Generative AI + MLOPs: The Next Big Shift for Organizations

Artificial Intelligence (AI) has revolutionized the way we live and work. From autonomous vehicles to personal assistants, AI has created new opportunities for businesses and consumers alike. As we enter the age of Artificial Intelligence, companies that are quick to embrace the technology are seeing a positive impact on revenue and cost reduction.

A study by McKinsey Global Survey in 2019 revealed that the majority of executives from companies who participated in the study reported a boost in revenue in the areas where AI was used, and 44% said that AI has reduced costs. The report also found that companies who are considered AI high performers achieve greater scale and see both higher revenue increases and greater cost decreases than other companies who use AI. The AI high performers are more likely to apply core practices for using AI to drive value across the organization, mitigate risks associated with the technology, and retrain workers to prepare them for AI adoption. The study also highlighted that AI high performers achieve greater scale and revenue increases, which presents a roadmap for laggards to follow. Among emerging technologies, Generative AI and MLOps are poised to be the next big shift for businesses looking to innovate and stay ahead of the competition.

In this blog post, we will explore the significant impact that generative AI and MLOps will have on organizations, and how companies can leverage this shift to gain a competitive edge.

Embracing the Combined Power of Generative AI and MLOps:

Before we jump into the advantages of integrating generative AI and MLOps into your business, let's first understand what they are. Let's start with Generative AI. This technology is the pioneer of the next wave of AI. Generative AI is a type of machine learning that enables algorithms to generate original and unique content, such as images, text, music, and more. This technology has led to the development of some popular AI applications like Google's Deep Dream, OpenAI's GPT-3, and NVIDIA's GameGAN, which generates games without the need for human input. It enables machines to not only learn from data but also to create new data on their own. That's right, machines that can generate new data! It's like having a robot scientist on your team, only without the lab coat.

MLOps, on the other hand, is a set of practices that apply DevOps principles to machine learning workflows. It takes care of the nitty-gritty of AI deployment and management. It automates the entire machine learning process, making it faster, more efficient, and easier to manage. With MLOps, you can sit back and relax while the machines do the work. The approach helps to streamline the process of deploying and managing machine learning models. 

When combined, Generative AI and MLOps offer a powerful solution for businesses looking to deploy and manage machine learning models. This combination enables businesses to generate new data, build and deploy models more efficiently, and monitor and analyze model performance in real time. The benefits of this combination are clear: businesses can improve the accuracy and reliability of their models, reduce the time and resources required for model deployment, and drive innovation through the creation of new data.

Evolution of Generative AI 

Generative AI has come a long way since its inception and has been evolving at an unprecedented pace. From its initial application in Edtech, which mainly focused on language and text-based applications, to the recent advancements in Chat GPT, generative AI has now found its way into other domains such as video, image, music, and code creation.

In Edtech, generative AI has been used to create educational content such as interactive quizzes, exercises, and tutorials. One of the most popular examples of Edtech-based generative AI is Duolingo, which uses AI to personalize language learning for its users. The AI algorithms track the progress of each user and generate custom lessons based on their individual learning needs.

In the text creation space, GPT-3, Jasper, and Copy.ai are examples of generative AI tools that businesses are leveraging. GPT-3 is a powerful text generation model that has been used for a wide range of applications such as chatbots, language translation, and content creation. Jasper, on the other hand, is an AI writer and AI art generator that can quickly create blog posts, marketing copy, and AI-generated images. Copy.ai is another generative AI tool that businesses are leveraging to create compelling content for social media, ad copy, and even product descriptions.

Video creation has become another popular application of generative AI in recent times. Platforms like RunwayML are now available to artists who want to use machine learning tools to create visually stunning content without the need for coding. RunwayML allows users to generate photorealistic images, video captions, and much more.

Fliki is another platform that utilizes generative AI to help create video and audio content. It is a text-to-video and text-to-speech creator that can create high-quality content in a matter of minutes. Its AI-powered technology ensures that the output is of the highest quality, and it's a perfect tool for anyone looking to create video content quickly and easily.

When it comes to image creation, DALL-E 2 and Open AI are some of the most popular generative AI tools. DALL-E 2 is an AI system that generates images from textual descriptions. On the other hand, OpenAI's GPT-3 can also create images using natural language descriptions.

