urgency, recognize vulnerable callers, and make judgment calls on whether patients should be passed to a human auxiliary.  

Everyone saw the difference. There was less waiting around, better call resolution, and most crucially, those that required care from a member of staff received it. Nonetheless, AI did not take the person out of the process; it elevated the person into the process, allowing them to experience empathy and enable humanity to work alongside effectiveness.

Such a focus on building technology around humans is rapidly becoming a signature of AI change in today’s world. You see it with retail AI, which customizes every touchpoint in the customer experience. It’s happening in manufacturing with costs associated with breakdowns being avoided through predictive maintenance. And in financial services, it’s experiencing massive shifts as AI technologies offer personalized financial consulting, fraud detection, and assistance to those missing traditional support.  

In all these examples, AI technologies support rather than replace people. Customer service representatives are equipped with richer context, which augments their responses, freelancers are liberated from doing repetitive work, and strategists get help concentrating on the right resources. Therefore, today’s best AI use cases focus on augmenting human experience instead of reducing the workforce.

Conclusion: 

Too often is the phrase “legacy business” misused to describe something as old-fashioned or boring. But in fact, these are businesses with long-standing customer relationships and histories, enabling them to evolve in meaningful ways.  

Modern AI solutions don’t simply replace manual labor as the advancement from spreadsheets and instinct-based decisions to fully integrated AI systems is more complex. Businesses progressively adopt modern practices all while having a vision and patience in terms of cultural branding. Plus, legacy businesses are contemporarily evolving and keeping up with the pace, and many are leading the race.  

AI today is changing everything and has now become a culture driving system. It impacts the very way we collaborate, deliver services, value customers, and so much more. Whether implementing new business strategies, redefining customer support, or optimizing computer science logistics, AI is proving to be a propellant for transformation focused on humans.  

Further, visionaries and team members who witnessed this automated evolution firsthand felt unity through action, fervently participating as data table-aligned pilots meshed with algorithms and numbers. Reminding us that change isn’t all technical; it’s human. It’s intricate, fulfilling, and simply put: essential.

To sum up, the future businesses are not the newest; rather, they are the oldest that choose to develop with a strong sense of intention behind it. In that development, legacy is not a hindrance, but rather, a powerful resource.