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Security and privacy issues in cloud computingSecurity and privacy issues in cloud computing

Cloud computing is the main support for many companies worldwide. More businesses are moving to cloud computing to improve how they work and compete.

It’s important to find the top security problems in cloud computing. Data leaks caused by cloud setup mistakes and past data leaks need to be watched. This is to avoid impact on the company. 

What is cloud computing? 

Cloud computing changes how we manage, access, and store data. This is done through internet services. This is different from the old way of using storage devices.  

 

The new cloud-computing model means you do not have to worry about managing servers. Both companies and people can benefit. They get strong data security and flexible, low-cost, and easy-to-adapt data solutions in the cloud. 

Why do you need cloud computing? 

Companies can use secure data centers, lower infrastructure costs, and do operation optimization at full length. It increases efficiency, lowers costs, and empowers businesses. 

 

With cloud computing, an organization can quickly adjust resources to match demand without requiring large initial hardware investments. An organization needs to pay for only the resources it consumes, lowering expenses for infrastructure and upkeep. You can access data and applications remotely with an internet connection, which increases accessibility to work and collaboration. You can, however, enable fast deployment of new applications and services, eliminating the lengthy lead times of traditional IT methods. In cloud computing, service providers take care of maintenance and updates, guaranteeing that you constantly receive the most up-to-date features and security. Numerous cloud services provide strong backup and recovery options, reducing downtime in the event of data loss. It streamlines IT resource management, enabling teams to concentrate on strategic projects instead of daily upkeep. 

Cloud security issues

There are multiple security issues in cloud computing, and there are hurdles to overcome to secure data and still be able to maintain operational reliability. In this article, we explore the main security concerns in cloud computing and the extent to which they could possibly harm businesses. 

Data loss

Data leakage has been a serious issue in cloud computing. Well, that is true, but only if our sensitive data is being taken care of by someone else whom we cannot trust absolutely, and just the opposite.

Therefore, if cloud service security is going to be baked by a hacker, then hackers can surely get a hold of our sensitive data or personal files. 

Insecure APIs

APIs are the easiest way to talk about the Cloud and need protection. Moreover, as third-party access public clouds, they too can be vulnerabilities to a cloud.

To secure these APIs, implementing SSL certificates is crucial, as they encrypt data in transit, making it harder for hackers to intercept sensitive information. Without this layer of security, attackers can exploit weaknesses in the API, leading to unauthorized access or data loss.  

Account hijacking

The most serious and pressing security threat out of myriads of cloud computing is account hijacking. Once a hacker compromises or hijacks the Account of a User or an Organization, he can access all unauthorized accounts and other activities. 

Change of service provider

Change of service provider is also an important Security issue in Cloud Computing. Many organizations will face different problems like data shifting and different charges for each vendor while shifting from one vendor to another. 

Skill gap 

The biggest problem with IT Companies that do not have skilled Employees is the need to shift to another service provider while working, another feature required, how to use a feature, and so on. Therefore, it requires an extremely skilled person to work in cloud computing. 

Insider threat

On the face of it, this would come out unlikely, but in reality, cloud security threats are those insiders that pose a serious threat to the organizations that avail cloud-based services.

These persons with authorized access to the most needed company resources may indulge in some forms of misconduct, either intentional or unintentional, which will lead to the misuse of their sensitive data. Sensitive data will include client accounts and all critical financial information. 

The important fact to be considered is that the threats from within in cloud security are likely to come through either malicious intent or unintended and just plain negligence. Most such threats can mature into serious violations of security if they develop further and can thereby put sensitive data at risk.



To fight effectively such insider threats while maintaining, at the same time, the confidentiality of data being protected and stored in the cloud, access control must be proper, along with tight and strict access controls.  

Moreover, full training courses including minute details about security should be provided to every member of the staff. In this regard also, monitoring should be done periodically. It is these aspects that have been the main reasons for protection against internal threats that may go about happening. 

Malware injection

The most potent cloud security threats are malware injections. Evil code is concealed in the guise of legitimate code in cloud services. The attacks compromise data integrity because malignant options allow attackers to eavesdrop, modify information, and escape data without detection.

It has become essential to secure the data from eavesdropping in cloud computing and security is an essential aspect. This has become a serious threat to the security of the cloud environment; it should be counter-attacked through careful vigilance and robust security to avoid access to the cloud infrastructure.

Misconfiguration

Indeed, misconfigurations in cloud security settings have proved to be one of the leading and most common causes of data breaches in the present-day digital, and these incidents are mostly the offspring of less-than-perfect practices about managing an effective posture of security.

The user-friendly nature of cloud infrastructure, set up primarily to allow easy exchange and interaction of data, poses significant hurdles to directing access of the data to only a targeted entity or personnel. 

Data storage issue 

This distributed cloud infrastructure is spread all over the globe. Sometimes it tends to keep user data outside the jurisdictions of the legal frameworks of certain regions, raising the range of such data among local law enforcement and regulations. The user dreads its violation because the notion of a cloud makes it difficult to identify one server in the process of transferring data overseas. 

