1. VP AIOps for the Autonomous Database
Sandesh Rao
LAOUC
Machine Learning and AI at Oracle
@sandeshr
https://www.linkedin.com/in/raosandesh/
https://www.slideshare.net/SandeshRao4
2. Types of Machine Learning
Supervised Learning
Predict future outcomes with the help of training
data provided by human experts
Semi-Supervised Learning
Discover patterns within raw data and make
predictions, which are then reviewed by human
experts, who provide feedback which is used to
improve the model accuracy
Unsupervised Learning
Find patterns without any external input other
than the raw data
Reinforcement Learning
Take decisions based on past rewards for this type
of action
3. ML Project Workflow
Set the business objectives
Gather compare and
clean data
Identify and extract features
(important columns) from imported data
This helps us identify the efficiency of the
algorithm
Take the input data which is also called the training data
and apply the algorithm to it
In order for the algorithm to function efficiently, it is
important to pick the right value for hyper parameters
(input parameters to the algorithm)
Once the training data in the
algorithm are combined we
get a model
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5. Does not replace data scientists but
rather expediate their capabilities
Does AutoML remove the need for Data Scientists?
At the advent of the assembly line in manufacturing,
many tedious processes were automated.
This enabled workers to put their time and energy into
bigger issues, from quality of product to improving design
and manufacturing processes.
AutoML gives similar power to data scientists,
delivering more time to engineer predictive
features, develop data acquisition strategies,
improve the data transformation pipelines, and
more.
33. Performs text analysis at scale
Understand unstructured text in documents e.g.:
• Customer feedback interactions
• Support tickets
• Social media
Built-in pre-trained models eliminates the need for machine learning expertise
Empowers developers to apply:
• Sentiment analysis
• Key-phrase extraction
• Text classification
• Named entity recognition
• + more
OCI Language
34. Provides automatic speech recognition
Real-time speech recognition using prebuilt models
Trained on thousands of native and non-native language speakers
Enables developers to easily:
• Convert file-based audio data containing human speech into highly
accurate text transcriptions
• Provide in-workflow closed captions
• Index content
• Enhance analytics on audio and video content
OCI Speech
35. Provides pre-trained computer vision models
Perform image recognition and document analysis tasks
Extend the models to other use cases e.g.:
• Scene monitoring
• Defect detection
• Document processing
Detect visual anomalies in manufacturing
Extract text from forms to automate business workflows
Tag items in images to count products or shipments
OCI Vision
36. Business-specific anomaly detection models
Flag critical irregularities early, which enables:
• Faster resolution
• Fewer operational disruption
Provides REST APIs and SDKs for several programming languages
Built on the patented MSET2 algorithm, which is used worldwide e.g.:
• Nuclear reactor health monitoring
• Fraud detection
• Predicting equipment breakdown
• Receiving data from multiple devices to predict failures
OCI Anomaly Detection
37. Delivers time-series forecasts
No need for data science expertise
Helps developers to quickly create accurate forecasts including:
• Product demand
• Revenue
• Resource requirements
Forecasts all have confidence intervals and explainability to help developers
make the right business decisions
OCI Forecasting
38. Helps users build labeled datasets to train AI models
Via user interfaces and public APIs, users can:
• Assemble data
• Create and browse datasets
• Apply labels to data records
The labeled data sets can be exported and used for model development
across many of Oracle’s AI and data science services
OCI Data Labeling