Key Components of MLOps

Your Models Are Just 𝗘𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Without 𝗠𝗟𝗢𝗽𝘀 Most machine learning models never make it to production—or worse, they fail after deployment. Why? Because without MLOps, they remain nothing more than costly experiments. MLOps isn’t just about automation; it’s about 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁. A well-defined MLOps pipeline ensures your models don’t just work in a notebook but deliver real impact in production. Here’s the 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 𝗠𝗟𝗢𝗽𝘀 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 that transforms ML models from research to production: ⭘ 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 ✓ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 – Collect raw data from multiple sources. ✓ 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – Ensure data quality, consistency, and integrity. ✓ 𝗖𝗹𝗲𝗮𝗻 𝗗𝗮𝘁𝗮 – Handle missing values, remove duplicates, and standardise formats. ✓ 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘀𝗲 𝗗𝗮𝘁𝗮 – Convert into a structured and uniform format. ✓ 𝗖𝘂𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – Organise for better feature engineering. ⭘ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 ✓ 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 – Identify key patterns and signals. ✓ 𝗦𝗲𝗹𝗲𝗰𝘁 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 – Retain only the most relevant ones. ⭘ 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 ✓ 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗖𝗮𝗻𝗱𝗶𝗱𝗮𝘁𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 – Explore ML algorithms suited to the task. ✓ 𝗪𝗿𝗶𝘁𝗲 𝗖𝗼𝗱𝗲 – Implement and optimise training scripts. ✓ 𝗧𝗿𝗮𝗶𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 – Use curated data for accurate predictions. ✓ 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 – Assess performance using key metrics. ⭘ 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 ✓ 𝗦𝗲𝗹𝗲𝗰𝘁 𝗕𝗲𝘀𝘁 𝗠𝗼𝗱𝗲𝗹 – Choose the highest-performing model aligned with business goals. ✓ 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 – Prepare for deployment with necessary dependencies. ✓ 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗠𝗼𝗱𝗲𝗹 – Track models in a central repository. ✓ 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘀𝗲 𝗠𝗼𝗱𝗲𝗹 – Ensure portability and scalability. ✓ 𝗗𝗲𝗽𝗹𝗼𝘆 𝗠𝗼𝗱𝗲𝗹 – Release into a production environment. ✓ 𝗦𝗲𝗿𝘃𝗲 𝗠𝗼𝗱𝗲𝗹 – Expose via APIs for seamless integration. ✓ 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗠𝗼𝗱𝗲𝗹 – Enable real-time predictions for decision-making. ⭘ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 ✓ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗠𝗼𝗱𝗲𝗹 – Track drift, latency, and performance. ✓ 𝗥𝗲𝘁𝗿𝗮𝗶𝗻 𝗼𝗿 𝗥𝗲𝘁𝗶𝗿𝗲 𝗠𝗼𝗱𝗲𝗹 – Update models or phase them out based on real-world performance. 𝘉𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘢 𝘮𝘰𝘥𝘦𝘭 𝘪𝘴 𝘦𝘢𝘴𝘺. 𝘔𝘢𝘬𝘪𝘯𝘨 𝘪𝘵 𝘸𝘰𝘳𝘬 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘺 𝘪𝘯 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘪𝘴 𝘵𝘩𝘦 𝘳𝘦𝘢𝘭 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦. 𝗠𝗟𝗢𝗽𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗮𝗻 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗮𝗻 𝗜𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺.