Shared infrastructure security concerns

Multi-tenancy is the sharing of resources, storage, applications, and services from one platform with many at the cloud provider’s site. This tends to enable the provider to recoup high returns on investment but puts the customer at risk. Hence, an attacker can use multi-homing options to make a successful attack against the remaining co-tenants. This has a privacy problem. 

Conclusion 

The business world is changing rapidly, and the rise of cloud computing has created huge security and privacy concerns. In the cloud, there are many issues, such as multiple users sharing the same infrastructure and relying on third parties. These make data vulnerable.

Organizations must be proactive to protect data. They need strong encryption, controlled access, regular security audits, and a clear understanding of their shared responsibility with cloud providers. 

Top 5 areas in the data pipeline with the least responsivenessTop 5 areas in the data pipeline with the least responsiveness

Data pipelines are critical for organizations handling vast amounts of data, yet many practitioners report challenges with responsiveness, especially in data analysis and storage.

Our latest generative AI report revealed that various elements within the pipeline significantly affect performance and usability. We wanted to investigate what could be affecting the responsiveness of the practitioners who reported issues. 

The main area of data workflow or pipeline where practitioners find the least responsiveness is data analysis (28.6%), followed by data storage (14.3%) and other reasons (14.3%), such as API calls, which generally take a significant amount of time.

What factors have an impact on that portion of the data pipeline?

We also asked practitioners about the factors impacting that portion of the pipeline. The majority (58.3%) cited the efficiency of the pipeline tool as the key factor. This could point to a pressing need for improvements in the performance and speed of these tools, which are essential for maintaining productivity and ensuring fast processing times in environments where quick decision-making is key.

With 25% of practitioners pointing to storage as a significant bottleneck after the efficiency of the pipeline tool, inadequate or inefficient storage solutions can impact the ability to process and manage large volumes of data effectively. 

16.7% of practitioners highlighted that code quality disrupts the smooth operation of AI pipelines. This can lead to errors, increased downtime, and complicated maintenance and updates. 

Code quality

The quality of the code in the data pipeline is key to its overall performance and reliability. High-quality code often leads to fewer errors and disruptions, translating to smoother data flows and more reliable outputs. 

Examples of how high code quality can enhance responsiveness:

1. Error handling and recovery2. Optimized algorithms 3. Scalability4. Maintainability and extensibility5. Parallel processing and multithreading6. Effective resource management 7. Testing and quality assurance

Efficiency of pipeline tool

Efficient tools can quickly handle large volumes of data, helping to support complex data operations without performance issues. This is an essential factor when dealing with big data or real-time processing needs, where delays can lead to outdated or irrelevant insights. 

Examples of how the efficiency of pipeline tools can enhance responsiveness:

Data processing speed Resource utilizationMinimized latencyCaching and state managementLoad balancingAutomation and orchestrationAdaptability to data volume and variety

Storage

Storage solutions in a data pipeline impact the cost-effectiveness and performance of data handling. Effective storage solutions must offer enough space to store data while being accessible and secure. 

Examples of how storage can enhance responsiveness:

Data retrieval speedData redundancy and backupScalabilityData integrity and securityCost efficiencyAutomation and management toolsIntegration capabilities

What use cases are driving your data pipeline?

What use cases are driving your data pipeline?

We also asked respondents to identify the specific scenarios or business needs that drive their data pipelines’ design, implementation, and operation to understand the primary purposes for which the data pipeline is being utilized within their organizations.

Natural language processing, or NLP, was highlighted as the main use case (42.8%), with an even distribution across the other use cases. This could be due to businesses increasing their operations in digital spaces, which generate vast amounts of textual data from sources like emails, social media, customer service chats, and more.

NLP

NLP applications require processing and analyzing text data to complete tasks like sentiment analysis, language translation, and chatbot interactions. Effective data pipelines for NLP need to manage diverse data sources like social media posts, customer feedback, and technical documents.

Examples of how NLP drives data pipelines:

Extracting key information from text dataCategorizing and tagging content automaticallyAnalyzing sentiment in customer feedbackEnhancing search and discovery through semantic analysisAutomating data entry from unstructured sourcesGenerating summaries from large text datasetsEnabling advanced question-answering systems

Image recognition

Image recognition analyzes visual data to identify objects, faces, scenes, and activities. Data pipelines for image recognition have to handle large volumes of image data efficiently, which requires significant storage and powerful processing capabilities. 

Examples of how image recognition drives data pipelines:

Automating quality control in manufacturingCategorizing and tagging digital images for easier retrievalEnhancing security systems with facial recognitionEnabling autonomous vehicle navigationAnalyzing medical images for diagnostic purposesMonitoring retail spaces for inventory controlProcessing satellite imagery for environmental monitoring

Image/visual generation

Data pipelines are designed to support the generation process when generative models are used to create new images or visual content, such as in graphic design or virtual reality. 

Examples of how image/visual generation drives data pipelines:

Creating virtual models for fashion designGenerating realistic game environments and charactersSimulating architectural visualizations for construction planningProducing visual content for marketing and advertisingDeveloping educational tools with custom illustrationsEnhancing film and video production with CGI effectsCreating personalized avatars for social media platforms

Recommender systems

Recommender systems are useful in a wide variety of applications, from e-commerce to content streaming services, where personalized suggestions improve user experience and engagement. 

