Machine Learning with Carlos Timoteo

Carleton University
Tue, Nov 9, 2021, 5:00 PM (EST)

About this event

Meet and greet with Carlos Timoteo, a Google Developer Expert, Data Scientist and Machine Learning Engineer, and a Google Developer Group organizer for the Ottawa and Montreal Chapter. 

Carlos will dive deep into machine learning and showcase many different applications for ML and AI within Google Cloud such as Time Series Forecasting and Vertex Forecasting. 

Time series forecasting is an important research area for machine learning (ML), particularly where accurate forecasting is critical, including several industries such as retail, supply chain, energy, finance, etc. For example, in the consumer goods domain, improving the accuracy of demand forecasting by 10-20% can reduce inventory by 5% and increase revenue by 2-3%. Current ML-based forecasting solutions are usually built by experts and require significant manual effort, including model construction, feature engineering, and hyper-parameter tuning. However, such expertise may not be broadly available, which can limit the benefits of applying ML towards time series forecasting challenges.

To address this, automated machine learning (AutoML) is an approach that makes ML more widely accessible by automating the process of creating ML models and has recently accelerated both ML research and the application of ML to real-world problems. For example, the initial work on neural architecture search enabled breakthroughs in computer vision, such as NasNet, AmoebaNet, and EfficientNet, and in natural language processing, such as Evolved Transformer. More recently, AutoML has also been applied to tabular data.

Today we introduce a scalable end-to-end AutoML solution for time series forecasting, which meets three key criteria:

Fully automated: The solution takes in data as input, and produces a servable TensorFlow model as output with no human intervention.
Generic: The solution works for most time series forecasting tasks and automatically searches for the best model configuration for each task.
High-quality: The produced models have competitive quality compared to those manually crafted for specific tasks.