Introducing MLOps by Mark Treveil & the Dataiku Team Most ML books end at model training, but this one starts where real impact begins, with MLOps. It shows you how to turn models into production-ready, scalable systems that work in the real world and that you can apply immediately to your projects. 1. Why MLOps Exists - Training a model ≠ creating value. - Without MLOps, models die in notebooks. - MLOps brings reliability, scale, and accountability. 2. It’s a Team Effort - Models don’t go live without coordination. - Data scientists build them, but engineers, ops, and product teams keep them running. - The best MLOps setups align tech with business by design. 3. From Notebook to Production - CI/CD, testing, and containers aren’t just extras, they’re the basics to build reliable software today. - Deploying a model is just the beginning. - Versioning + automation = survival. 4. Model Monitoring is Mandatory - Performance drops? Data drift? You’ll miss it without monitoring. - Log everything. Track everything. - Build alert systems like you would for production code. 5. Continuous Learning Loop - Models need retraining regularly. - Automate retraining pipelines with triggers and evaluations. - Keep humans in the loop for critical decisions. 6. Responsible & Governed AI - Explainability, fairness, and traceability are non-negotiable. - MLOps helps enforce compliance across the pipeline. - Transparency is not optional, it’s expected. 7. MLOps in the Enterprise - Real-world use cases: fraud detection, recommendations, forecasting. - Shows how companies scaled ML without chaos. - Key lesson: process beats ad hoc pipelines. If you’re serious about ML in production, you need MLOps. It’s the difference between a model that runs and one that delivers business value, at scale.

Using MLOps for Generative Models: Generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have unique challenges due to their complexity and dual-nature training processes. MLOps, or DevOps for machine learning, can help streamline the lifecycle of these models. Here's how you can apply MLOps principles to generative models: Version Control: Use tools like Git, DVC, or MLflow to version your model architecture, training scripts, and datasets. This ensures reproducibility and traceability. Continuous Integration (CI): Automate testing of your generative model's code to ensure that changes don't introduce bugs. Use CI tools like Jenkins or CircleCI to run unit tests, style checks, and other validations. Continuous Training (CT): Regularly retrain your generative models on new data or when significant drift is detected. Automate the training pipeline using tools like Kubeflow or TFX. Monitoring: Monitor the cost and performance metrics, including issues like mode collapse in GANs, of your generative models in real time. Tools like Prometheus or Grafana can assist with this real-time monitoring Continuous Deployment (CD): Once the model is trained and validated, automate its deployment to production environments. Use containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes) to ensure scalability and easy rollbacks. Feedback Loop: Collect feedback on the outputs of your generative models. This can be from user interactions or other metrics that gauge the quality of generated content. Use this feedback to retrain or fine-tune your models, ensuring they remain relevant and high-quality. Be aware of the ethical implications of the content generated. This can be part of the Feedback Loop, where user feedback can highlight any ethical concerns. Model Validation: Due to the stochastic nature of generative models, it's essential to validate the generated outputs regularly. Implement automated validation checks that assess the quality, diversity, and relevance of generated content. Model Explainability: Generative models can be black boxes. Use tools and techniques to shed light on how they work, which can be crucial for stakeholder trust. Tools like SHAP or LIME can be adapted to provide insights into generative models. Security: Ensure that the deployment environment is secure. Generative models can be exploited to produce malicious content. Implement strict access controls, monitoring, and anomaly detection to safeguard your deployment. Collaboration: Foster collaboration between data scientists, ML engineers, and operations teams. This ensures that the entire lifecycle of the generative model, from design to deployment, is smooth and efficient. By integrating continuous training, deployment, monitoring, and feedback, you can ensure that your generative models are robust, relevant, and consistently delivering value. #mlops #generativeai #ai #devops #ml