Examples of how recommender systems drive data pipelines:

Personalizing content recommendations on streaming platformsSuggesting products to users on e-commerce sitesTailoring news feeds on social mediaRecommending music based on listening habitsSuggesting connections on professional networksCustomizing advertising to user preferencesProposing travel destinations and activities based on past behavior

The rise of the Chief AI Officer: Is your organization ready?The rise of the Chief AI Officer: Is your organization ready?

Imagine this: It’s 2025. The CEO of a mid-sized tech company, overwhelmed by the rapid changes in AI, realizes the company is missing out. Despite having the latest tools and software, there’s still a gap—a missing strategic vision to make it all work seamlessly.

That’s when they decide to hire a Chief AI Officer. Within a year, the company transforms. Customer satisfaction is up, operations are smoother, and new revenue streams have opened. The CAIO didn’t just bring AI; they brought a revolution.

Artificial intelligence has evolved from an experimental technology to a core business necessity, reshaping operations, decision-making, and customer experiences. As its influence grows, so does the need for specialized leadership.

Enter the Chief AI Officer (CAIO), a role dedicated to embedding AI into the organization’s DNA. But what exactly does this role bring to the table that other tech executives might not?

Why a Chief AI Officer?

In many companies, AI initiatives have traditionally been managed by IT departments or overseen by roles like the Chief Data Officer (CDO) or Chief Technology Officer (CTO).

However, as AI’s impact broadens, the demand for dedicated AI leadership becomes clearer. A CAIO does more than oversee implementation; they shape how AI integrates with the organization’s core functions and long-term objectives.

Several critical factors underscore the rise of this role:

Specialized expertise in emerging AI applications: Implementing AI at a strategic level requires not only technical knowledge but also industry-specific insights. CAIOs need to stay ahead of AI’s evolving applications, including in non-traditional sectors like education, nonprofits, and disaster response. A CAIO with insights into these fields can tailor innovations to meet unique industry challenges, creating a distinct competitive advantage.Ethical and regulatory leadership: AI’s rapid adoption introduces pressing ethical and regulatory issues, from privacy concerns to managing bias. CAIOs play a crucial role in ensuring that AI systems adhere to ethical principles, such as those outlined in the UNESCO Recommendation on the Ethics of Artificial Intelligence. By establishing clear guidelines and monitoring AI’s impact, CAIOs can help mitigate potential harms, promote transparency, and foster public trust—elements critical for organizations that seek to lead responsibly in AI.Driving business transformation: The CAIO’s role goes beyond introducing AI tools; it’s about transforming business processes, opening new revenue streams, and improving customer experience. For instance, the grant proposal tool I implemented reduced preparation time by over 30 hours per proposal, illustrating the kind of measurable impact that a CAIO can bring. Positioned at the executive level, the CAIO drives AI initiatives that create significant, lasting change.Workforce development and transformation: The demand for AI talent is high, and a CAIO is essential in attracting, developing, and retaining team members who can deliver on AI strategies. They foster an AI-savvy culture that integrates technical and business knowledge across the workforce. By prioritizing internal training and upskilling, CAIOs can help employees embrace AI as a valuable tool, not a threat.Cross-departmental integration: AI’s reach extends to every corner of a business, impacting marketing, customer service, HR, and beyond. A CAIO ensures that AI adoption is cohesive and strategic, breaking down departmental silos to drive alignment with the company’s goals. For example, implementing an AI recommendation engine across product development and customer service can streamline and enhance the entire customer journey, delivering value at every touchpoint.


Key responsibilities of a Chief AI Officer

A CAIO’s responsibilities are diverse and strategic, encompassing the oversight of AI initiatives, risk management, and performance measurement. Key duties include:

Strategic planning: Develop a clear AI vision, prioritize high-impact projects, and collaborate with other executives to ensure AI initiatives align with organizational goals. Strategic planning with a CAIO is about more than timelines; it’s about identifying projects that will have meaningful, transformative impact.Implementation oversight: Oversee the end-to-end development and deployment of AI initiatives, ensuring each project—from model design to deployment—meets strategic objectives. CAIOs prioritize high-ROI projects and track their success to showcase AI’s tangible value within the organization.Governance and ethics: Establish ethical governance frameworks to manage biases, protect data privacy, and adhere to regulations, embedding responsible AI practices within the organization’s culture. In my work developing governance frameworks, I’ve built models to track and mitigate bias, highlighting that ethical AI governance is an ongoing process, not a one-time setup.Change management and education: Drive AI adoption across the organization by addressing concerns, promoting understanding, and providing upskilling opportunities. Educating employees about AI’s benefits is critical for fostering acceptance and creating a culture where AI is seen as empowering, not disruptive.Performance measurement and iteration: Set and monitor metrics—such as efficiency gains, revenue impact, and customer satisfaction improvements—to assess AI’s success. CAIOs continuously refine AI strategies to adapt to technological advancements, making performance measurement a cornerstone of AI leadership.

Is a CAIO right for your organization?

Not every organization may need a dedicated CAIO. For smaller businesses or those with limited AI applications, roles like the CTO or CDO might sufficiently cover AI needs.

