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Explore Quine: A streaming graph for modern data pipelines. Learn its inner workings, challenges faced during creation, and use cases illustrating its importance in data processing.
Explore how AI and NLP can enhance crime-solving, improve business efficiency, and create safer societies by automating information linking and reducing investigative time.
Explore managing large-scale spatiotemporal data fusion using the Geodesic Platform. Learn about cloud-native solutions, data ingest challenges, and leveraging Pachyderm for traceability and pipeline management.
Explore dataset management's crucial role in computer vision solutions. Learn to align business goals, express assumptions, and maintain datasets for successful real-world implementations.
Learn best practices for building ML data labeling pipelines through crowdsourcing. Explore real-life examples, requirements, and techniques for high-quality labeled data in supervised machine learning projects.
Explore data virtualization's role in accelerating AI/ML projects by streamlining data acquisition and preparation, integrating with popular data science tools, and efficiently handling large data volumes.
Learn to build real-time ML pipelines for fraud prediction, predictive maintenance, and customer churn prevention. Explore challenges, solutions, and best practices for handling high-velocity data and triggering actions in seconds.
Explore Azure Machine Learning for medical imaging applications, covering data management, model development, deployment, and explainability in healthcare scenarios.
Explore strategies to overcome imposter syndrome in STEM fields with expert panelists, focusing on building confidence and combating self-doubt in your professional journey.
Explore graph-native ML for enhanced predictions using relationship data. Learn graph feature engineering, ML techniques, and hands-on applications to leverage network structures in your data science models.
Explore the evolution of efficient AutoML systems, focusing on Auto-sklearn 2.0 and Auto-PyTorch. Learn about multi-fidelity optimization, portfolio construction, and automated policy selection for improved machine learning efficiency.
Learn to implement Git-based MLOps for continuous delivery, automating workflows, and enabling collaboration. Explore open-source tools and hosted ML platforms with a live demo on streamlining ML pipelines.
Explore interpretable machine learning for modeling drug effects on single-cell genomics. Learn about deep autoencoders, manifold identification, and predicting novel perturbations in cellular states.
Explore building an ethical data science practice, focusing on transparency, fairness, and explainability. Learn strategies to operationalize ethical AI and foster proactive risk management in your organization.
Learn to build robust data pipelines using dbt, Airflow, and Great Expectations. Discover how these tools complement each other to ensure data quality, perform transformations, and orchestrate workflows effectively.
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