Value Quantification : PreModel Development Model Provisioning: Kubernetes Kibana Model Monitoring Cloud Computing Python/PySpark SAS/SPSS Great Expectation Evidently AI Deployment Strategies (A/B Blue green Canary) Model testing Tools(KubeFlow BentoML) Integration testing ML Frameworks (TensorFlow PyTorch SciKit Learn CNTK Keras MXNet) Value Quantification: PostModel Deployment Model Experimentation R/ R Studio
Job requirements
Drift Frame Work : Framework for detecting drift Automatically monitor track accuracy and trigger model retraining and notifications to restore previous accuracy levels ML Generalist: Data Scientist with MLOPS Development and maintenance of ML pipeline ML Engineer focusing on experimentation and tracking Responsibilities: Model Development: Develop machine learning models and algorithms to solve business problems leveraging techniques such as supervised learning unsupervised learning and deep learning. Deployment and Integration: Deploy machine learning models into production environments and integrate them with existing systems and workflows. Performance Optimization: Optimize machine learning models for scalability efficiency and performance considering factors such as latency throughput and resource utilization. Monitoring and Maintenance: Monitor model performance in production identify and diagnose issues and implement solutions to ensure continued reliability and effectiveness. Collaboration: Collaborate with crossfunctional teams including data scientists software engineers and product managers to understand business requirements and deliver solutions that meet stakeholders needs. Research and Innovation: Stay uptodate with the latest advancements in artificial intelligence and machine learning research and explore new techniques and methodologies to improve model performance and capabilities.
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