However, companies with ambitious AI goals—especially in complex or regulated sectors like finance, healthcare, or retail—can gain substantial value from having a CAIO to focus on AI’s strategic alignment, ethical oversight, and cohesive deployment.

For organizations that aren’t yet ready to bring on a CAIO, developing CAIO-like responsibilities within existing roles can serve as a bridge. This approach prepares the organization to navigate AI’s growing influence, positioning it to embrace a future where the CAIO role might become essential.

The CAIO doesn’t just drive AI strategy; they align AI initiatives with the broader business vision, ensuring that implementations are impactful, ethical, and compliant. In an era where AI is integral to business success, a CAIO’s focused leadership could be the competitive edge that organizations need to stay ahead.

Conclusion

The emergence of the Chief AI Officer marks a pivotal shift in business, where AI becomes a strategic driver of innovation and a core element of corporate vision.

For organizations committed to responsible, comprehensive AI adoption, a CAIO can be the catalyst that unites people, processes, and technology, future-proofing the organization in an AI-powered world.

Transforming customer experiences, developing an AI-capable workforce, and establishing ethical standards, a Chief AI Officer (CAIO) plays a crucial role in driving the change needed to navigate today’s ever-evolving AI landscape.

Want more from Dr. Denise Turley?

Check out her other articles below:

Dr. Denise Turley – AI Accelerator Institute
Dr. Denise Turley integrates AI in academia and industry. As a speaker, she promotes diversity and inclusion, supporting women in tech through mentorship and policies for equitable opportunities.

Impact and innovation of AI in energy use with James ChalmersImpact and innovation of AI in energy use with James Chalmers

In the very first episode of our monhtly Explainable AI podcas, hosts Paul Anthony Claxton and Rohan Hall sat down with James Chalmers, Chief Revenue Officer of Novo Power, to discuss one of the most pressing issues in AI today: energy consumption and its environmental impact.

Together, they explored how AI’s rapid expansion is placing significant demands on global power infrastructures and what leaders in the tech industry are doing to address this.

The conversation covered various important topics, from the unique power demands of generative AI models to potential solutions like neuromorphic computing and waste heat recapture. If you’re interested in how AI shapes business and global energy policies, this episode is a must-listen.

Why this conversation matters for the future of AI

The rise of AI, especially generative models, isn’t just advancing technology; it’s consuming power at an unprecedented rate. Understanding these impacts is crucial for AI enthusiasts who want to see AI development continue sustainably and ethically.

As James explains, AI’s current reliance on massive datasets and intensive computational power has given it the fastest-growing energy footprint of any technology in history. For those working in AI, understanding how to manage these demands can be a significant asset in building future-forward solutions.

Main takeaways

AI’s power consumption problem: Generative AI models, which require vast amounts of energy for training and generation, consume ten times more power than traditional search engines.Waste heat utilization: Nearly all power in data centers is lost as waste heat. Solutions like those at Novo Power are exploring how to recycle this energy.Neuromorphic computing: This emerging technology, inspired by human neural networks, promises more energy-efficient AI processing.Shift to responsible use: AI can help businesses address inefficiencies, but organizations need to integrate AI where it truly supports business goals rather than simply following trends.Educational imperative: For AI to reach its potential without causing environmental strain, a broader understanding of its capabilities, impacts, and sustainable use is essential.

Meet James Chalmers

James Chalmers is a seasoned executive and strategist with extensive international experience guiding ventures through fundraising, product development, commercialization, and growth.

As the Founder and Managing Partner at BaseCamp, he has reshaped traditional engagement models between startups, service providers, and investors, emphasizing a unique approach to creating long-term value through differentiation.

Rather than merely enhancing existing processes, James champions transformative strategies that set companies apart, strongly emphasizing sustainable development.

Numerous accolades validate his work, including recognition from Forbes and Inc. Magazine as a leader of one of the Fastest-Growing and Most Innovative Companies, as well as B Corporation’s Best for The World and MedTech World’s Best Consultancy Services.

He’s also a LinkedIn ‘Top Voice’ on Product Development, Entrepreneurship, and Sustainable Development, reflecting his ability to drive substantial and sustainable growth through innovation and sound business fundamentals.

At BaseCamp, James applies his executive expertise to provide hands-on advisory services in fundraising, product development, commercialization, and executive strategy.

His commitment extends beyond addressing immediate business challenges; he prioritizes building competency and capacity within each startup he advises. Focused on sustainability, his work is dedicated to supporting companies that address one or more of the United Nations’ 17 Sustainable Development Goals through AI, DeepTech, or Platform Technologies.

About the hosts:

Paul Anthony Claxton – Q1 Velocity Venture Capital | LinkedIn
www.paulclaxton.io – am a Managing General Partner at Q1 Velocity Venture Capital… · Experience: Q1 Velocity Venture Capital · Education: Harvard Extension School · Location: Beverly Hills · 500+ connections on LinkedIn. View Paul Anthony Claxton’s profile on LinkedIn, a professional community of 1 billion members.

Rohan Hall – Code Genie AI | LinkedIn
Are you ready to transform your business using the power of AI? With over 30 years of… · Experience: Code Genie AI · Location: Los Angeles Metropolitan Area · 500+ connections on LinkedIn. View Rohan Hall’s profile on LinkedIn, a professional community of 1 billion members.

