## A Day in the Life of a Data Scientist ## Conquer Machine Learning Lifecycle on Kubernetes  ## Brian Redmond • Cloud Repeatable/consistent • CI/CD • This has worked well for App Dev. Now time for AI/ML • But, must ensure data scientist are not hindered by structure ## Why Containers, Kubernetes & Helm? ## • Container • Contains

industry. 2. We plan to build TVM team in China, based in Shanghai, Beijing and Shenzhen. 1. Applied Scientist and SDE positions 2. Internship for students interested in ML system. 3. Research & Development

developing world, why it matters Mathew Benson ## About Me • I like C++! • I am a Graduate Computer Scientist(since 2007) - I have been working with and researching on computers and programming and how to measurements. It was born of my reflections after a long hard journey working with software both as a scientist, developer and as a user. • So I may not have some hard numbers, but I seek to appeal to our reflection of us Must be as welcoming as possible to new users. - We need the performance benefits of C++ applied to where it matters most i.e. on Older, Cheaper Hardware to make computing more accessible to more

"Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set."), ) guided_json: Optional[Union[str, dict, BaseModel]] "Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set.") ) response_format: Optional[ResponseFormat] = Field( previous page) |→555-4321|321 Maple St, Sydney, NSW||| |---|---|---|---| |\|5\|Carol White|31\|Scientist|New Zealand|carol.w@example.com| |→555-6789|654 Birch St, Wellington, NZ||| |\|6\|Dave Green|28

description=( "Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set."), ) guided_json: Optional[Union[str, dict, BaseModel]] "Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set.") ) response_format: Optional[ResponseFormat] = Field( bob.b@example.com | | →555-4321 | 321 Maple St, Sydney, NSW | | | | | 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | | →555-6789 | 654 Birch St, Wellington, NZ | | | | | 6 |

description= ("Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set.", ) guided_json: Optional[Union[str, dict, BaseModel]] "Whether to include the stop string in the output. " "This is only applied when the stop or stop_token_ids is set.") ) response_format: Optional[ResponseFormat] = Field( bob.b@example.com | | →555-4321 | 321 Maple St, Sydney, NSW | | | | | 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | | →555-6789 | 654 Birch St, Wellington, NZ | | | | | 6 |

various prompt attempts ..... 64 Summary ..... 66 Endnotes ..... 68 # You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. ## I ntroduction When thinking about prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective the top-K and top-P criteria are candidates for the next predicted token, and then temperature is applied to sample from the tokens that passed the top-K and top-P criteria. If only top-K or top-P is available

bob.b@example.com | | →555-4321 | 321 Maple St, Sydney, NSW | | | | | 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | | →555-6789 | 654 Birch St, Wellington, NZ | | | | | 6 | v@example.com | | →555-4444 | 753 Willow St, Melbourne, VIC | | | | | 11 | Irene Orange | 26 | Scientist | New Zealand | irene.o@example.com | | →555-5555 | 912 Poplar St, Auckland, NZ | | | | | 12 | b@example.com | →555-3434 | 912 Fir St, Limerick, IE | | 19 | Quinn White | 28 | Scientist | USA | quinn.w@example.com | →555-5656 | 159 Willow St, Seattle, WA

bob.b@example.com | | →555-4321 | 321 Maple St, Sydney, NSW | | | | | 5 | Carol White | 31 | Scientist | New Zealand | carol.w@example.com | | →555-6789 | 654 Birch St, Wellington, NZ | | | | | 6 | v@example.com | | →555-4444 | 753 Willow St, Melbourne, VIC | | | | | 11 | Irene Orange | 26 | Scientist | New Zealand | irene.o@example.com | | →555-5555 | 912 Poplar St, Auckland, NZ | | | | | 12 | b@example.com | →555-3434 | 912 Fir St, Limerick, IE | | 19 | Quinn White | 28 | Scientist | USA | quinn.w@example.com | →555-5656 | 159 Willow St, Seattle, WA

specific insight, idea, or proprietary knowledge. It reflects how much that knowledge could be worth if applied effectively, even if it hasn't yet generated revenue. Source: Microsoft, 'Governing AI: A defeats Garry Kasparov, the world chess champion at the time 1/62: Arthur Samuel, an IBM computer scientist, creates a self-learning program that proves capable of defeating a top USA checkers champion ## the first general-purpose mobile robot that can reason about its own actions Stanford computer scientist John McCarthy convenes the Dartmouth Conference on 'Artificial Intelligence,' a term he