MLOps Best Practices for Success

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

𝗠𝗟𝗢𝗽𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗺𝗲𝗱𝗶𝘂𝗺 𝗮𝗻𝗱 𝗹𝗮𝗿𝗴𝗲 𝘁𝗲𝗮𝗺𝘀 This MLOps extends the small MLOps architecture, and expands into multi-account setup with emphasis on the quality check of the model running in the production. In this architecture: 1. Data scientists adopt a multi-account approach in which they develop the model in the development account. 2. As in a small MLOps setup, data scientists start with the sage maker notebook and perform versioning of the code with code commit and versioning of the environment with ECR. Then they create a re-training pipeline using a step function, which has steps for model training, verification and artefact saving in S3. The versioning of the model is done by the sage maker model registry, which allows the user to accept or reject the model. 3. The deployment steps of the model include sage maker endpoints and autoscaling groups, which are connected to lambda and API gateway to allow users to submit inference requests. However, these component services sit in different AWS accounts. A multi-account strategy is recommended because it provides a way to separate business units, easily define restrictions for production loads and provide a fine-grained view of the cost incurred by each component of the architecture. 4. The multiple-account strategy involves setting up a staging account alongside the production account. A new model is first deployed to the staging account, tested and then deployed to the production account. This deployment must happen automatically via the code pipeline in the development account. The code pipeline is automatically triggered by the event generated when a model version is approved in the model registry. 5. It is imperative to monitor changes in the behaviour or accuracy of the models running in production. We have enabled data capture on the endpoints of the staging and production accounts. It captures the incoming requests and outgoing inference results in S3 buckets. This captured data usually needs to be combined with labels or other data on the development account, so we have used S3 replication to move the data onto an S3 bucket in the development account. Now to tell whether the behaviour of the model or data has changed, we need something to compare against. This is where the model baseline comes in. During the training process we can generate a baseline dataset which records the expected behaviour of the data and the model. We can use the sage maker model monitor to compare the two datasets and generate the report. 6. The final step in this architecture is to take action based on the model report. When a significant change is detected, we can send an event to trigger the re-training of the pipeline. #MLOpsExpansion #ProductionQuality #MultiAccountSetup #ModelManagement #MLArchitectures

There are 3 ingredients that pretty much guarantee the failure of any Machine Learning project: having the Data Scientists training models in notebooks, having the data teams siloed, and having no DevOps for the ML applications! Interestingly enough, that is where most companies trying out ML get stuck. The level of investment in ML infrastructures for companies is directly proportional to the level of impact they expect ML to have on the business. And the level of impact is, in turn, proportional to the level of investment. It is a vicious circle! Both Microsoft and Google established standards for MLOps maturity that capture the degree of automation of ML practices, and there is a lot to learn from those:  - Microsoft: https://lnkd.in/gtzDcNb9   - Google: https://lnkd.in/gA4bR77x Level 0 is the stage without any automation. Typically, the Data Scientists (or ML engineers, depending on the company) are completely disconnected from the other data teams. That is the guaranteed failure stage! It is possible for companies to pass through that stage to explore some ML opportunities, but if they stay stuck at that stage, ML is never going to contribute to the company's revenue. The level 1 is when there is a sense that ML applications are software applications. As a consequence, basic DevOps principles are applied at the software level in production, but there is a failure to realize the specificity of ML operations. In development, data pipelines are better established to streamline manual model development. At level 2, things get interesting! ML becomes significant enough for the business that we invest in reducing model development time and errors. Data teams work closer as model development is automated and experiments are tracked and reproducible. If ML becomes a large driver of revenue, level 3 is the minimum bar to strive for! That is where moving from development to deployment is a breeze. DevOps principles extend to ML pipelines, including testing the models and the data. Models are A/B tested in production, and monitoring is maturing. This allows for fast model iteration and scaling for the ML engineering team. Level 4 is FAANG maturity level! A level that most companies shouldn't compare themselves to. Because of ads, Google owes ~70% of its revenue to ML and ~95% for Meta, so a high level of maturity is required. Teams work together, recurring training happens at least daily, and everything is fully monitored. For any company to succeed in ML, teams should work closely together and aim for a high level of automation, removing the human element as a source of error. #MachineLearning #DataScience #ArtificialIntelligence -- 👉 Register for the ML Fundamentals Bootcamp: https://lnkd.in/gasbhQSk --