MLOps & LLMOps Power Tools: 16 Must-Haves & Nice-to-Haves for 2024
Comprehensive version of the MLOps and LLMOps toolbelt
In this article, I’ll give a comprehensive version of the MLOps and LLMOps toolbelt and cover all critical components required for deploying and managing machine learning models and LLMs in a production environment. In this context, a tool or a service means a component in your full architecture.
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Imagine building a powerful race car. You wouldn’t just throw parts together and hope it works, right? Just like a race car, deploying and managing machine learning models (ML) and Large Language Models (LLMs) requires a well-oiled set of tools. This article dives into essential components for your MLOps & LLMOps “toolkit,” helping you navigate the world of production-ready ML.
MLOps is the practice of managing the entire lifecycle of ML models, from development to deployment. Think of it as a smooth pit crew for your ML projects. LLMOps extends these practices to the specific needs of LLMs, which are powerful but complex language models.
1. Version Control (Think “Code History”)
Imagine going back in time to fix a bug in your model’s code. Version control systems like Git or GitHub act like a time machine, allowing you to track changes, collaborate with others, and pinpoint the exact version responsible for an issue. Version control systems also provide features such as branch protection rules and approval processes to ensure code quality.
Examples: GitHub, GitLab, BitBucket, Azure DevOps.
2. CI/CD (Streamline & Automate)
Picture a conveyor belt efficiently moving cars through different stages of production. Continuous Integration and Deployment (CI/CD) pipelines do the same for your ML projects. They automate tests, ensuring code quality before deployment, and streamline the release process, just like a well-oiled assembly line.
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Continuous integration and deployment pipelines are essential for seamless workflows. You can use it to automate tests and ensure code quality and consistency before deployment. Deployment to production should only occur through the CD pipeline, maintaining a controlled and reliable release process
Examples: GitHub Actions, GitLab CI, Azure Pipelines, Jenkins, CircleCI.
3. Workflow Orchestration (The Conductor)
Imagine an orchestra where each instrument plays its part at the right time. Workflow orchestrators like Apache Airflow or AWS Step Functions act as your conductor for ML workflows. They manage dependencies between steps like data pre-processing and model training, ensuring they run in the correct order for optimal performance.
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An end-to-end ML cycle contains multiple steps, such as preprocessing, feature engineering, model training, and model deployment. A workflow orchestrator helps to manage dependencies between these steps, automate tasks, and ensure that tasks run in the correct order.
Examples: Apache Airflow, Databricks Workflows, AWS Step functions.
4. Model Registry & Experiment Tracking (Keeping Track of Your Wins)
Imagine a trophy case for your best racing achievements. A model registry acts like that, storing trained models with their versions and metadata. This allows you to track progress, reproduce experiments, and maintain consistency across environments. Experiment tracking tools like MLflow keep detailed records of your experiments, letting you learn from each attempt and optimize future models.
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For proper model management and deployment, it is important to store and version your trained model artifacts with their associated metadata. This way, you can track different versions of your models, reproduce previous experiments, and maintain consistency across different environments (development, acceptance, production). Versioning also helps debugging and provides a clear history of changes.
Experimentation allows data scientists to try different algorithms and hyperparameters to optimize model performance. It is an iterative process of developing and testing models while keeping detailed records of each experiment and its parameters. With experiment tracking, each run can be reproduced and compared to others.
Examples: MLflow, Neptune.ai, Weights & Biases, Comet.ml.
5. Container Registry (Portable Powerhouse)
Imagine a standardized container that can house all the components needed to run your model — code, libraries, and dependencies. Container registries like Docker Hub or Amazon ECR securely store and manage these containers, allowing for easy deployment across different environments.
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In some use cases, you need to store and manage Docker images, which are used in your ML workflows. These images could be used for model training, testing, and serving. With versioned Docker images, you ensure the environment stays consistent across different stages of the ML lifecycle, supporting reproducibility and scalability.
Examples: Azure Container Registry, Docker Hub, Amazon ECR.
6. Compute (Model training & serving / (The Engine Room))
This refers to the processing power needed to train and run your models. It can be on-premise servers, cloud-based platforms like Google Vertex AI or AWS SageMaker, or specialized hardware like GPUs for computationally intensive tasks. Choosing the right compute resources depends on factors like model complexity and processing needs.
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To run your processing, training, and evaluation scripts and to serve your model in real-time use cases, you need a compute resource. It can be on-premises or in the cloud. The most important requirement is that your scripts should run consistently across different environments — development, acceptance, production, etc. — without requiring modifications.
