The document discusses the role of unified AI and ML in cloud technologies, emphasizing the significance of choosing the right cloud service provider for data science applications. It highlights the benefits of cloud-based automated machine learning, particularly in relation to cost-effectiveness and proximity of compute resources to data. Additionally, it notes the importance of maintaining a good relationship with cloud providers for potential discounted services.

2. Role of Unified AI and ML in Cloud
Technologies. Which Cloud Service
Provider Better Supports Data Science?
Adnan Masood, PhD.
Chief AI Architect, UST

3. Confidential and Proprietary. © 2021 UST 3
UST introduction
Vision
Fast facts
1999
Founded
26,000+
Americas, Europe,
Asia-Pacific
42
Geographic Focus
Operating Centers
Employees
Industries covered
• Financial Services • Retail and CPG
• Tech Media and Telco • Healthcare
• Manufacturing
• Logistics and Transportation
• Energy and Utilities
Digital leader focusing on tangible outcomes through
meaningful innovation
Transforming lives
Mission
Value adds
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Commitment beyond contract, measuring our
success based on the tangible results we
achieve for our clients
Solutioning excellence:
Global network of partners, Innovation Centers and
CoEs that add meaningful value, creating and
deploying solutions that have direct business
impact
Talent and delivery excellence
Scaling at speed with hand picked right talent
and exceeding expectations in delivery with a
governance model to aim at before time delivery
Capabilities and solutions
Cybersecurity
IT strategy and execution
Customer experience
and agile
Data and
analytics
Innovation and
emerging solutions
Human-centered approach
based on empathy, followed
by digital design
Intelligent automation
Agile Transformation along
with distributed Prod Dev and
QA
Data Engineering Services
from Ingestion to
Consumption
Industry Experience with
Distributed Processing
systems like Spark
AI powered business use
cases in FS domain
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and automated remediation
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prioritization, minimizing
false positives
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autonomic healing
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and mapping tools
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efficiency
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delivery
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repeatable innovation
Emerging technologies (DLT,
QC, VR/AR) to address
business needs
35
Delivery Centers
7
Innovation Centers
UST
MultiCloud
Manager

4. 4
Dr. Adnan Masood
Adnan Masood, Ph.D. - Chief AI Architect at UST.
visiting Scholar at Stanford University, and Microsoft Regional
Director, and MVP (Most Valuable Professional) for AI.
Confidential and Proprietary. © 2021 UST Global Inc

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And the Oscar goes to ..
• If your data and compute resides in the cloud, it would make way more
sense to use the automated machine learning offered by that specific
cloud provider for all practical purposes. If you have a hybrid model, try to
keep your automated machine learning compute close to data you plan to
use, wherever it reside
• Generally speaking, cloud compute and storage resources for automated
machine learning can get expensive quickly , providing you are working
with large models and doing multiple simultaneous experiments. If you
have compute resources and data available in an on-prem scenario, that
could be an ideal playground without increasing your hyperscaler’s bill.
• This however comes with the responsibility of setting up the
infrastructure and do the setup for an on-prem toolkit. So if cost is not a
major concern and you want to quickly explore what automated machine
learning can do for you, cloud based toolkits would make an ideal
companion
• Your relationship with your cloud provider is important, you might be also
be able multi-service discounts.

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