Energy Forecasting Pipelines

Data Skeptic · Intermediate ·🏭 MLOps & LLMOps ·4y ago

Key Takeaways

The video discusses energy forecasting pipelines with Erin Boyle, Head of Data Science at Myst AI, focusing on time series forecasting for sustainability using Myst AI's platform and services like Weights and Biases.

Original Description

Erin Boyle, the Head of Data Science at Myst AI, joins us today to talk about her work with Myst AI, a time series forecasting platform and service with the objective for positively impacting sustainability. https://docs.myst.ai/docs Visit Weights and Biases at wandb.me/dataskeptic Find Better Data Faster with Nomad Data. Visit nomad-data.com
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Data Skeptic · Data Skeptic · 0 of 60

← Previous Next →
1 Data Skeptic book giveaway contest winner selection
Data Skeptic book giveaway contest winner selection
Data Skeptic
2 OpenHouse - Front end and API overview
OpenHouse - Front end and API overview
Data Skeptic
3 OpenHouse Crawling with AWS Lambda
OpenHouse Crawling with AWS Lambda
Data Skeptic
4 [MINI] Logistic Regression on Audio Data
[MINI] Logistic Regression on Audio Data
Data Skeptic
5 Data Provenance and Reproducibility with Pachyderm
Data Provenance and Reproducibility with Pachyderm
Data Skeptic
6 [MINI] Primer on Deep Learning
[MINI] Primer on Deep Learning
Data Skeptic
7 Big Data Tools and Trends
Big Data Tools and Trends
Data Skeptic
8 [MINI] Automated Feature Engineering
[MINI] Automated Feature Engineering
Data Skeptic
9 The Data Refuge Project
The Data Refuge Project
Data Skeptic
10 [MINI] The Perceptron
[MINI] The Perceptron
Data Skeptic
11 [MINI] Feed Forward Neural Networks
[MINI] Feed Forward Neural Networks
Data Skeptic
12 Data Science at Patreon
Data Science at Patreon
Data Skeptic
13 [MINI] Backpropagation
[MINI] Backpropagation
Data Skeptic
14 [MINI] GPU CPU
[MINI] GPU CPU
Data Skeptic
15 OpenHouse
OpenHouse
Data Skeptic
16 [MINI] Generative Adversarial Networks
[MINI] Generative Adversarial Networks
Data Skeptic
17 [MINI] AdaBoost
[MINI] AdaBoost
Data Skeptic
18 [MINI] The Bootstrap
[MINI] The Bootstrap
Data Skeptic
19 [MINI] Dropout
[MINI] Dropout
Data Skeptic
20 [MINI] Gini Coefficients
[MINI] Gini Coefficients
Data Skeptic
21 [MINI] Random Forest
[MINI] Random Forest
Data Skeptic
22 [MINI] Heteroskedasticity
[MINI] Heteroskedasticity
Data Skeptic
23 [MINI] ANOVA
[MINI] ANOVA
Data Skeptic
24 Urban Congestion
Urban Congestion
Data Skeptic
25 [MINI] The CAP Theorem
[MINI] The CAP Theorem
Data Skeptic
26 Unstructured Data for Finance
Unstructured Data for Finance
Data Skeptic
27 Detecting Terrorists with Facial Recognition?
Detecting Terrorists with Facial Recognition?
Data Skeptic
28 Predictive Models on Random Data
Predictive Models on Random Data
Data Skeptic
29 [MINI] Entropy
[MINI] Entropy
Data Skeptic
30 [MINI] F1 Score
[MINI] F1 Score
Data Skeptic
31 Causal Impact
Causal Impact
Data Skeptic
32 Machine Learning on Images with Noisy Human-centric Labels
Machine Learning on Images with Noisy Human-centric Labels
Data Skeptic
33 The Library Problem
The Library Problem
Data Skeptic
34 Stealing Models from the Cloud
Stealing Models from the Cloud
Data Skeptic
35 Data Science at eHarmony
Data Science at eHarmony
Data Skeptic
36 Multiple Comparisons and Conversion Optimization
Multiple Comparisons and Conversion Optimization
Data Skeptic
37 Election Predictions
Election Predictions
Data Skeptic
38 [MINI] Calculating Feature Importance
[MINI] Calculating Feature Importance
Data Skeptic
39 MS Connect Conference
MS Connect Conference
Data Skeptic
40 Music21
Music21
Data Skeptic
41 The Police Data and the Data Driven Justice Initiatives
The Police Data and the Data Driven Justice Initiatives
Data Skeptic
42 Studying Competition and Gender Through Chess
Studying Competition and Gender Through Chess
Data Skeptic
43 [MINI] Goodhart's Law
[MINI] Goodhart's Law
Data Skeptic
44 Trusting Machine Learning Models with LIME
Trusting Machine Learning Models with LIME
Data Skeptic
45 [MINI] Leakage
[MINI] Leakage
Data Skeptic
46 Predictive Policing
Predictive Policing
Data Skeptic
47 Mutli-Agent Diverse Generative Adversarial Networks
Mutli-Agent Diverse Generative Adversarial Networks
Data Skeptic
48 [MINI] Convolutional Neural Networks
[MINI] Convolutional Neural Networks
Data Skeptic
49 Unsupervised Depth Perception
Unsupervised Depth Perception
Data Skeptic
50 [MINI] Max-pooling
[MINI] Max-pooling
Data Skeptic
51 MS Build 2017
MS Build 2017
Data Skeptic
52 Activation Functions
Activation Functions
Data Skeptic
53 Doctor AI
Doctor AI
Data Skeptic
54 [MINI] The Vanishing Gradient
[MINI] The Vanishing Gradient
Data Skeptic
55 CosmosDB
CosmosDB
Data Skeptic
56 Estimating Sheep Pain with Facial Recognition
Estimating Sheep Pain with Facial Recognition
Data Skeptic
57 [MINI] Conditional Independence
[MINI] Conditional Independence
Data Skeptic
58 MINI: Bayesian Belief Networks
MINI: Bayesian Belief Networks
Data Skeptic
59 Project Common Voice
Project Common Voice
Data Skeptic
60 [MINI] Recurrent Neural Networks
[MINI] Recurrent Neural Networks
Data Skeptic