Balancing innovation and safety in AI with Karanveer AnandBalancing innovation and safety in AI with Karanveer Anand

In the latest episode of The Generative AI Podcast, host Arsenii Shatokhin sat down with Karanveer Anand, a Technical Program Manager at Google, to explore how AI is reshaping the field of program management.

They dove into everything from the role of AI in cloud computing to the evolving balance between AI innovation and safety. If you’re curious about how AI is influencing the future of technical program management, this discussion is a must-listen.

Catch the full episode right here.

Why AI enthusiasts should care

AI is no longer just a buzzword; it’s becoming a critical part of how businesses operate, making it essential for technical program managers to understand its implications.

As Karanveer explains, AI is revolutionizing how program management is done, introducing new efficiencies and ways to optimize workflows. For those working in AI and tech, understanding the intersection of AI and program management can provide a significant competitive edge.

Main takeaways

AI in program management: AI requires deep technical understanding and brings new challenges to program management.AI’s role in cloud computing: AI optimizes cloud resource allocation for better efficiency and cost management.AI as a necessity: Tools like Gemini integrate AI into everyday tasks, making it indispensable.Prioritizing AI safety: Safety should be built into AI frameworks from the start.AI’s future across industries: AI is set to transform sectors like healthcare and finance, offering new opportunities for innovation.

Meet Karanveer Anand

Karanveer Anand is a Technical Program Manager at Google, specializing in software reliability. His role focuses on ensuring that Google’s services remain highly reliable and accessible to billions of users worldwide.

With a background in cloud infrastructure and AI, Karanveer is passionate about using AI to improve software reliability and streamline processes. He is always exploring innovative ways to apply AI and is a thought leader in the intersection of AI, cloud computing, and program management.

End GPU underutilization: Achieve peak efficiencyEnd GPU underutilization: Achieve peak efficiency

AI and deep learning inference demand powerful AI accelerators, but are you truly maximizing yours?

GPUs often operate at a mere 30-40% utilization, squandering valuable silicon, budget, and energy.

In this live session, NeuReality’s Field CTO, Iddo Kadim, tackles the critical challenge of maximizing AI accelerator capability. Whether you build, borrow, or buy AI acceleration – this is a must-attend.

Date: Thursday, December 5
Time: 10 AM PST | 5 PM GMT
Location: Online

Iddo will reveal a multi-faceted approach encompassing intelligent software, optimized APIs, and efficient AI inference instructions to unlock benchmark-shattering performance for ANY AI accelerator.

The result?

You’ll get more from the GPUs buy, rather than buying more GPUs to make up for the limitations of today’s CPU and NIC-reliant inference architectures. And, you’ll likely achieve superior system performance within your current energy and cost constraints. 

Your key takeaways:

The urgency of GPU optimization: Is mediocre utilization hindering your AI initiatives? Discover new approaches to achieve 100% utilization with superior performance per dollar and per watt leading to greater energy efficiency.Factors impacting utilization: Master the key metrics that influence GPU utilization: compute usage, memory usage, and memory bandwidth.Beyond hardware: Harness the power of intelligent software and APIs. Optimize AI data pre-processing, compute graphs, and workload routing to maximize your AI accelerator (XPU, ASIC, FPGA) investments.Smart options to explore: Uncover the root causes of underutilized AI accelerators and explore modern solutions to remedy them. You’ll get a summary of recent LLM real-world performance results – made possible by pairing NeuReality’s NR1 server-on-a-chip with any GPU or AI accelerator.

You spent a fortune on your GPUs – don’t let them sit idle for any amount of time.

Crafting ethical AI: Addressing bias and challengesCrafting ethical AI: Addressing bias and challenges

Did you know that 27.1% of AI practitioners and 32.5% of AI tools’ end users don’t specifically address artificial intelligence’s biases and challenges? The technology is helping to improve industries like healthcare, where diagnoses can be improved through rapidly evolving technology. 

However, this raises ethical concerns about the potential for AI systems to be biased, threaten human rights, contribute to climate change, and more. In our Generative AI 2024 report, we set out to understand how businesses address these ethical AI issues by surveying practitioners and end users.

With the global AI market size forecast to be US$1.8tn by 2030 and AI being deeply intertwined with our lives, it’s vital to address potential issues. Ethical AI is developing and deploying systems to highlight accountability, transparency, and fairness for human values. 

Understanding AI bias

Bias can occur throughout the various stages of the AI pipeline, and one of the primary sources of this bias is data collection. Outputs are more likely to be biased if the data collected to train AI algorithms isn’t diverse or representative of minorities.

It’s also important to recognize other stages where bias can occur unconsciously, such as: 

Data labeling. Annotators can have different interpretations of the same labels.Model training. The data collected must be balanced, and the model architecture capable of handling diverse inputs must be balanced, or the outputs could be biased.Model deployment. The AI systems must be monitored and tested for bias before deployment.

As we increasingly utilize AI in society, there have been situations where bias has surfaced. In healthcare, for example, computer-aided diagnosis (CAD) systems have been proven to provide lower accuracy results for black female patients when compared to white female patients.

