Data Science in the cloud
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43.9. Data Science in the cloud#
43.9.1. 1. What is the cloud?#
The Cloud, or Cloud Computing, is the delivery of a wide range of pay-as-you-go computing services hosted on infrastructure over the internet. Services include solutions such as storage, databases, networking, software, analytics, and intelligent services.
Infrastructure as a Service (IaaS)
Platform as a Service (PaaS)
Software as a Service (SaaS)
Public cloud
Private cloud
Hybrid cloud
43.9.2. 2. Why choose the cloud for Data Science?#
Innovation
Flexibility
Budget
Scalability
Productivity
Reliability
Security
These are some of the most common reasons why people choose to use Cloud services.
Storing large amounts of data
Performing Data Integration
Processing data
Using data analytics services
Using Machine Learning and data intelligence services
43.9.3. 3. The “low code/no code” way#
Low code/no code tools is the cloud-based platform for building and operating Machine Learning solutions. It includes a wide range of features and capabilities that help data scientists prepare data, train models, publish predictive services, and monitor their usage.
Most importantly, it helps them to increase their efficiency by automating many of the time-consuming tasks associated with training models; and it enables them to use cloud-based compute resources that scale effectively, to handle large volumes of data while incurring costs only when actually used.
2021 no-code AI market map[1]
1. 45 no-code ai tools: The complete no-code ai guide(Updated December 2022). (n.d.). Akkio. Retrieved 5 March 2023, from https://www.akkio.com/post/45-no-code-ai-tools-complete-guide
43.9.4. 4. Low code/no code Machine Learning#
The Low code/No code way is easier to start with as it involves interacting with a GUI (Graphical User Interface), with no prior knowledge of code required.
Low code/no code training of a model
Low code/no code training with AutoML
Low code/no code model deployment and endpoint consumption