MLOps and AIOps with a Real Life Case Study P2 скачать в хорошем качестве

MLOps and AIOps with a Real Life Case Study P2

#mlops #aiops MLOps and AIOps with a Real-Life Case Study – How One Company Can Benefit Part 2 Defining scope for clarity at the start: 1. Define scope: Detect elements such as garbage, broken signals, construction on roads, etc. 2. Define the task: Evaluate solving the problem using object detection or classification 3. Define metrics: Choose the best way to evaluate your models, such as accuracy, precision, latency, or throughput 4. Estimate resources and the timeline for the project. Data Collection and Annotation: 1. Accurate data collection: Data should resemble real-time scenarios Annotating data at scale: Large volume of data can be challenging to annotate 2. Label consistency: Ensure labels are consistent throughout the process Experiment Tracking and Performance Improvement: Experiment tracking: 1. Keep track of experiments to improve accuracy 2. Improving model performance: Experiment to take the model accuracy from a 66% baseline to 82% with a data-centric approach. Check out the approach here: https://wandb.ai/buntyshah/Data%20Cet... Containerization, Deployment, and Versioning: 1. Containerization: Use tools like Docker to make the model portable and easy to deploy 2. Deployment: Deploy the containerized model to a cluster like Kubernetes for scaling and parallel processing 3. Versioning: Use Git or similar version control system to keep track of different model versions and dependencies Automated Testing and Continuous Integration and Delivery: 1. Automated testing: Use frameworks like TensorFlow Lite Micro to test the model and ensure its continued functionality 2. Continuous Integration and Delivery: Use CI/CD tools like Jenkins or GitLab CI to automate the build, test, and deployment process for quick and easy model deployment to production Management and Governance: 1. Management and Governance: Use TensorFlow Extended (TFX) to manage and govern the entire MLOps pipeline, providing a centralized interface for monitoring, versioning, and deploying models 2. Monitoring and Maintenance: Continuously monitor and maintain the system for optimal performance Join us on our journey to explore the exciting world of MLOps and AIOps. Like, share and subscribe to our channel    / @mlopshub   for more updates and insights. #mlops #aiops #mlopswithcasestudy #ml #ai #tfx #datascience #machinelearning #machinelearningengineer #machinelearningbasics #machinelearningbasics [You may ignore below] #blockchain #Quantumcomputing #quantumcryptography #onlinestudy #lockdownstudy #cloudcomputing #english #hindi #cryptocurrency #trading #newchannel #odia #odiatoka #odisha #symbiosis #quantum #minning #informationtechnology #technology #IT #newtechnology #revolution #disruption #advance #advancement #Brave #bitcoin #ethereum #cryptotrading #informationtechnology #bank #transaction #klub #Peer #AbhasDash #DMZ #demiliterzedzone #darkweb #deepweb #tor #onionbrowser #torbrowser #silkroad #COBIT #framework #machinelearning #supervisedlearning #artificialintelligence #kinetoclub #kinetoklub