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AI, ML and Data Engineering
Videos
1 — Databases and Stream Processing: a Future of Consolidation
2 — Accuracy as a Failure
3 — BERT for Sentiment Analysis on Sustainability Reporting
4 — The Fast Track to AI with JavaScript and Serverless
5 — Applying Machine Learning to Financial Payments
6 — Machine Learning through Streaming at Lyft
7 — Monitoring and Tracing @Netflix Streaming Data Infrastructure
8 — Swift for Tensorflow
9 — Kafka Needs No Keeper
10 — Data Mesh Paradigm Shift in Data Platform Architecture
11 — ML's Hidden Tasks: A Checklist for Developers When Building ML Systems
12 — Machine Learning 101
13 — Intuition & Use-Cases of Embeddings in NLP & beyond
14 — Not Sold Yet, GraphQL: A Humble Tale from Skeptic to Enthusiast
15 — Future of Data Engineering
16 — The Future of Transportation
17 — MLflow: An Open Platform to Simplify the Machine Learning Lifecycle
18 — Test-Driven Machine Learning
19 — EBtree - Design for a Scheduler and Use (Almost) Everywhere
20 — The Whys and Hows of Database Streaming
21 — Algorithms behind Modern Storage Systems
22 — Building the Enchanted Land
23 — Engineering Systems for Real-Time Predictions @DoorDash
24 — ETL Is Dead, Long Live Streams: real-time streams w/ Apache Kafka
25 — Michelangelo - Machine Learning @Uber
26 — Semi-Supervised Deep Learning on Large Scale Climate Models
27 — Artificial Intelligence and Machine Learning for the SWE
28 — Bias in BigData/AI and ML
29 — Serverless & GraphQL
30 — Panel: SQL over Streams, Ask the Experts
31 — Artificial Intelligence and Machine Learning for the SWE - QCon London 2018
32 — Fundamentals of Stream Processing with Apache Beam
33 — ML Data Pipelines for Real-Time Fraud Prevention @PayPal
34 — Human-Centric Machine Learning Infrastructure @Netflix
35 — Open Source Robotics: Hands on with Gazebo and ROS 2
36 — From Research to Production with PyTorch
37 — Practical Change Data Streaming Use Cases with Apache Kafka & Debezium
38 — ML in the Browser: Interactive Experiences with Tensorflow.js
39 — Machine Learning on Mobile and Edge Devices with TensorFlow Lite
40 — Visual Intro to Machine Learning and Deep Learning
41 — Anti-Entropy Using CRDTs on HA Datastores @Netflix
42 — Taming the Data Mess, How Not to Be Overwhelmed by the Data Landscape
43 — Building & Operating High-Fidelity Data Streams
44 — Federated GraphQL to Solve Service Sprawl at Major League Baseball
45 — Data Pipelines & Data Mesh: Where We Are and What the Future Looks Like
46 — Panel: Future of Language Support for ML
47 — How Do You Distribute Your Database over Hundreds of Edge Locations?
48 — Robust Foundation for Data Pipelines at Scale - Lessons from Netflix
49 — Evolving Analytics in the Data Platform
50 — Designing Better ML Systems: Learnings from Netflix
51 — From Batch to Streams: Building Value from Data In-Motion
52 — Data-Driven Development in the Automotive Field
53 — Data Mesh: an Architectural Deep Dive
54 — Designing IoT Data Pipelines for Deep Observability
55 — Scaling & Optimizing the Training of Predictive Models
