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Премиум
  1. Урок 1. 00:08:20
    Introduction to the course
  2. Урок 2. 00:08:52
    What is the goal of this course?
  3. Урок 3. 00:15:17
    Why Rust?
  4. Урок 4. 00:10:43
    Our plan
  5. Урок 5. 00:16:28
    Installing the tools: rustc, cargo, rust-analyzer
  6. Урок 6. 00:21:04
    How to generate and run the binaries
  7. Урок 7. 00:10:00
    Steps to train an ML model in Rust
  8. Урок 8. 00:26:56
    How to download the CSV file - Part 1
  9. Урок 9. 00:20:22
    How to handle errors and Results with anyhow and ?
  10. Урок 10. 00:23:10
    Generic types, traits and trait bounds in function definitions
  11. Урок 11. 00:10:37
    How to download the CSV file - Part 2
  12. Урок 12. 00:03:17
    Wrap up
  13. Урок 13. 00:05:04
    Goals for today
  14. Урок 14. 00:18:15
    Load the CSV file into memory with Polars Rust
  15. Урок 15. 00:05:11
    Extract functions to lib.rs
  16. Урок 16. 00:25:31
    Splitting the data into training and test - Part 1
  17. Урок 17. 00:18:33
    Splitting the data into training and test - Part 2
  18. Урок 18. 00:04:06
    Questions and answers
  19. Урок 19. 00:12:21
    Split datasets into features and targets
  20. Урок 20. 00:24:43
    Transforming Polars DataFrame to DMatrix for XGBoost training
  21. Урок 21. 00:20:48
    Training the XGBoost model - Part 1
  22. Урок 22. 00:02:53
    Questions and answers
  23. Урок 23. 00:12:04
    Training the XGBoost model - Part 2
  24. Урок 24. 00:18:41
    Pushing the model artifact to AWS S3 bucket
  25. Урок 25. 00:01:48
    How to set up your AWS credentials to talk to your S3 bucket
  26. Урок 26. 00:01:49
    Wrap up
  27. Урок 27. 00:03:35
    Goals for today
  28. Урок 28. 00:05:08
    Tip -> devcontainers
  29. Урок 29. 00:05:55
    Add 2 entry points, one for training, one for REST API
  30. Урок 30. 00:02:35
    Questions and answers
  31. Урок 31. 00:19:55
    Building a minimal API with health endpoint - Part 1
  32. Урок 32. 00:10:25
    Building a minimal API with health endpoint - Part 2
  33. Урок 33. 00:22:20
    Adding a predict endpoint to our REST API
  34. Урок 34. 00:02:34
    Tip -> How to break a large problem into smaller (easier) ones
  35. Урок 35. 00:23:37
    Download model artifact from AWS S3 bucket
  36. Урок 36. 00:17:27
    Refactorings -> Break our lib.rs into separate files
  37. Урок 37. 00:11:24
    Adding CLI parameters for training using Clap
  38. Урок 38. 00:07:39
    Loading the model from S3 into memory
  39. Урок 39. 00:21:03
    Adding model to the AppState so we can use it in our predict functions - Part 1
  40. Урок 40. 00:07:46
    Adding model to the AppState so we can use it in our predict functions - Part 2
  41. Урок 41. 00:02:32
    Wrap up
  42. Урок 42. 00:02:17
    Plan for today
  43. Урок 43. 00:21:04
    Transform input payload to dmatrix for XGBoost
  44. Урок 44. 00:15:42
    Formatting the response with the prediction
  45. Урок 45. 00:07:26
    Extracting the port number as input argument
  46. Урок 46. 00:11:22
    Two challenges for you
  47. Урок 47. 00:20:40
    Dockerizing the API - Part 1
  48. Урок 48. 00:06:49
    Trick -> How to solve Docker no space left problems
  49. Урок 49. 00:23:00
    Dockerizing the API - Part 2
  50. Урок 50. 00:22:35
    Dockerizing the API - Part 3
  51. Урок 51. 00:21:47
    Dockerizing the API - Part 4
  52. Урок 52. 00:07:43
    Dockerizing the API - Part 5
  53. Урок 53. 00:01:51
    Dockerizing the API - Part 6
  54. Урок 54. 00:02:37
    Wrap up
  55. Урок 55. 00:02:49
    00 - Intro
  56. Урок 56. 00:04:27
    01 - Bootstrap the project
  57. Урок 57. 00:21:43
    02 - Hello world with a /health endpoint
  58. Урок 58. 00:02:58
    03 - A bit of refactoring
  59. Урок 59. 00:08:39
    04 - Add a /predictions endpoint
  60. Урок 60. 00:08:28
    05 - Break down project in several files
  61. Урок 61. 00:07:52
    06 - Start Postgres DB with Docker
  62. Урок 62. 00:20:28
    07 - Fetch data from Postgres
  63. Урок 63. 00:02:07
    08 - Loading environment variables
  64. Урок 64. 00:04:57
    09 - Add GET parameter to the /predictions endpoint and compile for release