With Midjourney, academic research found that, when asked, the technology generated images of people in specialized professions as men looking older and women looking younger, which reinforces gendered bias. 

A few organizations in the criminal justice system are using AI tools to predict areas where a high incidence of crime is likely. As these tools can often rely solely on historical arrest data, this can reinforce any existing patterns of racial profiling, leading to an excessive targeting of minority communities. 



Challenges in creating ethical AI

We’ve seen how bias can exist in AI, but that isn’t the only one it faces. AI can potentially improve business efficiency, but there are a few challenges to ensuring that the ethics of AI solutions are a key focus.

1. Security

AI can be susceptible to hacking; the Cybersecurity Infrastructure and Security Agency (CISA) mentions documented times when attacks have led to objects being hidden from security camera footage and autonomous vehicles acting poorly.

2. Misinformation

With the potential to cause severe reputational damage, it’s essential to curb the likelihood of AI tools spreading untrue facts by establishing proper steps when developing the technology. Misinformation can affect public opinion and spread the wrong information as if it is true.

3. Job displacement

AI can automate various work activities, freeing up valuable worker time. However, this could lead to job loss, with lower-wage workers needing to upskill or change careers. Creating ethical AI also includes making sure that tools complement jobs and not replace them.

4. Intellectual property

OpenAI had a lawsuit involving multiple famous writers who stated their platform, ChatGPT, illegally used their copyrighted work. The lawsuit claimed that AI exploits intellectual property, which can lead to authors being unable to make a living from their work.

5. Ethics and competition

With the constant need to innovate, companies may need to take more time to ensure their AI systems are designed to be ethically sound. Additionally, strong security measures must be in place to protect businesses and users.

Strategies to address AI bias

We wanted to know how practitioners and end users of AI tools addressed biases and challenges, as companies need to be aware of steps that need to be taken when using this technology.

1. Regular audits and assessments

44.1% of practitioners and 31.1% of end users stated they addressed bias by regular auditing and assessing. This often includes a comprehensive evaluation of AI system algorithms, where the first step is to understand where bias is more likely to occur.

Following this, it’s vital to examine for unconscious bias, such as disparities in how AI systems handle age, ethnicity, gender, and other factors. Recognizing these issues allows businesses to create and implement strategies to minimize and remove biases for improved fairness. This could be changing the training data for AI models or proposing new documentation.

2. Rely on tool providers’ ethical guidelines

According to UNESCO, there are ten core principles to make sure ethical AI has a human-centered approach:

Proportionality and do no harm. AI systems are to be used only when necessary, and risk assessments need to be done to avoid harmful outcomes from their use.Safety and security. Security and safety risks need to be avoided and addressed by AI actors.Right to privacy and data protection. Data protection frameworks need to be established alongside privacy.Multi-stakeholder and adaptive governance & collaboration. AI governance is essential; diverse stakeholders must participate, and companies must follow international law and national sovereignty regarding data use.Responsibility and accountability. Companies creating AI systems need to have mechanisms in place so these can be audited and traced.Transparency and explainability. AI systems need appropriate levels of explainability and transparency to ensure safety, security, and privacy.Human oversight and determination. AI systems can’t displace human accountability and responsibility.Sustainability. Assessments must be made to determine the impact AI systems have on sustainability. Awareness and literacy. It’s vital to ensure an open and accessible education for the public about AI and data.Fitness and non-discrimination. To ensure AI can benefit all, fairness, social justice, and non-discrimination must be promoted.

28.6% of end users and 22% of practitioners rely on AI tool providers to follow appropriate ethical guidelines, so it’s essential that AI systems have ethical AI in all stages of development and deployment of their technology.

An introduction to ethical considerations in AI
Ethics involves the broader considerations of artificial intelligence (AI) and how it plays a role in society beyond the code.

3. Don’t specifically address

A substantial percentage of end users and practitioners, 32.5% and 27.1%, respectively, said they don’t specifically address biases when using AI tools. With this technology being widely used across various industries, not addressing concerns and challenges could lead to further issues.

In addition to data bias, privacy is a top concern; smart home software, for example, must have robust privacy settings to prevent hacking or tampering. Similarly, AI systems can often make decisions that have a profound impact—autonomous vehicles must keep everyone on the road safe, and ensuring that AI doesn’t make mistakes is essential.

Crafting better AI tools

When creating AI tools, it’s important to focus on all aspects—ethical AI is, perhaps, the most vital component, as it affects output and how various minorities, such as gender and ethnicity, may be treated in industries like healthcare and law.

Our Generative AI 2024 report offers a comprehensive overview of how practitioners and end users use AI tools and how the sentiment is on the ground. Trust is fundamental for AI technology, so make sure to get your copy to learn how much confidence users currently have.

Your guide to LLMOpsYour guide to LLMOps

Navigating the field of large language model operations (LLMOps) is more important than ever as businesses and technology sectors intensify utilizing these advanced tools. 

LLMOps is a niche technical domain and a fundamental aspect of modern artificial intelligence frameworks, influencing everything from model design to deployment. 

Whether you’re a seasoned data scientist, a machine learning engineer, or an IT professional, understanding the multifaceted landscape of LLMOps is essential for harnessing the full potential of large language models in today’s digital world. 

