
We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.

Explore the implementation of data capture for machine learning observability and drift detection in this 24-minute conference talk by Pushkar Garg. Dive into the complexities of modern ML systems, including data pipelines and transformations across mult…

Explore the innovative data architectures enabled by DuckDB, an in-process analytical data management system, in this 32-minute talk by Prof. Dr. Hannes Mühleisen. Discover how DuckDB's lightweight yet powerful design, available under the MIT license, al…

Explore how data lakehouse architecture with Apache Hudi supports real-world predictive ML and vector-based AI use cases in this 30-minute keynote by Vinoth Chandar, creator of Apache Hudi. Learn about ingesting data with minute-level freshness, providin…

Explore the concept of Full RAG (Retrieval-Augmented Generation) and its potential to revolutionize user experiences across industries in this 28-minute talk by Mike Del Balso, co-founder of Tecton. Examine four levels of context personalization, from ba…

Explore the Daft distributed Python data engine for multimodal data curation at any scale in this 27-minute talk by Jay Chia. Discover how Daft addresses the fundamental needs of ML/AI data platforms, including terabyte-scale ETL with complex model batch…

Explore reproducible data science techniques for data lakes in this 12-minute conference talk by Ciro Greco, presented at DE4AI. Delve into the challenges of achieving reproducibility in Lakehouse architectures and discover recent advancements made at Ba…

Explore the critical roles of data contracts and observability in ensuring data quality and reliability in this 14-minute talk by Mark Freeman. Gain insights into how these two approaches complement each other, with data contracts preventing known issues…

Explore the challenges and solutions in scaling data systems reliably in this 16-minute conference talk by Miriah Peterson at MLOps.community. Delve into the concept of Data Downtime and its impact on business outcomes. Learn how Data Reliability Enginee…

Explore common machine learning serving architectures in this 18-minute conference talk by Rebecca Taylor, tech lead of Personalization at Lidl e-commerce. Gain insights into the disconnect between academic teachings and industry practices in model deplo…

Explore the challenges and solutions in data engineering for optimizing the data science developer experience in this 12-minute talk by Aishwarya Joshi from Chime. Discover how to enable efficient feature engineering and deployment for low-latency infere…

Discover strategies for effective collaboration between data science and product teams in this 19-minute talk by Daniela Santisteban from MLOps.community. Learn how to leverage product management partnerships to ensure quality data ingestion and training…

Explore the evolution of Lyft's machine learning feature store in this 13-minute conference talk presented by Devon Mittow, a Staff Data Engineer with over a decade of experience in data engineering across various industries. Gain insights into how Lyft'…

Explore the intersection of AI and data platforms in this 13-minute conference talk by Dr. Shelby Heinecke, an AI research team leader at Salesforce. Delve into the critical operation of data unification and discover how small, efficient Large Language M…

Explore a comprehensive analysis of lakehouse design and its integration with AI in this insightful 13-minute talk by Simon Whiteley, a Databricks Beacon and Microsoft MVP. Delve into the challenges companies face when adopting lakehouses and learn how t…

Explore DuckDB's underrated search capabilities and its potential within an LLM stack in this 13-minute talk by Mehdi Ouazza, an experienced data engineer and developer relations lead at MotherDuck. Discover how DuckDB's versatile and efficient search me…

Build a real-time anomaly detection system using streaming data engineering techniques. Learn end-to-end implementation for monitoring and identifying unusual patterns in data streams.

Explore Avast's MLOps journey, tooling, and cultural shift for enhanced ML productivity. Learn strategies for model tracking, storage, orchestration, and E2E deployments in large-scale ML pipelines.

Discover how to build and scale ML services for health and wellness products, reducing release time by 85% using open-source tools like DBT, AirFlow, and MLFlow.

Explore a policy-centric approach to ML platforms, extending beyond model-centric views to enhance engineering productivity and directly improve business metrics.

Explore Dynatrace Davis AI's anomaly detection for data observability pillars: freshness, distribution, volume, schema, and lineage. Learn automation techniques for alerting and monitoring data integrity.