Key Steps in Implementing MLOps

If I were advancing my DevOps skills in this AI-driven era, understanding the MLOps process would be my starting point (also knowing the DevOps role in each stage) Let's break down what you need to know: 1. Data Strategy: Define goals and data needs for the ML project. ↳ DevOps Role: Provides infrastructure and tools for collaboration and documentation. 2. Data Collection: Acquire data from diverse sources, ensuring compliance. ↳ DevOps Role: Sets up and manages data pipelines, storage, and access controls. 3. Data Validation: Check quality and integrity of collected data. ↳ DevOps Role: Automates validation processes and integrates them into data pipelines. 4. Data Preprocessing: Clean, normalize, and transform data for training. ↳ DevOps Role: Provides scalable compute resources and infrastructure for preprocessing. 5. Feature Engineering: Create meaningful inputs from raw data. ↳ DevOps Role: Supports feature stores and automates feature pipeline deployment. 6. Version Control: Manage changes in data, code, and model setups. ↳DevOps Role: Implements and manages version control systems (Git) for code, data, and models. 7. Model Training: Develop models with curated data sets. ↳DevOps Role: Manages compute resources (CPU/GPU), automates training pipelines, and handles experiments (MLflow, etc.). 8. Model Evaluation: Analyze perf metrics. ↳DevOps Role: Integrates evaluation metrics into CI/CD pipelines and builds monitoring dashboards. 9. Model Registry: Log and store trained models with versions. ↳DevOps Role: Sets up and manages the model registry as a central artifact store. 10. Model Packaging: Bundle models and dependencies for deployment. ↳DevOps Role: Automates the containerization of models and their dependencies. 11. Deployment Strategy: Outline roll-out processes and fallback plans. ↳DevOps Role: Leads the design and implementation of deployment strategies (Canary, Blue/Green, etc.). 12. Infrastructure Setup: Arrange compute resources and scaling guidelines. ↳DevOps Role: Provisions and manages the underlying infrastructure (cloud resources, Kubernetes, etc.). 13. Model Deployment: Move models into the production environment. ↳DevOps Role: Automates the deployment process using CI/CD pipelines. 14. Model Serving: Activate model endpoints for application use. ↳ DevOps Role: Manages the serving infrastructure, scaling, and API endpoints. 15. Resource Optimization: Ensure compute efficiency and cost-effectiveness. ↳ DevOps Role: Implements auto-scaling, cost management strategies, and infrastructure optimization. 16. Model Updates: Organize re-training and version advancements. ↳DevOps Role: Automates the retraining and redeployment processes through CI/CD pipelines. It's a steep learning curve, but actively working on MLOps projects and understanding these stages is absolutely vital today.. 🔔 Follow Vishakha Sadhwani for more cloud & DevOps content. ♻️ Share so more people can learn. Image source: Deepak Bhardwaj

In enterprise AI - '23 was the mad rush to a flashy demo - '24 will be all about getting to real production value Three key steps for this in our experience: - (1) Develop your "micro" benchmarks - (2) Develop your data - (3) Tune your entire LLM system- not just the model 1/ Develop your "micro" benchmarks: - "Macro" benchmarks e.g. public leaderboards dominate the dialogue - But what matters for your use case is a lot narrower - Must be defined iteratively by business/product and data scientist together! Building these "unit tests" is step 1. 2/ Develop your data: - Whether via a prompt or fine-tuning/alignment, the key is the data in, and how you develop it - Develop = label, select/sample, filter, augment, etc. - Simple intuition: would you dump a random pile of books on a student's desk? Data curation is key. 3/ Tune your entire LLM system- not just the model: - AI use cases generally require multi-component LLM systems (eg. LLM + RAG) - These systems have multiple tunable components (eg. LLM, retrieval model, embeddings, etc) - For complex/high value use cases, often all need tuning 4/ For all of these steps, AI data development is at the center of getting good results. Check out how we make this data development programmatic and scalable for real enterprise use cases @SnorkelAI snorkel.ai :)