In the coding space, Mutable.ai and Replit are generative AI tools that can write code snippets for developers. They use machine learning to analyze code patterns and generate optimized code snippets that developers can integrate into their codebase.

Generative AI is also being leveraged in marketing and customer service. For example, AI chatbots are being used by businesses to provide quick and efficient customer service. The chatbots use natural language processing to understand customer queries and provide appropriate responses. The use of generative AI in customer service and marketing is enabling businesses to provide a better customer experience and drive customer engagement.

Generative AI is rapidly evolving, and its applications are expanding across various industries. The applications discussed above are just a few examples of the immense potential of generative AI. As businesses embrace generative AI, they are better positioned to stay ahead of the curve and provide better customer experiences.

How Businesses are Riding the AI Evolution

In a bold move, Microsoft has declared its commitment to invest a staggering $10 billion into OpenAI, the company behind the popular ChatGPT chatbot. It has announced a new version of its search engine Bing, powered by an upgraded version of the same AI technology that underpins ChatGPT. The company is launching the product alongside new AI-enhanced features for its Edge browser. The two will provide a new experience for browsing the web and finding information online. With the new AI technology, Bing will be able to provide more accurate search results, faster loading times, and more personalized experiences.

This significant investment represents the largest financial commitment to challenge Google in search since the launch of Bing over a decade ago. As if on cue, both Google and Microsoft introduced their new chatbots - Bard and ChatGPT, respectively - just one day apart. Meanwhile, Google recently hosted a conference in Paris where the company revealed more information about their progress in integrating AI into search. These developments demonstrate the intense competition and investment taking place in the world of AI-powered search, with companies vying to outdo each other in terms of innovation and technological advancements.

With more accurate search results and personalized experiences, businesses will need to optimize their websites and online content to rank higher in search results and attract more traffic. Additionally, the increased competition in the AI-powered search market may result in more innovative and efficient search tools, which could potentially benefit businesses in terms of productivity and efficiency. However, businesses may also need to adapt to new AI-based technologies and keep up with the latest advancements to remain competitive in their respective industries. ( Source: Bloomberg)

Navigating the Potential Challenges of Generative AI

“Alphabet Inc., the parent company of Google, took a massive hit recently after its highly-anticipated new chatbot, Google Bard, delivered a major blunder. During a live demonstration of its advanced AI capabilities, the chatbot provided an incorrect response about the James Webb Telescope, causing Alphabet's shares to plummet by a staggering $163 billion in just two days.” (Source: The Guardian)

As Generative AI continues to become more prevalent, it's important to consider the potential downsides that come along with their usage.

Overestimation of Capabilities

‍To perform tasks, generative AI algorithms require a massive amount of training data. These AI tools, however, cannot generate entirely new images or texts. They simply combine what they know in various ways. It is difficult to control the behavior of some Generative AI models. They perform erratically and produce unexpected results.

Risk of Bias and Discrimination

‍Language models can perpetuate and amplify biases in data and society. For example, if a language model is trained on biased data, it will generate biased outputs. This can lead to discrimination in various sectors like hiring, lending, and criminal justice.

Privacy Concerns

‍Generative AI can create deepfakes, which can pose a threat to privacy and security. People's images and voices can be manipulated to create fake videos and audio recordings. This can be used to spread false information or to create harm.

Misuse and Manipulation

‍Language models can be used to create propaganda or to spread fake news. This can be particularly dangerous in political campaigns or during times of crisis. Generative AI can be used to create fake reviews or to manipulate stock prices.

Legal Liability‍

If an AI system generates harmful or discriminatory content, the organization that created it could face legal consequences.

By applying best practices for deploying, managing, and monitoring machine learning models, businesses can ensure that their AI systems are performing as expected and not introducing bias or other unintended consequences. To address these and other potential downsides, businesses, and investors can leverage MLOps in the following ways:

Data Governance

MLOps can help organizations ensure that their data is unbiased and diverse. This can be done through data audits, data validation, and data anonymization.

Model Monitoring

MLOps can help organizations to monitor their language models and generative AI systems for bias, privacy, and security concerns. This can be done through continuous testing, monitoring, and feedback mechanisms.

Model Explainability

MLOps can help organizations ensure that their language models are explainable and transparent. This can be done through model interpretation techniques that explain how the model arrived at its decision.