AI is no longer a single discipline — it’s a full-stack ecosystem. From AIOps → MLOps → DevOps → LLMOps, each layer represents a critical stage in building, deploying, and scaling intelligent systems in production. Let’s break it down: 🔹 AIOps (AI for IT Operations) Focus: Reliability & automation Telemetry ingestion (logs, metrics, traces) Data normalization across systems Anomaly detection (rules + ML models) Event correlation to reduce noise Auto-remediation (runbooks, alerts, ticketing) 👉 Goal: Reduce downtime and operational overhead 🔹 MLOps (Machine Learning Operations) Focus: Model lifecycle management Problem definition & data collection Data/version control (datasets + features) Feature engineering & pipelines Model training, validation, deployment Monitoring (drift detection, performance alerts) 👉 Goal: Ship reliable, reproducible ML systems 🔹 DevOps Focus: Delivery & infrastructure Backlog planning & version control CI/CD pipelines Automated testing (unit + integration) Infrastructure as Code (Terraform, CloudFormation) Observability (logs, metrics, SLOs) 👉 Goal: Fast, stable, and scalable releases 🔹 LLMOps (Large Language Model Ops) Focus: AI-native application layer Use-case scoping & data curation Prompt engineering (templates, few-shot learning) Model selection (OpenAI, open-source, fine-tuned) Guardrails (toxicity filters, grounding, evals) Deployment & monitoring (latency, cost, hallucination rate) 👉 Goal: Safe, reliable, and cost-efficient AI experiences 💡 Key Insight: DevOps builds the pipeline → MLOps adds intelligence → AIOps optimizes operations → LLMOps enables next-gen applications And if we’re being honest… many of us started from basics like HTML, CSS, and JavaScript tutorials — big respect to w3schools.com for quietly laying the foundation for millions of developers stepping into AI today 🙌 The shift is clear: We’re moving from writing code → to engineering intelligent, self-improving systems. If you’re working across these layers, you’re not just a developer anymore — you’re building the future of software. Follow me for deep dives on AI systems, cloud architecture, and real-world engineering 🚀 #AI #AIOps #MLOps #LLMOps #DevOps #MachineLearning #LLM #CloudComputing #AWS #SystemDesign #PlatformEngineering #DataEngineering #W3Schools #TechLeadership #Innovation

Stop mixing AIOps, MLOps, and LLMOps. They touch the same stack, but they ship different outcomes: reliability, prediction, and grounded generation. Example, Bank launching a Smart Collections release: AIOps (Reliability) What it does: Watches service health during the release. Correlates spikes in 5xx + queue lag from Kafka + DB lock waits → flags risky change → triggers progressive rollback if SLO burn rate > 2 for 10 min. Inputs: Logs/metrics/traces, deploy events, topology map, runbooks. Controls: Auto-remediation playbooks (scale out, cache warm, feature flag off), change approval gates. KPIs (illustrative): MTTR < 15 min, alert precision > 85%, p95 API latency < 400 ms, change failure rate < 15%. MLOps (Prediction) What it does: Ships a repayment-propensity model behind canary (5% → 25% → 100%). Monitors data/feature drift and retrains weekly if PSI > 0.2 or AUC drops > 3 pts. Inputs: Feature store (payment history, income signals), labeled outcomes, model registry. Controls: Reproducible pipelines (train/validate/package/deploy), shadow tests, canary rollback on AUC/latency regressions. KPIs (illustrative): AUC ≥ 0.86 in prod, inference latency < 80 ms, drift alerts/week, canary success %, lineage & model card completeness. LLMOps (Grounded Generation) What it does: Runs a collections copilot for agents with RAG over policy/contract PDFs. Enforces pre/post safety filters (PII masking, tone), tool use (CRM notes, payment plans), and a release gate (eval suite must pass before rollout). Inputs: Vector index of policies/contracts, prompts, tool schemas, red-team eval sets, chat transcripts. Controls: Prompt/version governance, grounding checks (context-hit ≥ 0.8), refusal policy, human-in-loop for high-risk offers. KPIs (illustrative): Grounding score ≥ 0.85, hallucination rate < 1%, p95 response < 1.2s, cost/1K tokens within budget, guardrail hit rate, red-team pass %. Who signs off: AIOps → SRE/Platform (change window + SLOs) MLOps → ML Eng/DS (model AUC/latency + drift) LLMOps → AI Platform + Safety (eval pass, guardrails, audit logs) #AIOps #MLOps #LLMOps #GenAI #PlatformEngineering #EnterpriseAI

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.

🏭🧠 For OT and IT architects building Industrial AI applications, the gap between an AI prototype and a reliable production system is often where projects fail. Data Scientist-led experiments are "clean," but industrial operations are messy. To move AI from the lab to the plant floor, your MLOps strategy must address three critical pillars: 1/ DataOps (The Foundation): Industrial data is often scattered. MLOps creates a "single source of truth" using tools like a Data Lakehouse and Unity Catalog, ensuring your models aren't running on "shifting sand" or inconsistent sensor inputs. 2/ ModelOps (The Decision Engine): Decisions on the factory floor must be auditable. MLOps provides Reproducibility and Governance, tracking exactly how a model was built, who approved it, and how it’s performing against real-time telemetry. 3/ DevOps (The Execution): High-stakes environments can’t afford "it worked in development" excuses. MLOps automates the CI/CD pipeline, ensuring code is tested, modular, and ready for the rigors of 24/7 operations. The Bottom Line: High MLOps maturity shifts your AI from a manual, reactive effort into a stable, engineered capability. It creates a measurable ROI through improved quality and throughput of your production operations. See the full post by Jiayi Wu and Alex Miller on the Databricks Community Blog: https://lnkd.in/gupqFNS6