Scaling MLOps on AWS: Embracing Multi-Account Mastery 🚀 Move beyond the small team playground and build robust MLOps for your growing AI ambitions. This architecture unlocks scalability, efficiency, and rock-solid quality control – all while embracing the power of multi-account setups. Ditch the bottlenecks, embrace agility: 🔗 Multi-account mastery: Separate development, staging, and production environments for enhanced control and security. 🔄 Automated model lifecycle: Seamless workflow from code versioning to production deployment, powered by SageMaker notebooks, Step Functions, and Model Registry. 🌟 Quality at every step: Deploy to staging first, rigorously test, and seamlessly transition to production, all guided by a multi-account strategy. 📊 Continuous monitoring and feedback: Capture inference data, compare against baselines, and trigger automated re-training if a significant drift is detected. Here's how it unfolds: 1️⃣ Development Sandbox: Data scientists experiment in dedicated accounts, leveraging familiar tools like SageMaker notebooks and Git-based version control. 2️⃣ Automated Retraining Pipeline: Step Functions orchestrate model training, verification, and artifact storage in S3, while the Model Registry keeps track of versions and facilitates approvals. 3️⃣ Multi-Account Deployment: Staging and production environments provide controlled testing grounds before unleashing your model on the world. SageMaker endpoints and Auto Scaling groups handle inference requests, powered by Lambda and API Gateway across different accounts. 4️⃣ Continuous Quality Control: Capture inference data from both staging and production environments in S3 buckets. Replicate it to the development account for analysis. 5️⃣ Baseline Comparison and Drift Detection: Use SageMaker Model Monitor to compare real-world data with established baselines, identifying potential model or data shifts. 6️⃣ Automated Remediation: Trigger re-training pipelines based on significant drift alerts, ensuring continuous improvement and top-notch model performance. This is just the tip of the iceberg! Follow Shadab Hussain for deeper dives into each element of this robust MLOps architecture, explore advanced tools and practices, and empower your medium and large teams to conquer the AI frontier. 🚀 #MLOps #AI #Scalability #MultiAccount #QualityControl #ShadabHussain

Most ML systems don’t fail because of poor models. They fail at the systems level! You can have a world-class model architecture, but if you can’t reproduce your training runs, automate deployments, or monitor model drift, you don’t have a reliable system. You have a science project. That’s where MLOps comes in. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟬 - 𝗠𝗮𝗻𝘂𝗮𝗹 & 𝗙𝗿𝗮𝗴𝗶𝗹𝗲 This is where many teams operate today. → Training runs are triggered manually (notebooks, scripts) → No CI/CD, no tracking of datasets or parameters → Model artifacts are not versioned → Deployments are inconsistent, sometimes even manual copy-paste to production There’s no real observability, no rollback strategy, no trust in reproducibility. To move forward: → Start versioning datasets, models, and training scripts → Introduce structured experiment tracking (e.g. MLflow, Weights & Biases) → Add automated tests for data schema and training logic This is the foundation. Without it, everything downstream is unstable. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟭 - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 & 𝗥𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 Here, you start treating ML like software engineering. → Training pipelines are orchestrated (Kubeflow, Vertex Pipelines, Airflow) → Every commit triggers CI: code linting, schema checks, smoke training runs → Artifacts are logged and versioned, models are registered before deployment → Deployments are reproducible and traceable This isn’t about chasing tools, it’s about building trust in your system. You know exactly which dataset and code version produced a given model. You can roll back. You can iterate safely. To get here: → Automate your training pipeline → Use registries to track models and metadata → Add monitoring for drift, latency, and performance degradation in production My 2 cents 🫰 → Most ML projects don’t die because the model didn’t work. → They die because no one could explain what changed between the last good version and the one that broke. → MLOps isn’t overhead. It’s the only path to stable, scalable ML systems. → Start small, build systematically, treat your pipeline as a product. If you’re building for reliability, not just performance, you’re already ahead. Workflow inspired by: Google Cloud ---- If you found this post insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more deep dive AI/ML insights!