In this guide, we’ll cover:

What is LLMOps?How does LLMOps work?What are the benefits of LLMOps?LLMOps best practices

What is LLMOps?

Large language model operations, or LLMOps, are techniques, practices, and tools that are used in operating and managing LLMs throughout their entire lifecycle.

These operations comprise language model training, fine-tuning, monitoring, and deployment, as well as data preparation.  

What is the current LLMops landscape?

LLMs. What opened the way for LLMOps.Custom LLM stack. A wider array of tools that can fine-tune and implement proprietary solutions from open-source regulations.LLM-as-a-Service. The most popular way of delivering closed-based models, it offers LLMs as an API through its infrastructure.Prompt execution tools. By managing prompt templates and creating chain-like sequences of relevant prompts, they help to improve and optimize model output.Prompt engineering tech. Instead of the more expensive fine-tuning, these technologies allow for in-context learning, which doesn’t use sensitive data.Vector databases. These retrieve contextually relevant data for specific commands.

The fall of centralized data and the future of LLMs
Gregory Allen, Co-Founder and CEO at Datasent, gave this presentation at our Generative AI Summit in Austin in 2024.

What are the key LLMOps components?

Architectural selection and design

Choosing the right model architecture. Involving data, domain, model performance, and computing resources.Personalizing models for tasks. Pre-trained models can be customized for lower costs and time efficiency. Hyperparameter optimization. This optimizes model performance as it finds the best combination. For example, you can use random search, grid research, and Bayesian optimization.Tweaking and preparation. Unsupervised pre-training and transfer learning lower training time and enhance model performance. Model assessment and benchmarking. It’s always good practice to benchmark models against industry standards. 

Data management

Organization, storing, and versioning data. The right database and storage solutions simplify data storage, retrieval, and modification during the LLM lifecycle.Data gathering and processing. As LLMs run on diverse, high-quality data, models might need data from various domains, sources, and languages. Data needs to be cleaned and pre-processed before being fed into LLMs.Data labeling and annotation. Supervised learning needs consistent and reliable labeled data; when domain-specific or complex instances need expert judgment, human-in-the-loop techniques are beneficial.Data privacy and control. Involves pseudonymization, anonymization techniques, data access control, model security considerations, and compliance with GDPR and CCPA.Data version control. LLM iteration and performance improvement are simpler with a clear data history; you’ll find errors early by versioning models and thoroughly testing them.

Deployment platforms and strategies

Model maintenance. Showcases issues like model drift and flaws.Optimizing scalability and performance. Models might need to be horizontally scaled with more instances or vertically scaled with additional resources within high-traffic settings.On-premises or cloud deployment. Cloud deployment is flexible, easy to use, and scalable, while on-premises deployment could improve data control and security. 


LLMOps vs. MLOps: What’s the difference?

Machine learning operations, or MLOps, are practices that simplify and automate machine learning workflows and deployments. MLOps are essential for releasing new machine learning models with both data and code changes at the same time.

There are a few key principles of MLOps:

1. Model governance

Managing all aspects of machine learning to increase efficiency, governance is vital to institute a structured process for reviewing, validating, and approving models before launch. This also includes considering ethical, fairness, and ethical concerns.

2. Version control

Tracking changes in machine learning assets allows you to copy results and roll back to older versions when needed. Code reviews are part of all machine learning training models and code, and each is versioned for ease of auditing and reproduction.

3. Continuous X

Tests and code deployments are run continuously across machine learning pipelines. Within MLOps, ‘continuous’ relates to four activities that happen simultaneously whenever anything is changed in the system:

Continuous integrationContinuous deliveryContinuous trainingContinuous monitoring 

4. Automation

Through automation, there can be consistency, repeatability, and scalability within machine learning pipelines. Factors like model training code changes, messaging, and application code changes can initiate automated model training and deployment.

MLOps have a few key benefits:

Improved productivity. Deployments can be standardized for speed by reusing machine learning models across various applications.Faster time to market. Model creation and deployment can be automated, resulting in faster go-to-market times and reduced operational costs.Efficient model deployment. Continuous delivery (CI/CD) pipelines limit model performance degradation and help to retain quality. 

LLMOps are MLOps with technology and process upgrades tuned to the individual needs of LLMs. LLMs change machine learning workflows and requirements in distinct ways:

1. Performance metrics

When evaluating LLMs, there are several different standard scoring and benchmarks to take into account, like recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU).

2. Cost savings

Hyperparameter tuning in LLMs is vital to cutting the computational power and cost needs of both inference and training. LLMs start with a foundational model before being fine-tuned with new data for domain-specific refinements, allowing them to deliver higher performance with fewer costs.

3. Human feedback

LLM operations are typically open-ended, meaning human feedback from end users is essential to evaluate performance. Having these feedback loops in KKMOps pipelines streamlines assessment and provides data for future fine-tuning cycles.

4. Prompt engineering

Models that follow instructions can use complicated prompts or instructions, which are important to receive consistent and correct responses from LLMs. Through prompt engineering, you can lower the risk of prompt hacking and model hallucination.

5. Transfer learning

LLM models start with a foundational model and are then fine-tuned with new data, allowing for cutting-edge performance for specific applications with fewer computational resources.

