Accelerating Value from ML Models in Production: Mastering MLOps Challenges and Embracing Generative LLM

In an era of rapid technological advancements, MLOps (Machine Learning Operations) has emerged as the critical linchpin for businesses aiming to harness the power of Machine Learning (ML) and AI. By bridging the ML and software engineering gap, MLOps streamlines and standardizes building, deploying, and maintaining ML models in production environments, ultimately unlocking their full business potential.

(The rise of generative Large Language Models (LLMs) has accelerated the pace of innovation in the ML landscape, pushing companies to operationalize models swiftly and capitalize on their benefits. However, numerous challenges hinder the implementation of MLOps, creating a pressing need for robust MLOps management.)

As the demand for generative Large Language Models (LLMs) continues to surge, the ML landscape has witnessed an unprecedented acceleration in innovation. This rapid progress has compelled companies to expedite the operationalization of ML models, leveraging their immense benefits to gain a competitive edge.

However, amidst this fervent drive, various challenges have emerged, impeding the seamless onboarding of ML models that adds generative LLM capabilities to its systems. Consequently, a pressing need has arisen for robust and scalable development and deployment of models, aiming to overcome these obstacles.

Addressing Skills Gap and Operational Challenges: The Key to Unlocking LLM and ML Potential in Organizations

Many organizations grapple with a notable skills gap, as highlighted in a recent O'Reilly survey. According to the study, 44% of respondents identified this gap as a major hurdle when working with AI and ML technologies. This need for more expertise gives rise to several challenges, including subpar model performance, lengthy development cycles, and integration complexities. While most companies have dedicated teams of Data Scientists who specialize in developing new models, the actual challenges arise when these models, often existing as extensive notebooks, are handed over to deployment teams. The production environment typically operates on microservices systems, necessitating extensive refactoring of these notebooks. Unfortunately, they need to be returned to the Data Science team in many cases due to incompatibility issues with downstream applications intended to utilize these models.

ML/DS Workspace management poses yet another significant challenge that emerges with the advent of generative Large Language Models (LLMs). When working with LLMs, various approaches exist, such as embedding them within existing systems, utilizing them for quality assurance during data preprocessing and feature engineering, and organizing model development logs for more straightforward interpretation and analysis. Consequently, IT administrators are faced with the delicate task of striking a balance between the workspace requirements of Data Scientists and the associated costs and security considerations. In addition, the mounting pressure to foster an environment conducive to experimentation and innovation can exacerbate the tension between market responsiveness and governance objectives, further complicating the workspace management landscape.

According to a recent study conducted by Gartner, 68% of organizations struggle with effectively managing ML/DS workspaces, with cost optimization and security emerging as the top concerns. Furthermore, a survey conducted by McKinsey revealed that 82% of organizations need help to maintain an agile workspace environment, hampering their ability to respond to market demands swiftly.

Executive buy-in stands as one of the most critical barriers for enterprises seeking to expand their AI capabilities across customer-facing and operational domains. A recent survey conducted by Vantage Partners revealed that a mere 24% of executives had made substantial investments in AI and ML initiatives. This statistic highlights the prevailing challenge of securing buy-in from top-level decision-makers and underscores the significance of providing compelling evidence for the value proposition of ML initiatives.

This predicament becomes particularly pronounced when considering the incorporation of generative Large Language Models (LLMs) into ML development activities, given the considerable costs associated with training such models, often amounting to thousands of dollars. However, companies that have embraced LLMs early on are reaping substantial benefits, as evidenced by a comparative analysis of revenue growth. These pioneering companies enjoy the advantage of a more mature development framework, along with well-established processes and tools for deploying and sustaining the ML lifecycle. In contrast, the majority of organizations lag behind, lacking standardized and scalable processes and tools, thereby impeding their ability to fully leverage the potential of LLMs and ML at large.

According to a recent study conducted by Deloitte, organizations that have adopted LLMs at an early stage have witnessed an average revenue increase of 22% compared to their counterparts who have not yet embraced LLMs. Furthermore, a survey conducted by Forrester Research highlights that organizations with well-defined ML operationalization processes experience a 40% reduction in time-to-market for ML models, resulting in a significant competitive advantage. These statistics underscore the importance of executive buy-in and the need for organizations to swiftly overcome the challenges hindering the adoption of ML capabilities and maximize their potential for revenue growth and operational efficiency.

