ML Observability for Mission-Critical Use Cases
Explore key components and challenges of ML Observability for critical AI deployments, including explainability, monitoring, and risk mitigation. Learn from real-world case studies.
Deep Dive into ChatGPT: Architecture and Comparison with Bard and LLaMa
Explore ChatGPT's architecture, implementation, and evolution. Compare with Bard and LLaMa. Learn to build tinyGPT. Gain insights into transformers, attention mechanisms, and the GPT family of models.
Multimodal Generative AI: Technology Overview and Business Implications
Explore multimodal generative AI's technology, business applications, and limitations. Gain insights into training, costs, and open-source systems like LLaVA for text, image, and audio processing.
Deploying Deep Learning Models for Inference at Production Scale
Optimize and deploy AI models at scale using NVIDIA TensorRT and Triton Inference Server. Learn production-ready techniques for efficient inference across various platforms and infrastructures.
Productionising Data Science
Explore techniques for creating data pipelines, productionizing data science, and operationalizing machine learning with insights from experienced engineers.
Mission to Maturity: Deploying MLOps to Scale Machine Learning
Explore MLOps deployment for scaling machine learning at M&S, covering personalization and enterprise functions. Learn about ML maturity, testing, and monitoring for production-ready systems.
Advanced Techniques for Online Advertisement Spend Optimization
Explore advanced techniques for optimizing online ad spend using Reinforcement Learning, Bayesian statistics, and Thompson sampling to improve budget allocation and maximize returns.
From Zero to SOTA: Fine-Tuning BERT Using Hugging Face for World-Class Training Performance
Discover how to fine-tune BERT using Hugging Face and Graphcore's IPU for state-of-the-art NLP performance. Learn to accelerate AI development, optimize models, and leverage cloud platforms for efficient machine learning workflows.
Seeing the Unseen - AI, Ethics and the Future of Transparency
Explore AI ethics, transparency, and societal impact with expert panelists. Discuss emerging dilemmas, legislation, and user experiences in AI development and implementation.
To See or Not to See - Is AI at Its Best When People Know It's There?
Explore the ethical implications of AI transparency in online services, examining strategies from Reddit's open-source approach to Google's partial disclosure and their impact on user trust and engagement.
From Zero to Data Hero - Take Raw Data to Curated Data in Under 90 Minutes
Discover how to efficiently manage, process, and clean data using low-code tools. Learn to connect Apache Spark, Databricks, and TimeXtender for rapid data curation and confident decision-making.
Introduction to Machine Learning - Clustering - Part 3
Explore clustering in machine learning, focusing on k-means and hierarchical algorithms. Learn to uncover data relationships and apply techniques with practical considerations.
Introduction to Neural Networks
Explore neural networks, their components, and architectures. Learn about RNNs, LSTMs, and CNNs with practical examples in Python for movie review and image classification tasks.
Introduction to Python Libraries and Algorithms for Machine Learning
Explore Python libraries and algorithms for machine learning, covering regression, clustering, and classification using popular tools like Pandas.
How to Get Value Out of ChatGPT and Stable Diffusion Models with Proprietary Data
Explore applications of language models and generative AI in corporate settings, boosting productivity and customer experience while preserving proprietary data.
Advanced Python Libraries for Data Science
Explore advanced Python libraries for data science, focusing on scikit-learn, TensorFlow, and PyTorch Geometric. Learn implementation, methodologies, and practical applications through code exercises.
Advanced SQL Techniques for Data Science
Expand SQL knowledge with complex conditions, functions, joins, and data engineering concepts. Learn from Facebook's Data Engineering Manager in this advanced session.
How to Deploy Machine Learning Models in Production
Learn to train, deploy, and maintain machine learning models in production environments, covering real-time decision-making and scalable frameworks for experimentation and deployment.
Your First Data Science Project: Planning for Success
Learn effective project management techniques for data science, covering agile and waterfall methodologies, autoML, model monitoring, and data drift to ensure successful implementation of data-driven initiatives.
To Buy or Not to Buy: Predicting Shopper Behavior in Grocery Retail
Explore demand modeling in grocery retail: forecasting sales, diagnosing performance, and optimizing pricing strategies. Learn key requirements, challenges, and solutions in this high-level overview.
From Code to Compliance - An Overview of AI Regulations for Data Practitioners
Explore AI regulations, standards, and responsible development practices to drive organizational value and foster innovation in the evolving regulatory landscape.