Examples: Azure ML, AWS SageMaker, Google Vertex AI, Databricks, Kubernetes.
7. Feature Store & Serving (The Fuel Source)
Features are the building blocks used by models to make predictions. A feature store like Feast or Hopsworks acts as a central repository for managing and serving features, ensuring consistent and high-quality data for your models.
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A feature store is a central repository for managing and serving features used in ML models. It provides reusability of features across different models, and teams, ensures consistency, supports large datasets, and handles high query volumes.
Examples: Feast, Hopsworks, Databricks Feature Store, AWS Sagemaker.
8. Monitoring Systems(Keeping an Eye on Dashboard)
Just like monitoring a car’s performance, keeping an eye on your model’s health is crucial. Monitoring tools like Prometheus or Amazon SageMaker monitor metrics such as accuracy, latency, and resource utilization. This helps you identify and address any issues that might affect your model’s performance in production.
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Monitoring ML systems requires more, than standard software applications. It involves regular checks on the model performance to catch and address unexpected predictions by tracking metrics such as model accuracy, latency, and key KPIs. Setting up alerts and creating dashboards to monitor system health and performance is always a good practice, ensuring that any issues are promptly identified and addressed.
Examples: ELK Stack, Splunk, Prometheus + Grafana, AWS SageMaker.
9. Labeling Data
Labeled data is required for ML models, especially supervised learning tasks, and it’s not always provided out of the box. The quality and accuracy of labels have a direct impact on the performance of the model. Labeling tools can provide a nice interface, and quality assurance, and allow for collaboration between multiple annotators.
Examples: Amazon SageMaker Ground Truth, Labelbox, Scale AI.
10. Responsible AI (Steering the Right Way)
Responsible AI is an approach that considers ethical and legal points when developing and deploying artificial intelligence (AI). The goal is to create safe, reliable, and ethical AI applications.
For small or large organizations, implementing Responsible AI within the end-to-end ML cycle ensures compliance with regulations, mitigates risks and biases, prevents unfair AI systems, and builds public trust. This is not only important for protecting one organization’s reputation, but it also has an impact on how AI is perceived by society.
Examples: Guardrails AI, Arthur, Fiddler, AWS Bedrock Guardrails.
11. Vector Database (Understanding the Language)
With the increased popularity of LLMs, vector databases have become inevitable for many LLM-based use cases. A vector database is a collection of data stored as mathematical representations, as known as embeddings, alongside their metadata. There are many vector database providers.
Examples: Quadrant, Weaviate, Opensearch, Pinecone.
12. Model Hub (Pre-built Power)
A model hub is a collection of pre-trained models or endpoints that can be directly used in an LLM application. These models, whether open-source or proprietary, are provided by different organizations. They are often preferred over training a large language model in-house because they require significant time, computational resources, and huge amounts of data for training.
Examples: Sagemaker Jumpstart, AWS Bedrock, Hugging Face, Github.
13. Human in the Loop
In some ML models, involving humans in the decision-making or validation process can help increase performance, especially in situations where the model’s predictions or actions are uncertain, risky, or require human judgment. Large Language Models (LLMs) can particularly benefit from this approach. It is also known as Reinforcement Learning from Human Feedback (RLHF), where human-provided feedback is used in training or fine-tuning reinforcement learning models. Amazon A2I and SageMaker Ground Truth are services that can be used to implement this.
14. LLM Monitoring
Monitoring and analysis of LLM applications is necessary to ensure their performance and security. It improves the explainability of models by providing insights and helps detect issues quickly by offering end-to-end visibility.
Examples: LangCheck, HoneyHive, Langtrace AI.
15. Prompt Engineering
Prompt engineering is the practice where the inputs for mainly LLMs are designed to produce optimal outputs. Even though LLMs mimic human answers, it helps to adapt instructions to get higher-quality, more relevant, and also more secure answers.
Examples: Promptflow, MLflow.
16. LLM Frameworks
LLM frameworks are the architecture and software tools that help to develop, train, and deploy LLMs. They reduce complexity and make it easier to create LLM-based AI applications.
Examples: Llama Index, LangChain, Hugging Face Agents.
Conclusion:
Don’t be a fan of end-to-end “MLOps tools” that claim to do it all. In essence, it is all about how you use these tools and integrate them with other systems. In any organization, introducing a single tool can result in complicated sourcing and security discussions. Investing in MLOps is always worthwhile, both money and time-wise, but it is also important to keep it at the most efficient level.
The goal is not to create a complex architecture but to design a simple system with all the necessary functionalities.
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