Overcoming Technical Challenges: MLOps for LLM

The introduction of generative LLMs increases the challenges faced by enterprises in managing ML operations. Approximately 85% of companies are in the early stages of their AI journey, characterized by the absence of consistent workflows and standards applied to the development, deployment, and sustainable ML lifecycle tasks. While this initial stage may yield results in small-scale projects, the presence of LLM models necessitates a more mature governance approach for ML assets, which include data, code, models, infrastructure, and middleware. Moreover, technical and business teams must work in constant collaboration. ML teams should not only be responsible for developing new ML capabilities at scale but also provide business stakeholders with visibility into the value added by these models in production. Simultaneously, the business should allocate resources to ML teams for experimenting with new AI capabilities, such as LLMs, while directing the ML teams to achieve model objectives regarding business impact.

To successfully incorporate LLM capabilities into their AI/ML systems, enterprises must follow a full, incremental, and iterative implementation cycle based on MLOps best practices.

MLOps is not a capability that you turn on within the analytics operations of a company. Instead, it's an evolving domain that requires consistent application of tools, processes, and standards, leading companies to an industrialization stage of their AI products. MLOps uses DevOps, DataOps, ModelOps, and several other concepts from different domains, like SecOps, to establish best practices across all the ML lifecycles.

Companies that embrace MLOps in their operations experience significant benefits. For example, they achieve a 4x reduction in time to market and a 30% decrease in investments compared to their counterparts without MLOps. Let's consider an example to illustrate this further:

Suppose Company A and Company B are both developing AI solutions. However, company A has implemented MLOps practices, whereas Company B still needs to. When Company A introduces a new ML model or updates an existing one, its MLOps framework ensures a streamlined process for development, testing, deployment, and monitoring. This leads to faster delivery of AI products to the market, reducing the time their customers can benefit from the latest innovations.

On the other hand, Company B needs an established MLOps framework. As a result, their ML development process may need to be more cohesive, needing proper version control, testing procedures, or monitoring capabilities. As a result, it takes them longer to deploy ML models, leading to delayed releases and missed opportunities in the market.

Moreover, Company A's adherence to MLOps practices allows them to optimize their resource allocation, resulting in a 30% reduction in overall investments. By efficiently managing their ML assets, such as data, code, and infrastructure, they can achieve cost savings while maintaining high-quality AI solutions.

Streamlining Processes and Optimizing Resources for LLM usage

The adoption of LLM introduces a set of challenges that extend beyond technical considerations. It involves effectively managing how people utilize generative LLM in their ML tasks, standardizing the utilization processes across all stages of the ML lifecycle, controlling API costs, and ensuring enterprise information security while leveraging these models. These challenges can be daunting, requiring careful attention and expertise.

Companies have two primary approaches to utilizing LLM. The first approach involves training their own LLM models for specific purposes. For instance, Bloomberg developed a specialized LLM model focusing on financial topics. This custom-built LLM enables them to generate high-quality content relevant to their domain, such as financial news articles or market analysis reports. In addition, by training their LLM, companies can leverage their capabilities to address specific business needs and gain a competitive advantage in their industry.

The second approach involves using LLM as part of the ML operations (Ops) framework to support ML tasks. In this scenario, LLM serves as a quality assurance (QA) component, executing various ML tasks to enhance efficiency and accuracy. While this approach is still under development, it promises to improve the overall ML lifecycle.

For the purpose of this discussion, let's focus on the first approach—training custom LLM models. This process involves several key steps. First, companies must gather a large and diverse dataset relevant to their domain, such as financial news articles, stock market data, or economic reports, depending on the specific use case. This dataset serves as the foundation for training the LLM model.

Next, the gathered dataset is used to train the LLM through techniques like unsupervised learning or reinforcement learning. Finally, the LLM model learns patterns and structures within the data, enabling it to generate coherent and contextually relevant output based on the input provided.

