2. MACHINE LEARNING PLATFORM ARCHITECTURE
Models: AKS
Batch predictions: SQL DB
Analytics
Power BI
Synapse
Business Apps
Responsible ML tools
Seamless studio experience
Notebooks Designer
Comprehensive MLOps
Unified management across clouds and on-premises
Serverless
Compute
Managed
Kubernetes
Azure Edge & Hybrid
Azure Arc-enabled
Kubernetes
Edge/IoT Devices
Reproducibility Automation Deployment Re-training
Security and Governance
Automated ML
Azure ML
Structured Data
Unstructured Data
All other ML scenarios;
NLP, Vision, IoT, etc.
Open
Datasets
Prepare Data Build & Train Manage & Monitor
Deploy
Data
Bricks
3. Generative AI
Prompt:
Write a tagline for an ice cream shop.
Response:
We serve up smiles with every scoop!
Prompt:
Table customers, columns = [CustomerId,
FirstName, LastName, Company, Address,
City, State, Country, PostalCode]
Create a SQL query for all customers in
Texas named Jane
query =
Response:
SELECT *
FROM customers
WHERE State = 'TX' AND FirstName =
'Jane'
Prompt: A white Siamese cat
Response:
GPT-3 Codex DALL·E
4. Inferencing
timeCost
Capability
Ada
• Simple classification
• Parsing and formatting text
Curie
• Answering questions
• Complex, nuanced classification
Davinci
• Summarizing for
specific audience
• Generating creative content
Babbage
• Semantic search ranking
• Moderately complex classification
Azure OpenAI Service models
Cushman-codex
Davinci-codex
Capability
Codex
GPT-3
AZURE ML can help us implement a Responsible MLOps process, for our entire ML lifecycle regardless of where our compute is running.
With built-in integration with Azure DevOps, developers can ensure model reproducibility, validation, deployment, and retraining.
Here, we can see that the platform architecture includes tools and services across the lifecycle – from data preparation to utilization that can be realized through deep integration with other Azure Data Services.
When the key components of a platform are Data, Model Building, Model Governance and Model Operations.
Actually, Azure Machine Learning Platform empowers data scientists and developers with a wide range of capabilities to help with building, training, and deployment of machine learning models securely and at scale.
When GPT-3 is for text completion, and it is a set of models that can understand and generate natural language.
it can also do interesting things with Classification and summarization, and we will get to some of these examples later.
The second is Codex that is actually a descendant of GPT-3 that can understand and generate code, including translating natural language to code.
And there is a lot of interesting innovation in this space, for example take a given code and translate it to my SQL instead of SQL.
And DALL-E that is different from GPT 3 and Codex that can generate or manipulate images from natural language.
And this becomes an interesting use especially for generating images on the fly.
So instead of spending an hour to find prefect image for our power point presentation for example, we can generate image and do manipulation of images by providing further instructions for example to change the background.
