MLOps - The Assembly Line of ML
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The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
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Abstract
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Abstract
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A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold.
While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence?
The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces.
However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces.
Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)
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MLOps - The Assembly Line of ML
- 1. MLOps: The Assembly Line of Machine Learning Jordan Birdsell Chief Architect, ML
- 2. phData Services 2 Data Platform Administration Data Architecture & Engineering Machine Learning & MLOps Services and software to build, manage, and secure your data platform, on- premises or in the cloud. Data engineering to integrate, transform, and serve all of your enterprise data: big, medium, small, complex, structured, and unstructured. Methodologies and expertise to perform feature engineering, model training or refitting, and model validation.
- 3. It is anticipated that spending on cognitive systems will triple in the next 4 years. Value from innovation comes through the ability to mass produce and distribute your ideas
- 4. Research to Value 4 PresentationsResearch PoCs Industrialized ML Academia Corporate R&D Data Science MLOps & ML Engineering
- 5. Why MLOps? ➔ Organizations need to deploy systems of models, not just one off solutions ➔ Models must be constantly monitored and retrained to maintain accuracy ➔ Structure is inconsistent from project to project ➔ Models and experiments are not properly tracked ➔ Code and dependencies aren’t being properly managed 5
- 7. Beyond Traditional Data DevOps ➔ Ground Truth Collection - Data Labeling/Annotating ➔ Broad scope of tooling, languages and libraries for single platform ➔ API-style services ➔ Statistical performance monitoring ➔ Lineage for models (code, parameters, metrics, artifacts) ➔ Deployment styles (A/B testing, Canary tests, etc) 7
- 11. AWS SageMaker 11
- 13. People & Activities 13 Data Science ML Engineering ML Platform Support MLOps Support Data Exploration Model Training & Experimentation Evaluation Continuous Training and Development Deployment Monitoring Research Industrialized Machine Learning BUILD RUN BUILD RUN
- 15. Use-Case Exploration Data Identification & Acquisition Data Exploration & Discovery Feature Engineering Model Training Validation & Testing Scope your project to ensure a measurable and successful delivery. Identify and ingest all the data necessary to set data scientists up for success. Data scientists determine if a model can be built and lay the foundation for remaining work. Data scientists are data artists who drive maximum model performance with expertly crafted features. Data scientists know the right algorithm for your unique problem and know how to train that model at scale. Establish ongoing validation that will help identify drift before it is a problem. Governance Your machine learning and AI must live within appropriate legal, ethical, and regulatory constraints. Data Science 15
- 16. Data Scientist Machine Learning Engineer Data Engineer ML Engineering Model Development Model Deployment Data Pipelines
- 18. Q&A 18
- 19. phData.io