Once the LLM model is trained, it undergoes rigorous testing and validation to ensure its performance meets the desired standards. This involves evaluating the quality of the generated content, checking for biases or inaccuracies, and fine-tuning the model as needed.

Finally, the trained LLM model is integrated into the existing ML workflow, where it can be utilized for various tasks, such as generating financial reports, automating customer support responses, or assisting with data analysis.

By training custom LLM models, companies can tap into the power of generative AI to address specific business challenges and improve operational efficiency. However, it is important to note that deploying and managing LLM models requires careful consideration of ethical concerns, privacy regulations, and security measures to safeguard sensitive information.

In conclusion, adopting LLM presents challenges that extend beyond technical aspects, including managing usage, standardizing processes, controlling costs, and ensuring security. Companies can leverage LLM in two ways: training custom models for specific purposes or utilizing LLM as part of the ML Ops framework. Training custom LLM models allow companies to address specific business needs, while careful attention must be given to ethical considerations and data security throughout the entire ML lifecycle.

Mastering MLOps and Unlocking Business Value of Generative LLM: LLMOps concept

LLMOps, which focuses on the operational capabilities and infrastructure required to fine-tune instruction-tuned generative LLM models and deploy them as part of a product, is an essential aspect of MLOps. While LLMOps may not be a novel concept in the MLOps movement, it represents a distinct sub-category with specific requirements for fine-tuning and deploying these models.

Instruction-tuned LLM models, such as GPT-3.5 with its massive 175 billion parameters, demand enormous amounts of data and compute resources for training. For example, Lambda Labs estimates that training GPT-3 on a single NVIDIA Tesla V100 GPU would take approximately 355 years. Although fine-tuning these models may require a different scale of data or computation, it remains a substantial task. The key lies in having infrastructure capable of handling large datasets and leveraging parallel GPU machines effectively.

The cost implications of running inference on such massive models, like ChatGPT, have sparked discussions. While OpenAI has not made any public statements, these discussions underscore the need for a different level of computing resources compared to traditional ML models. Moreover, an inference may involve a single model and a chain of models and additional safeguards to ensure optimal output for end users.

In the LLMOps landscape, there are similarities to the broader MLOps ecosystem. However, many existing MLOps tools tailored for specific use cases may need to be more readily adapted to the requirements of fine-tuning and deploying LLMs. For instance, a Spark environment like Databricks, which works well for traditional ML, may not be suitable for fine-tuning LLMs. However, with the appropriate knowledge and experience, Databricks can instead be one of the best platforms to explore instruction-tuned LLM for different enterprise ML use cases.

Broadly speaking, the LLMOps landscape encompasses the following:

• Platforms: These platforms facilitate the fine-tuning, versioning, and deployment of LLMs while abstracting away the underlying infrastructure complexities.
• No-code and low-code platforms: Specifically designed for LLMs, these platforms provide a high-level abstraction layer to simplify adoption. However, they may have limitations in terms of flexibility.
• Code-first platforms (including specific MLOps platforms): These platforms cater to custom ML systems incorporating LLMs and other foundational models. They offer a combination of high flexibility and convenient access to computing resources for expert users.
• Frameworks: These frameworks aim to simplify the development of LLM applications. For example, they standardize interfaces between different LLMs and address prompt-related challenges.
• Ancillary tools: These tools streamline specific parts of the LLMOps workflow, such as testing prompts, incorporating human feedback (RLHF), or evaluating datasets.

By mastering MLOps and understanding the LLMOps landscape, organizations can effectively fine-tune and deploy generative LLMs, unlocking their true business value. It is crucial to choose the right combination of platforms, tools, and frameworks that align with the specific requirements of LLM development and deployment, enabling organizations to harness the potential of these powerful models in practical applications.

At Tredence, we have a proven track record of helping clients build and deploy ML models that deliver tangible business value. By mastering MLOps, we empower our clients to harness the full potential of their ML models and embrace the transformative power of generative LLMs.

AUTHOR - FOLLOW
Rodrigo Masini de Melo
Lead MLOps Engineer, Tredence Inc.

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