Continuous Learning

MLOps can help organizations to continuously improve their language models and generative AI systems. This can be done through continuous learning techniques that enable the model to learn from new data and adapt to new situations.

In conclusion, while there are potential downsides to language models and generative AI, MLOps can help organizations to mitigate these risks and to reap the benefits of AI. By ensuring that their models are transparent, explainable, and continuously learning, organizations can leverage generative AI to create new and innovative products and services.

Common obstacles organizations face in implementing AI

As with any emerging technology, there are risks associated with ignoring generative AI and MLOps. Businesses that fail to embrace these technologies risk falling behind their competitors, losing market share, and missing out on new business opportunities. Additionally, businesses that ignore the ethical and responsible use of generative AI and MLOps risk reputational damage and potential legal liabilities. Some of the common problems companies face while dealing with Generative AI and MLOPs are:

Lack of data

‍One of the biggest challenges is having enough data to train the algorithms. Without enough data, the AI system may not be able to learn and produce accurate results. MLOPs can help overcome the issue of lack of data by creating synthetic data that can be used to train AI algorithms.

Difficulty in integrating with existing systems

‍Integrating AI systems with existing technology can be challenging, especially if legacy systems are in place. MLOps can help organizations overcome the challenge of integrating AI systems with existing technology by automating the deployment and maintenance of AI systems.

Lack of skills

‍There is a shortage of skilled professionals who can develop and maintain AI systems, which can make implementation challenging for many organizations. Organizations can overcome the lack of skills by investing in training and upskilling programs for their employees, or by partnering with companies that specialize in AI development.

Regulatory and ethical concerns

‍AI raises ethical and regulatory concerns around privacy, data protection, and bias, which can be difficult to navigate. By developing and adhering to ethical and regulatory frameworks, organizations can ensure that they are using AI responsibly and ethically.

High costs

‍Developing and implementing AI systems can be expensive, which can be a barrier for smaller organizations.  Cloud-based AI solutions can help reduce the costs associated with developing and implementing AI systems, making them more accessible to smaller organizations.

What is at Stake?

The rise of generative AI is undoubtedly transforming many industries, but what about the impact on the human workforce? While some experts predict that AI will result in widespread job displacement and mass unemployment, others believe that it will create new opportunities and transform the way we work. One thing is certain: the onset of generative AI will have a significant impact on people at every step of the journey. From the researchers and engineers developing the technology to the workers whose jobs are replaced, there are real human costs associated with the evolution of AI and machine learning.

Take Microsoft's recent investment of billions of dollars in OpenAI as an example. While this partnership is sure to result in many exciting advancements in the field of AI, it also raises concerns about job displacement. Thousands of Microsoft employees have already been informed that their jobs are on the chopping block, leaving them with uncertain futures. Google's recent move to bring in company founders Larry Page and Sergey Brin to help with the AI fight, while simultaneously letting go of experienced employees, only further underscores the precarious position of human workers in the face of generative AI.

While some argue that AI will ultimately create new jobs and opportunities, it is essential to acknowledge and address the immediate impact on human workers. Organizations investing in AI should prioritize ethical and responsible implementation, as well as provide support and resources for employees affected by technological changes. As we move forward into the age of generative AI, it is crucial to prioritize the human toll and work toward a future that is both innovative and humane.

Conclusion

As the commercialization of AI continues to gain momentum, major players in the tech industry are investing heavily in R&D initiatives to develop AI-based products and capabilities. From image and video editing to drug discovery, these advancements are transforming various sectors and creating new opportunities for growth and innovation.

To keep up with the pace of change and expand their core competencies, large tech consolidators will seek out strategic M&A opportunities. Meanwhile, venture and early-stage growth companies will look for investors that can provide capital and resources to further develop their technologies and offer access to a larger platform for product distribution.

One area of AI that is gaining traction is Generative AI, which allows for the creation of entirely new content through the use of algorithms. This technology, combined with MLOps, is helping companies like Jasper.ai to create production-ready AI solutions that can be applied to a variety of sectors.

With the continued evolution of AI and the increasing demand for end-to-end solutions, it is clear that MLOps and Generative AI will play a significant role in shaping the future of the tech industry. As such, investors and companies alike should keep a close eye on this space and be prepared to seize the opportunities that arise.