MLOPS_Andrew_Ng

• 앤드류 응 MLOps 강의

Idx	Notes	Course detail
1	lesson 1 note	The Machine Learning Project Lifecycle | Welcome
2	lesson 2 note	The Machine Learning Project Lifecycle | Steps of an ML project
3	lesson 3 note	The Machine Learning Project Lifecycle | Steps of an ML project
4	lesson 4 note	The Machine Learning Project Lifecycle | Case study: speech recognition
5	lesson 5 note	Deployment | Key challenges
6	lesson 6 note	Deployment | Deployment patterns
7	lesson 7 note	Deployment | Monitoring
8	skip
9	lesson 9 note	Select and train model | Modeling overview
10	lesson 10 note	Select and train model | Key challenges
11	lesson 11 note	Select and train model | Why low average test error isn't good enough
12	lesson 12 note	Select and train model | Establish a baseline
13	lesson 13 note	Select and train model | Tips for getting started
14	lesson 14 note	Error analysis and performance auditing | Error analysis example
15	lesson 15 note	Error analysis and performance auditing | Prioritizing what to work on
16	lesson 16 note	Error analysis and performance auditing | Skewed datasets
17	lesson 17 note	Error analysis and performance auditing | Performance auditing
18	lesson 18 note	Data iteration | Data-centric AI development
19	lesson 19 note	Data iteration | A useful picture of data augmentation
20	lesson 20 note	Data iteration | Data audgmentation
21	lesson 21 note	Data iteration | Can adding data hurt?
22	lesson 22 note	Data iteration | Adding features
23	lesson 23 note	Data iteration | Experiment tracking
24	lesson 24 note	Data iteration | From big data to good data
25	lesson 25 note	Define data and establish baseline | Why is data definition hard?
26	lesson 26 note	Define data and establish baseline | More label ambiguity examples
27	lesson 27 note	Define data and establish baseline | Major types of adata problems
28	lesson 28 note	Define data and establish baseline | Small data and label consistency
29	lesson 29 note	Define data and establish baseline | Improving label consistency
30	lesson 30 note	Define data and establish baseline | Human level performance (HLP)
31	lesson 31 note	Define data and establish baseline | Raising HLP
32	lesson 32 note	Label and organize data | Obtaining data
33	lesson 33 note	Label and organize data | Data pipeline
34	lesson 34 note	Label and organize data | Meta-data, data provenance and lineage
35	lesson 35 note	Label and organize data | Balanced train/dev/test splits
36	lesson 36 note	Scoping | What is scoping?
37	lesson 37 note	Scoping | Scoping process
38	lesson 38 note	Scoping | Diligence on feasibility and value
39	lesson 39 note	Scoping | Diligence on value
40	lesson 40 note	Scoping | Milestones and resourcing