“completed” ## How can we tell if we have a good design? ## Elsewhere, design thinking requires prototyping Try stuff Reframe problems Empathize Ideate Build your way forward! Define Prototype Test := 0; k < m; k++ { t += a.AT___(i, k) * b.AT___(k, j) } } } ## Prototyping raises design questions we didn’t even know we should be asking ## Are index operator methods good more? Plenty of stuff to think about ... ## Conclusion • Go is a fantastic language for prototyping. • Prototyping allows us to build our way to good design. - If we can prototype language changes, we

methods are used during planning to decompose and translate requirements (e.g., storyboarding, prototyping, user shadowing, interviews, demonstrations, etc.)? - Does the program have clear, agreed-upon Enterprise Architecture ### 9.3 Continuous Prototyping “I have not failed. I have successfully discovered 10,000 ways to NOT make a light bulb.” – Thomas Edison Prototyping plays an essential role in developing but also has other uses. Real innovation always includes a risk of failure, and continuous prototyping gives program managers and users a powerful resource to reduce risk, quickly integrate new technologies

with fashioning abstract conceptual structures of great complexity. I suggest: • Using rapid prototyping as part of a planned iteration in establishing software requirements • Growing software organically suggest: • Exploiting the mass market to avoid constructing what can be bought. • Using rapid prototyping as part of a planned iteration in establishing software requirements. • Growing software organically Requirements refinement and rapid prototyping • Incremental development – grow, not build, software • Great designers ## Requirements refinement and rapid prototyping • The hardest single part of building

training jobs and pipelines with Argo • Serve trained models for inference with TF Serving • Rapid prototyping with self-service Jupyter notebook from JupyterHub ## Simplified ML Workflow/Pipeline • Keeping VMs as needed to save cost ## 口 Demo: Create End to End ML Pipelines with Argo ## Demo: Rapid prototyping with self-service Jupyter notebook from JupyterHub ## What's Next? • Pachyderm can version

the proof details until a later point. There are two main reasons why they are useful. • When prototyping, it is useful to be able to test programs before finishing all the details of proofs. • When reading (although evaluation at the prompt is possible without proof details). Sometimes, especially when prototyping, it is easier not to have to do this. It might even be beneficial to test programs before attempting believe_me : a -> b Obviously, this should be used with extreme caution. It is useful when prototyping, and can also be appropriate when asserting properties of external code (perhaps in an external

the proof details until a later point. There are two main reasons why they are useful. • When prototyping, it is useful to be able to test programs before finishing all the details of proofs. • When reading (although evaluation at the prompt is possible without proof details). Sometimes, especially when prototyping, it is easier not to have to do this. It might even be beneficial to test programs before attempting believe_me : a -> b Obviously, this should be used with extreme caution. It is useful when prototyping, and can also be appropriate when asserting properties of external code (perhaps in an external

the proof details until a later point. There are two main reasons why they are useful. • When prototyping, it is useful to be able to test programs before finishing all the details of proofs. • When reading (although evaluation at the prompt is possible without proof details). Sometimes, especially when prototyping, it is easier not to have to do this. It might even be beneficial to test programs before attempting believe_me : a -> b Obviously, this should be used with extreme caution. It is useful when prototyping, and can also be appropriate when asserting properties of external code (perhaps in an external

the proof details until a later point. There are two main reasons why they are useful. • When prototyping, it is useful to be able to test programs before finishing all the details of proofs. - When reading (although evaluation at the prompt is possible without proof details). Sometimes, especially when prototyping, it is easier not to have to do this. It might even be beneficial to test programs before attempting believe_me : a -> b Obviously, this should be used with extreme caution. It is useful when prototyping, and can also be appropriate when asserting properties of external code (perhaps in an external

the proof details until a later point. There are two main reasons why they are useful. • When prototyping, it is useful to be able to test programs before finishing all the details of proofs. - When reading (although evaluation at the prompt is possible without proof details). Sometimes, especially when prototyping, it is easier not to have to do this. It might even be beneficial to test programs before attempting believe_me : a -> b Obviously, this should be used with extreme caution. It is useful when prototyping, and can also be appropriate when asserting properties of external code (perhaps in an external

append/execute code (definitions, side effects, etc.) • education, scientific community, rapid prototyping of any kind ☐ ☐ Elements Console Sources Network Performance Memory top Filter Default by Neil Horlock - A Jupyter Notebook is an interactive document - a collaborative platform for prototyping, experimentation and analysis • Mix and share: code, text, data, computation and visualization