This video teaches how to build and optimize energy forecasting pipelines using time series forecasting techniques and platforms like Myst AI, with a focus on sustainability and AI safety. It matters because accurate forecasting can significantly impact energy efficiency and reduce waste. By understanding these pipelines, viewers can apply similar techniques to other domains.

Key Takeaways
  1. Explore Myst AI's time series forecasting platform
  2. Learn about Weights and Biases for model tracking
  3. Understand the importance of sustainability in AI applications
  4. Design a basic forecasting pipeline
  5. Optimize the pipeline for better performance
💡 Accurate energy forecasting can have a significant positive impact on sustainability by optimizing energy usage and reducing waste, making it a critical application of AI for social good.

Related Reads

📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixing, increasing model reliability and reducing downtime
Medium · Machine Learning
📰
Building a self-healing MLOps pipeline on AWS: from raw data to a model that fixes itself
Learn to build a self-healing MLOps pipeline on AWS that automates model fixes, increasing model reliability and reducing downtime
Medium · DevOps
📰
qModel Open-Source Platform v1.2.0 Released: Streamlined Python Model Integration & Execution Pipeline
Learn how to streamline Python model integration and execution with qModel Open-Source Platform v1.2.0, a tool for MLOps and AI development
Dev.to AI
📰
Inference Infrastructure Best Practices for High-Traffic AI Applications
Learn best practices for building scalable inference infrastructure for high-traffic AI applications to ensure reliable and efficient deployment
Dev.to AI
Up next
Pole Pruner How A Rope Lever Shears High Branches
Innoforge Studio
Watch →