The document discusses various AI platforms and considerations for selecting an AI platform. It provides details on hardware requirements, basic libraries, compilers, programming languages, and operating systems that AI platforms use. It then describes the functionalities AI platforms provide like image recognition, NLP, recommendations. Key considerations for selection include intended users, simplicity vs flexibility, availability of pre-trained models, scalability, and licensing. Popular platforms discussed are Google Cloud, AWS, Microsoft Azure, IBM Watson.

1. AI Platforms

2. AI Platforms stack
x86, ARM, CUDA, Mali, Adreno
Linux ( CentOS, Ubuntu, RedHat, SUSE, Debian),
Android, Windows, BSD, iOS, MacOS
LLVM,GCC, ICC, Rose, PGI, Lift …
cuBLAS, BLAS, MAGMA, ViennaCL, CLBlast, cuDNN, openBLAS,
clBLAS, libDNN, tinyDNN, ARM compute lib, libXMM etc
C++, Fortran, Java, Python, Byte code, Assembler
CUDA, MPI, OpenMP, TBB, OpenCL, StarPU
Tensorflow, Caffe, Torch, Theano, TensorRT, CNTK, OpenCV
Microsoft Azure, Google Cloud, XSEDE, Watson etc.
AI Platforms
Hardware
Basic libraries
Compilers
Parallel
programming
Languages
Operating
System

3. Why AI Platforms ?
● Provide tool kit to build intelligent applications
● Enabling developers to create business solutions
● Providing APIs for building machine learning
models
● Analyze data to add various features to application

4. AI Platforms provide a wide range of
functionalities
● Image recognition
● Natural language processing
● Voice recognition
● Recommendation systems
● Predictive analytics
● Computer vision
● Artificial neural networks
● Natural language generation

5. Key considerations for AI Platform selection
User of AI
platform?
Software Engineer, Data Scientist or anyone else ?
Each of them have different background and
different needs.
Data scientists have math and statistical
background, heavy users of visualization tools and
responsible for analyzing data and tuning models.
Software engineers are familiar with web and
mobile apps, concerned about integrating with rest
of the systems and data sources. They are more
concerned about retrieving model results, model
versioning etc.
Important to understand Users, their needs and
their usage behaviour for platform selection

6. Key considerations for AI Platform selection ..contd.
Simplicity v/s
Flexibility
For some users, an ideal platform is one that abstracts away
all data science details. They are not concerned about how the
AI model is built, or even which algorithm is being used behind
the scenes
For other users, an ideal platform is one that allows a
maximum level of flexibility. Many software engineers like to
build completely custom AI engines. freedom to tailor­make
and fine­tune every component is their style
Availability of Pre­
trained models
Typically, AI models require large data sets and
computational resources for training, whereas pretrained
models allow you to start from a “hot” model and only optimize
the model slightly for a new application, using significantly
less computation resources and a smaller data set.

7. Key considerations for AI Platform selection ..contd.
Other important considerations are
● Horizontal scalability
● Support for multiple CPUs
● Licensing model
● Developer and support ecosystem around the model
● Known large deployments for the model
● Benchmarks
● Speed of inference, forward propagation (faster means better for
production deployment and model application)
● Speed of training, gradient computation (faster means better for
model development and experimentation)
● Availability of debugging tools
● Availability of documentations etc.

8. AI Platforms
● Microsoft Azure Machine
Learning
● Google Cloud Prediction API
● IBM Watson
● Tensorflow
● Infosys Nia
● Wipro Holmes
● Api.ai
● Premonition
● Salesforce Einstein
● Rainbird
● Ayasdi
● Wit
● Vital.ai
● Mindmeld
● Kai
● Meya
● Receptivity

9. Google Cloud AI
Google Cloud's AI provides modern machine learning services, with
pre­trained models and a service to generate your own tailored
models.
Capabilities :
Video Analysis
Image Analysis
Speech recognition
Text Analysis
Dynamic Translation
Natural Language Processing
Vision support
Video intelligence
● Train custom machine learning
algorithms
● Large scale machine learning
service
● Job search and discovery­ Cloud
Jobs API
●

10. AWS AI/ML
Amazon is offering wide range of AI/ML tools and services. These
services are to cater to adding intelligence to any application and
supports majority of ML framework such as tensorflow, caffe, apache
etc.
Capabilities:
DeepLens
Deep Learning AMIs
Apache MXNet on AWS
Tensorflow on AWS
SageMaker
Comprehend
Lex
Polly
Recognition
Machine Learning
Translate
Transcribe

11. Microsoft Azure ML Platform
Azure ML—a production environment that simplifies the development
and deployment of machine learning models. The platform enables
growing community of developers and data scientists to share their
analytics solutions.
Microsoft offers a machine learning service and bot service on Azure.
It also has Cognitive Services that include a Bing Web Search API,
Text Analytics API, Face API, Computer Vision API and Custom
Vision Service.

12. IBM Watson
A platform for data exploration, visualization and predictive analytics.
The platform offers an interface through which data is deposited and
plain English questions are asked.
Capabilities:
● Human like conversation( to understand question and answer
appropriately)
● Discovery for the content and context
● Language analytics
● Language translation
● Document conversion
● Tone analysis
● Tradeoff analytics
● Text to speech and speech to next
● Retrieve and rank