The key challenge in making AI technology more accessible to the broader community is the scarcity of AI experts. Most businesses simply don’t have the much needed resources or skills for modeling and engineering. This is why automated machine learning and deep learning technologies (AutoML and AutoDL) are increasingly valued by academics and industry. The core of AI is the model design. Automated machine learning technology reduces the barriers to AI application, enabling developers with no AI expertise to independently and easily develop and deploy AI models. Automated machine learning is expected to completely overturn the AI industry in the next few years, making AI ubiquitous.
2. Ning Jiang
Co-founder and CTO of OneClick.ai.
Previously Dev Manager at Microsoft
Bing, Ning has over 17 years of R&D
experience in AI for ads relevance,
local search, and cyber security.
17. Cross Features
Textual cross
features
● Text similarity
● N-gram set relations
● Word embedding diff.
● Substrings
● Fuzzy match
● ...
Numeric cross
features
● a - b
● |a - b|
● a > b
● a * b
● a / b
● (a - b)**2
● ...
18. Feature Selection
Feature Selection
Stepwise Regression
Feature Importance
Random Projection
Locality-Sensitive Hashing
Random Projection
Linear Projection
PCA
LDA
Non-linear Projection
Auto-Encoder
GDA
19. Model Selection & Tuning
Model Selection
● Brute force
Hyperparameter
Tuning
● Grid search
● Random search
● Bayes Optimization
21. Other Perspectives of AutoML
Modeling
which we just
covered :-)
Data Imports
File formats, data
bases, Hadoo,
clouds, NFS
Deployment
API serving, live updates, A/B
testing, batch serving, scalability
and failure recovery.
23. Data Cleansing Encoding Model Architecture & Training
Missing values
Data types
Anomalies
Text encoding
Partitioning
Scalar
Sequence
Tensors
Loss
Deep Learning Framework
26. Model Architecture
Validation setTraining set
Average loss on the validation set
Neural Architecture Search (NAS)
Controller
Updating model
architectures in response
to the validation feedback
Loss
Training
On the training
set
Validation
On the validation
set
27. Controller
Prune
Stop developing less
promising branches
Generate
Enumerate model
architectures on a
predefined search
space
Reinforcement
Use Policy Gradient to update
RL models and stochastic
sampling for model
instantiation
28. AutoDL Players
Less restrictive on input data
Microsoft
Custom
Vision
Adaptive to different
applications
Application-specific
29. Challenges
1. Solutions are application-specific
2. New solutions for new applications
3. Heavily depends on human knowledge on the application
4. Assumes a linear architecture with skip connections
5. Cold start
6. Slow to converge
31. Data Cleansing Feature Extraction Feature Selection Model Selection & Tuning
Missing values
Data types
Anomalies
Encoding
Data partition
Numeric
Discrete
Textual/Image
Time-series
Linear proj.
N/L proj.
Reduction
Selection
Hyperparameters
Training
Cross-features
Machine Learning Framework
32. Data Cleansing Feature Extraction Feature
Selection
Model Selection &
tuning
ML Model
Converted to DAG
33. Data Cleansing Encoding Model Architecture & Training
Missing values
Data types
Anomalies
Text encoding
Partitioning
Scalar
Sequence
Tensors
Loss
Deep Learning Framework
35. Individual losses on the validation set
The Elias Engine
Validation set
Model Architecture Validation setTraining set
Controller
Updatig model
architectures in response to
the validation feedback
Loss
Training
On the training
set
Validation
On the validation
set
36. Where it stands out?
1. Works with both Deep Learning and traditional Machine Learning
2. Learning arbitrary DAGs
3. feature extraction coordinating with model architecture/selection
4. Shared Controller RL models to avoid cold start
5. Fewer models to train (20-30 models vs. thousands)
37. Automated Feature
Engineering
Automated Model
Selection & Tuning
Automated ML/DL Engine Elias
#US patent pending#
Time-series Forecasting
Deep Learning helps find more
complex patterns in and between time
series than any data scientists
Unstructured Data
Supports numeric and categorical
data, text, images, time-series, and
any mix of them
Performance
Custom DL models
often lead to better
performance
Functions
Algorithms
Versatility &
Performance
Automated Neural Architecture
Search
OneClick.ai Platform
38. Use AI to Build AI
Developed world’s first
automated DL engine
OneClick.ai
Incorporated in Belelvue, WA
Early 2017
Seeds Round
led by Sinovation
2017/4 2018/7
OneClick.ai on AWS
Public beta launched
2017/11
OneClick.ai Enterprise
On-premise deployment for
enhanced privacy and data security
Roadmap
Open to Public
One step closer to our goal of
making AI accessible to everyone
2018/8
41. Thank You
Free Sign-up
Scan to get double tokens for a
limited time, or sign up at
http://www.oneclick.ai using
promo code AUTOML4K
Follow us on Twitter
http://twitter.com/oneclickai
