Data Science Courses
Comprehensive Data Science training programs for professionals
86 courses found • Page 5 of 8
5 days
Multiple Locations
Training Course on AI Ethics and Responsible Development
Training Course on AI Ethics & Responsible Development dives deep into the core principles of fairness, accountability, and transparency (FAT), equipping participants with the knowledge and practical tools to build trustworthy, equitable, and human-centric AI solutions
Training Course on AI Ethics & Responsible Development dives deep into the core principles of fairness, accountability, and transparency (FAT), equipping participants with the knowledge and practical tools to build trustworthy, equitable, and human-centric AI solutions
5 days
Multiple Locations
Training Course on Privacy-Preserving Machine Learning
Training Course on Privacy-Preserving Machine Learning delves into the cutting-edge methodologies that enable organizations to unlock the full potential of their data for advanced analytics and AI model development, all while ensuring the confidentiality, security, and ethical handling of sensitive information.
Training Course on Privacy-Preserving Machine Learning delves into the cutting-edge methodologies that enable organizations to unlock the full potential of their data for advanced analytics and AI model development, all while ensuring the confidentiality, security, and ethical handling of sensitive information.
5 days
Multiple Locations
Training Course on Explainable AI (XAI) and Model Interpretability
Training Course on Explainable AI (XAI) & Model Interpretability addresses this critical gap, equipping participants with the essential techniques to demystify complex AI systems, fostering greater confidence and responsible AI deployment.
Training Course on Explainable AI (XAI) & Model Interpretability addresses this critical gap, equipping participants with the essential techniques to demystify complex AI systems, fostering greater confidence and responsible AI deployment.
10 days
Accra, Addis Ababa, Bangkok, Cape Town, Dubai, Istanbul, Kigali, Nairobi
Training Course on Cost Optimization in MLOps
Training Course on Cost Optimization in MLOps: Managing cloud infrastructure and compute costs for ML workloads delves deep into the intersection of MLOps best practices and cloud financial management. Participants will gain actionable insights into identifying cost inefficiencies, implementing governance policies, and adopting a FinOps for ML mindset.
Training Course on Cost Optimization in MLOps: Managing cloud infrastructure and compute costs for ML workloads delves deep into the intersection of MLOps best practices and cloud financial management. Participants will gain actionable insights into identifying cost inefficiencies, implementing governance policies, and adopting a FinOps for ML mindset.
10 days
Multiple Locations
Training Course on Feature Store Design and Implementation
Training Course on Feature Store Design & Implementation: Centralizing and Serving Features for ML Models outlines a comprehensive training course on Feature Store Design & Implementation, a critical component of modern MLOps.
Training Course on Feature Store Design & Implementation: Centralizing and Serving Features for ML Models outlines a comprehensive training course on Feature Store Design & Implementation, a critical component of modern MLOps.
10 days
Multiple Locations
Training Course on MLOps for Real-time Inference
Training Course on MLOps for Real-time Inference: Optimizing Models for Low-Latency Predictions is meticulously designed to equip professionals with the cutting-edge skills and practical expertise needed to deploy, manage, and optimize machine learning models for low-latency predictions in production environments.
Training Course on MLOps for Real-time Inference: Optimizing Models for Low-Latency Predictions is meticulously designed to equip professionals with the cutting-edge skills and practical expertise needed to deploy, manage, and optimize machine learning models for low-latency predictions in production environments.
10 days
Multiple Locations
Training Course on Data Versioning and Experiment Tracking for ML
Training Course on Data Versioning & Experiment Tracking for ML: Tools like DVC, MLflow for Reproducible Research delves into the critical practices of data versioning and experiment tracking, empowering data scientists, ML engineers, and researchers to effectively manage their ML workflows.
Training Course on Data Versioning & Experiment Tracking for ML: Tools like DVC, MLflow for Reproducible Research delves into the critical practices of data versioning and experiment tracking, empowering data scientists, ML engineers, and researchers to effectively manage their ML workflows.
10 days
Multiple Locations
Training Course on Cloud MLOps on AWS (SageMaker Advanced)
Training Course on Cloud MLOps on AWS (SageMaker Advanced): Deep Dive into AWS Services for MLOps provides a comprehensive, hands-on deep dive into Cloud MLOps principles and practices, specifically leveraging advanced AWS services, with a strong focus on Amazon SageMaker
Training Course on Cloud MLOps on AWS (SageMaker Advanced): Deep Dive into AWS Services for MLOps provides a comprehensive, hands-on deep dive into Cloud MLOps principles and practices, specifically leveraging advanced AWS services, with a strong focus on Amazon SageMaker
10 days
Multiple Locations
Training Course on Scalable ML Serving with Kubeflow/Sagemaker
Training Course on Scalable ML Serving with Kubeflow/Sagemaker: Deploying and Scaling Models in Cloud Environments focuses on empowering data scientists, ML engineers, and DevOps professionals with the essential skills to deploy and scale machine learning models efficiently in cloud environments.
Training Course on Scalable ML Serving with Kubeflow/Sagemaker: Deploying and Scaling Models in Cloud Environments focuses on empowering data scientists, ML engineers, and DevOps professionals with the essential skills to deploy and scale machine learning models efficiently in cloud environments.
10 days
Multiple Locations
Training Course on Cloud MLOps on Azure (Azure ML Advanced)
Training Course on Cloud MLOps on Azure (Azure ML Advanced): Deep dive into Azure services for MLOps. is meticulously designed to equip professionals with the cutting-edge skills and best practices required to streamline the entire Machine Learning Lifecycle on Microsoft Azure.
Training Course on Cloud MLOps on Azure (Azure ML Advanced): Deep dive into Azure services for MLOps. is meticulously designed to equip professionals with the cutting-edge skills and best practices required to streamline the entire Machine Learning Lifecycle on Microsoft Azure.
10 days
Multiple Locations
Training Course on Cloud MLOps on GCP (Vertex AI Advanced)
Training Course on Cloud MLOps on GCP (Vertex AI Advanced): Deep Dive into GCP Services for MLOps provides a deep dive into Cloud MLOps on Google Cloud Platform (GCP), specifically leveraging Vertex AI Advanced capabilities. Participants will gain hands-on expertise in building, deploying, monitoring, and managing robust Machine Learning (ML) pipelines in a production environment.
Training Course on Cloud MLOps on GCP (Vertex AI Advanced): Deep Dive into GCP Services for MLOps provides a deep dive into Cloud MLOps on Google Cloud Platform (GCP), specifically leveraging Vertex AI Advanced capabilities. Participants will gain hands-on expertise in building, deploying, monitoring, and managing robust Machine Learning (ML) pipelines in a production environment.
10 days
Multiple Locations
Training Course on A/B Testing and Experimentation for ML Models
Training Course on A/B Testing & Experimentation for ML Models: Designing and Analyzing Online Experiments equips participants with the statistical rigor, experimental design principles, and practical tools necessary to confidently launch, analyze, and iterate on ML model deployments, moving beyond offline metrics to live production validation.
Training Course on A/B Testing & Experimentation for ML Models: Designing and Analyzing Online Experiments equips participants with the statistical rigor, experimental design principles, and practical tools necessary to confidently launch, analyze, and iterate on ML model deployments, moving beyond offline metrics to live production validation.