6. LLM pipelines

These pipelines integrate various LLM calls to other systems like web searches, allowing LLMs to conduct sophisticated activities like a knowledge base Q&A. LLM application development tends to focus on creating pipelines, not new ones. 

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How does LLMOps work?

LLMOps involve a few important steps:

1.  Selection of foundation model

Foundation models, which are LLMs pre-trained on big datasets, are used for downstream operations. Training models from scratch can be very expensive and time-consuming; big companies often develop proprietary foundation models, which are larger and have better performance than open-source ones. They do, however, have more expensive APIs and lower adaptability.

Proprietary model vendors:

OpenAI (GPT-3, GPT-4)AI21 Labs (Jurassic-2)Anthropic (Claude)

Open-source models:

LLaMAStable DiffusionFlan-T5

2. Downstream task adaptation

After selecting the foundation model, you can use LLM APIs, which don’t always say what input leads to what output. It might take iterations to get the LLM API output you need, and LLMs can hallucinate if they don’t have the right data. Model A/B testing or LLM-specific evaluation is often used to test performance.

You can adapt foundation models to downstream activities:

Model assessmentPrompt engineeringUsing embeddingsFine-tuning pre-trained modelsUsing external data for contextual information

3. Model deployment and monitoring

LLM-powered apps must closely monitor API model changes, as LLM deployment can change significantly across different versions.

What are the benefits of LLMOps?

Scalability

You can achieve more streamlined management and scalability of data, which is vital when overseeing, managing, controlling, or monitoring thousands of models for continuous deployment, integration, and delivery.

LLMOps does this by enhancing model latency for more responsiveness in user experience. Model monitoring with a continuous integration, deployment, and delivery environment can simplify scalability.

LLM pipelines often encourage collaboration and reduce speed release cycles, being easy to reproduce and leading to better collaboration across data teams. This leads to reduced conflict and increased release speed.

LLMOps can manage large amounts of requests simultaneously, which is important in enterprise applications.

Efficiency

LLMOps allow for streamlined collaboration between machine learning engineers, data scientists, stakeholders, and DevOps – this leads to a more unified platform for knowledge sharing and communication, as well as model development and employment, which allows for faster delivery.

You can also cut down on computational costs by optimizing model training. This includes choosing suitable architectures and using model pruning and quantization techniques, for example.

With LLMOps, you can also access more suitable hardware resources like GPUs, allowing for efficient monitoring, fine-tuning, and resource usage optimization. Data management is also simplified, as LLMOps facilitate strong data management practices for high-quality dataset sourcing, cleaning, and usage in training.

With model performance able to be improved through high-quality and domain-relevant training data, LLMOps guarantees peak performance. Hyperparameters can also be improved, and DaraOps integration can ease a smooth data flow. 

You can also speed up iteration and feedback loops through task automation and fast experimentation. 

3. Risk reduction

Advanced, enterprise-grade LLMOps can be used to enhance privacy and security as they prioritize protecting sensitive information. 

With transparency and faster responses to regulatory requests, you’ll be able to comply with organization and industry policies much more easily.

Other LLMOps benefits

Data labeling and annotation GPU acceleration for REST API model endpointsPrompt analytics, logging, and testingModel inference and servingData preparationModel review and governance

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LLMOps best practices

These practices are a set of guidelines to help you manage and deploy LLMs efficiently and effectively. They cover several aspects of the LLMOps life cycle:

Exploratory Data Analysis (EDA)

Involves iteratively sharing, exploring, and preparing data for the machine learning lifecycle in order to produce editable, repeatable, and shareable datasets, visualizations, and tables.

Be part of a community

Stay up-to-date with the latest practices and advancements by engaging with the open-source community.

Data management

Appropriate software that can handle large volumes of data allows for efficient data recovery throughout the LLM lifecycle. Making sure to track changes with versioning is essential for seamless transitions between versions. Data must also be protected with access controls and transit encryption.

Data deployment

Tailor pre-trained models to conduct specific tasks for a more cost-effective approach.

Continuous model maintenance and monitoring

Dedicated monitoring tools are able to detect drift in model performance. Real-world feedback for model outputs can also help to refine and re-train the models.

Ethical model development

Discovering, anticipating, and correcting biases within training model outputs to avoid distortion.

Privacy and compliance

Ensure that operations follow regulations like CCPA and GDPR by having regular compliance checks.

Model fine-tuning, monitoring, and training

A responsive user experience relies on optimized model latency. Having tracking mechanisms for both pipeline and model lineage helps efficient lifecycle management. Distributed training helps to manage vast amounts of data and parameters in LLMs.

Model security

Conduct regular security tests and audits, checking for vulnerabilities.

Prompt engineering

Make sure to set prompt templates correctly for reliable and accurate responses. This also minimizes the probability of prompt hacking and model hallucinations.

LLM pipelines or chains

You can link several LLM external system interactions or calls to allow for complex tasks.

Computational resource management

Specialized GPUs help with extensive calculations on large datasets, allowing for faster and more data-parallel operations.

Disaster redundancy and recovery

Ensure that data, models, and configurations are regularly backed up. Redundancy allows you to handle system failures without any impact on model availability. 

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