⚙️ 𝐌𝐋𝐎𝐩𝐬 𝐁𝐮𝐢𝐥𝐝 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞: 𝐀𝐖𝐒 𝐒𝐚𝐠𝐞𝐌𝐚𝐤𝐞𝐫 𝐚𝐧𝐝 𝐆𝐢𝐭𝐇𝐮𝐛 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 🛠️ ⇢ 𝘔𝘓𝘖𝘱𝘴 𝘸𝘪𝘵𝘩 𝘈𝘞𝘚 𝘚𝘦𝘳𝘪𝘦𝘴 — 𝘗𝘢𝘳𝘵 10 Just published a comprehensive guide on implementing MLOps practices that automate the entire machine learning workflow - from data processing to model registry. 𝑾𝒉𝒂𝒕 𝒕𝒉𝒊𝒔 𝒄𝒐𝒗𝒆𝒓𝒔: ☞ Automated data preparation and model training ☞ Continuous integration for ML workflows ☞ Model evaluation with quality gates ☞ Seamless integration between GitHub and AWS SageMaker ☞ Custom SageMaker project templates 𝑲𝒆𝒚 𝒃𝒆𝒏𝒆𝒇𝒊𝒕𝒔: ☞ Zero manual intervention once set up ☞ Consistent model development process ☞ Automatic model registration with approval workflows ☞ Cost-optimized pipeline configuration 𝑻𝒉𝒆 𝒑𝒊𝒑𝒆𝒍𝒊𝒏𝒆 𝒂𝒖𝒕𝒐𝒎𝒂𝒕𝒊𝒄𝒂𝒍𝒍𝒚: ☞ Processes your data when code changes are pushed ☞ Trains models using XGBoost on SageMaker ☞ Evaluates model performance against quality thresholds ☞ Registers approved models in SageMaker Model Registry This setup eliminates the manual overhead of running ML experiments and ensures every model follows the same rigorous process before reaching production consideration. The guide includes step-by-step instructions for AWS CodeConnection setup, IAM configuration, Lambda deployment, and Service Catalog template creation. Perfect for ML engineers and data scientists looking to implement production-grade MLOps practices without the complexity of building everything from scratch. ✍️ 𝐋𝐢𝐧𝐤 𝐭𝐨 𝐟𝐮𝐥𝐥 𝐠𝐮𝐢𝐝𝐞: https://lnkd.in/gsTRsRVz 🚀 𝐒𝐨𝐮𝐫𝐜𝐞 𝐂𝐨𝐝𝐞: https://lnkd.in/gAsxDhva #MLOps #MachineLearning #AWS #SageMaker #GitHubActions #DataScience #CloudComputing #Automation

As a former SRE and during my time at DataRobot, I've seen firsthand how crucial it is to have a robust MLOps strategy in place. But let's be honest - implementing MLOps can be a real challenge. 🤔 Recently, I've been exploring how tools can enhance MLOps practices. 1. Continuous monitoring is key: track model performance, data drift, and system health in real-time. This allows us to catch issues before they impact our production systems. 2. Automation: By integrating into a CI/CD pipeline, you can automate much of our model deployment and monitoring processes. This not only saves time but also reduces human error. 3. Data-centric management is crucial: maintain data quality and consistency, which is essential for model accuracy. 4. Observability goes beyond metrics: With Elastic, we get a holistic view of our ML systems, including logs and traces. This helps us troubleshoot issues faster and understand the broader impact of our models. 5. Security can't be an afterthought: Elastic's security features have helped us implement robust access controls and ensure compliance with data governance policies. #MLOps #SiteReliabilityEngineering #Observability #AIOps https://lnkd.in/e_r8G6Tz

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