How to Improve Model Deployment Processes

𝐓𝐡𝐞 𝐛𝐞𝐬𝐭 𝐌𝐋 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬𝐧’𝐭 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐬 𝐰𝐞𝐥𝐥 𝐢𝐧 𝐚 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤—𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐨𝐧𝐞 𝐭𝐡𝐚𝐭 𝐫𝐮𝐧𝐬 𝐢𝐧 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧. 🚀 It’s time we shift the focus from experimentation to execution. Model deployment isn’t an afterthought—it’s a core skill every ML practitioner must master. Here’s how to level up your ML deployment game: 👉 Structure Your Code Like a Pro Ditch messy notebooks. Use clean Python scripts with modular structure—Cookiecutter templates can save your life here. 👉 Log & Monitor Everything From training metrics to production drift, implement structured logging and model monitoring for clear visibility and control. 👉 Automate with Pipelines Use tools like DVC or MLflow to version data, track experiments, and automate retraining or deployments. 👉 Use Config Files, Not Hardcoded Values Externalize your config with YAML or JSON—cleaner code, better reproducibility, faster updates. 👉 Choose the Right Framework Flask, FastAPI, Django—or go serverless with AWS Lambda or Google Cloud Functions for effortless scalability. Want to dive deeper? Check out this slide by Zhao Rui, which breaks down: • Transitioning from notebooks to production-ready code • Setting up logging & configuration • Real-time vs batch vs edge deployments • Using Flask, FastAPI, and serverless tools • Best practices in MLOps Remember: Building a model is just the beginning. Getting it into production is where the real impact begins.

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!

If you are working in a big tech company on ML projects, chances are you are working on some version of Continuous Integration / Continuous Deployment (CI/CD). It represents a high level of maturity in MLOps with Continuous Training (CT) at the top. This level of automation really helps ML engineers to solely focus on experimenting with new ideas while delegating repetitive tasks to engineering pipelines and minimizing human errors. On a side note, when I was working at Meta, the level of automation was of the highest degree. That was simultaneously fascinating and quite frustrating! I had spent so many years learning how to deal with ML deployment and management that I had learned to like it. I was becoming good at it, and suddenly all that work seemed meaningless as it was abstracted away in some automation. I think this is what many people are feeling when it comes to AutoML: a simple call to a "fit" function seems to replace what took years of work and experience for some people to learn. There are many ways to implement CI/CD/CT for Machine Learning but here is a typical process: - The experimental phase - The ML Engineer wants to test a new idea (let's say a new feature transformation). He modifies the code base to implement the new transformation, trains a model, and validates that the new transformation indeed yields higher performance. The resulting outcome at this point is just a piece of code that needs to be included in the master repo. - Continuous integration - The engineer then creates a Pull Request (PR) that automatically triggers unit testing (like a typical CI process) but also triggers the instantiation of the automated training pipeline to retrain the model, potentially test it through integration tests or test cases and push it to a model registry. There is a manual process for another engineer to validate the PR and performance reading of the new model.  - Continuous deployment - Activating a deployment triggers a canary deployment to make sure the model fits in a serving pipeline and runs an A/B test experiment to test it against the production model. After satisfactory results, we can propose the new model as a replacement for the production one. - Continuous training - as soon as the model enters the model registry, it deteriorates and you might want to activate recurring training right away. For example, each day the model can be further fine-tuned with the new training data of the day, deployed, and the serving pipeline is rerouted to the updated model. The Google Cloud documentation is a good read on the subject: https://lnkd.in/gA4bR77x  https://lnkd.in/g6BjrBvS ---- Receive 50 ML lessons (100 pages) when subscribing to our newsletter: TheAiEdge.io #machinelearning #datascience #artificialintelligence

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

This MLOps workflow outlines a structured approach to managing machine learning models from development to deployment, leveraging various tools and platforms. Here's a breakdown of the process: Week 0: Configuration Management - Hydra: Fetches configurations using Hydra, a framework for configuring complex applications. Week 1: Model Conversion - ONNX: Converts the trained model to ONNX format, designed for cross-framework compatibility. Week 2: Deployment and Configuration - D/C: Involves deployment and configuration tasks. Week 3: Model Persistence - AWS S3: Persists and fetches the trained model from Amazon S3. Week 4: Model Packaging - Docker: Packages the model and dependencies for consistent deployment. Week 5: Containerization and Deployment - AWS ECR & Lambda: Builds and updates Docker images in Amazon ECR, utilizing AWS Lambda. Week 6: Logging and Monitoring - CloudWatch & Elasticsearch: Streams logs for monitoring and troubleshooting. Week 7: Query and Response - API Gateway & Lambda: Handles requests, computes, and returns results. Week 8: Log Analysis - Elasticsearch & Kibana: Updates the monitoring dashboard with log data. Week 9: Dashboard Updates - Monitoring Dashboard: Provides insights into system performance and health. This workflow showcases a comprehensive approach to managing machine learning models, encompassing configuration, deployment, and monitoring using modern tools and platforms. #MLOps #MachineLearning #Deployment #Monitoring #AI #Technology