Kevin R.C.
Project OwnerLead data scientist and ML research engineer, project owner, and manager
Milestone Release 1 |
$24,000 USD | Pending | TBD |
Milestone Release 2 |
$6,000 USD | Pending | TBD |
Milestone Release 3 |
$2,000 USD | Transfer Complete | 29 Aug 2024 |
Milestone Release 4 |
$8,000 USD | Transfer Complete | 10 Oct 2024 |
Milestone Release 5 |
$5,000 USD | Transfer Complete | 09 Sep 2024 |
Milestone Release 6 |
$6,000 USD | Transfer Complete | 20 Sep 2024 |
Milestone Release 7 |
$4,000 USD | Pending | TBD |
Milestone Release 8 |
$6,000 USD | Pending | TBD |
Milestone Release 9 |
$4,000 USD | Pending | TBD |
Milestone Release 10 |
$4,000 USD | Pending | TBD |
Milestone Release 11 |
$6,000 USD | Pending | TBD |
Milestone Release 12 |
$6,000 USD | Pending | TBD |
Milestone Release 13 |
$5,000 USD | Pending | TBD |
Milestone Release 14 |
$10,000 USD | Pending | TBD |
Deep SIBYL (D-SYBIL) is an advanced forecasting service and time-series research tool. It comprises a stacked ensemble of cutting-edge deep learning (DL) models that capture more complex, nonlinear, and multivariate patterns in time-series data. Immediate use cases of D-SYBIL range from mapping extreme weather patterns (like flooding) to tracking crypto price movements (like boom/bust cycles). D-SYBIL is an independent service from SYBIL, a DF-R2 awarded service that contains mainly statistical and machine learning (ML) base models. Nevertheless, D-SYBIL can combine with SYBIL to become the ultimate forecasting service on the SNET platform, covering a wide array of time-series applications.
As explained in the Project Details section SYBIL will utilize D-SYBIL for its DL base model forecasts not the only way around.
New AI service
MAUQ service converts any model point prediction into a probabilistic prediction including forecasting models from SYBIL or D-SYBIL. MAUQ contains a single function called Quantity Uncertainty. Its input and output are specified below. This service is awarded in DF-R3 and will be available on the SNET marketplace shortly.
User inputs a calibration dataset (CSV file) and config parameters (JSON or YAML file) for MAUQ to fit the uncertainty measures. Just like in SYBIL MAUQ's front-end will convert the file format into JSON and concatenate the config parameters before passing it into the MAUQ service API.
MAUQ outputs that dataset with the uncertainty measures back to the user in JSON and then converted into a compatible file format like CSV.
New reviews and ratings are disabled for Awarded Projects
This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.
You can use this amount for payment of API calls on our platform. Use it to call other services or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.
$24,000 USD
GPU computational costs buffer for training and inference for these deep learning models on top of the 25% API Calls & Hosting.
None. I will request access to this buffer budget when needed during the implementation of Deep SYBIL (D-SIBYL) to pay for the expected additional GPU costs beyond what is allocated in the API Calls & Hostings budget.
$6,000 USD
Finalize milestone details and sign contract with SNET
- Finalize milestone details (including allocation of API Calls budget across development milestones) - Sign contract with SNET - Assemble team and set up tooling for D-SYBIL development (including JIRA GitHub Databricks AWS Cloud etc.)
$2,000 USD
Finalize D-SYBIL architectural design report
Write-up D-SYBIL architectural design report detailing its API schema code and libraries deep learning time-series models stacked ensemble design evaluation metrics applications and integration with SYBIL and other existing services.
$8,000 USD
Replace RNN with at least two statistical models in existing SYBIL service
- Remove RNN and PyTorch library (reduce library size) in existing SYBIL service - Add at least two statistical models in SYBIL in place of RNN such as Kalman Filter Fast Fourier Transform (FFT) or Wavelet
$5,000 USD
Set up a skeleton version of D-SYBIL service with RNN-based models as the first base deep learning models
- Spin-up AWS cloud environment (i.e. EC2 instance or Lambda) - Set up API gateway in accordance to API schema test hardcoded API calls and responses - Add RNN (and variants like LSTM) and Block RNN models to D-SYBIL service - Create example notebooks to run D-SYBIL service with RNN-based models as test
$6,000 USD
Add D-Linear and N-Linear models to D-SYBIL as base models
- Conduct further research into D-Linear and N-Linear models including their hyperparameters and computational load - Integrate D-Linear and N-Linear models to D-SYBIL service source code - Test D-Linear and N-Linear models using the example notebooks to run D-SYBIL service
$4,000 USD
Add N-BEATS and N-HiTS models to D-SYBIL as base models
- Conduct further research into N-BEATS and N-HiTS models including their hyperparameters and computational load - Integrate N-BEATS and N-HiTS models to D-SYBIL service source code - Test N-BEATS and N-HiTS models using the example notebooks to run D-SYBIL service
$6,000 USD
Add Temporal Convolutional Networks (TCN) models to D-SYBIL as a base model
- Conduct further research into TCN models including their hyperparameters computational load and uses in multivariate or cross-sectional forecasting - Integrate TCN models to D-SYBIL service source code - Test TCN models using the example notebooks to run D-SYBIL service
$4,000 USD
Add neural networks (NN) to D-SYBIL as a meta-model
- Conduct further research into using neural networks (NNs) as a meta-model within a stacked ensemble - Integrate NN meta-model to D-SYBIL service source code - Test NN meta-model using the example notebooks to run D-SYBIL service
$4,000 USD
Add at least one time-series transformer or transformer-like model to D-SYBIL as a base model
- Conduct further research into the feasibility of time-series transformer/transformer-like models including Time Series Mixer (TSMixer) Temporal Fusion Transformer (TFT) or Time-series Dense Encoder (TiDE) - Integrate at least one transformer/transformer-like model to D-SYBIL service source code - Test transformer/transformer-like model using the example notebooks to run D-SYBIL service
$6,000 USD
Integrate D-SYBIL service into the original SYBIL service with MAUQ for probabilistic forecasts
- Add D-SIBYL ensemble into SYBIL as a single base model - Add D-SYBIL's DL base models into SYBIL base model options (and SYBIL will pass all the DL base models to D-SYBIL) - Allow users to toggle between both options - Test D-SYBIL integration using the example notebooks to run SYBIL service itself - Test SYBIL output calibrated forecasts with MAUQ service
$6,000 USD
Deploy D-SIBYL onto SNET marketplace platform
- Deploy D-SIBYL API endpoint using SNET CLI - Publish D-SIBYL service onto SNET marketplace - Develop basic front-end marketplace UI - Test end-to-end from SNET marketplace UI
$5,000 USD
Finalize D-SIBYL service write up a final report and promote
- Make final integration tests and perform any adjustments/fixes of D-SIBYL - Write up a final report for D-SIBYL - Work on demoing outreach and promotion of D-SIBYL (and other interoperable services like SIBYL) with the assistance of SNET and the wider ecosystem to gain user traction including creating an initial growth strategy plan
$10,000 USD
Please create account or login to post comments.
Reviews & Ratings
New reviews and ratings are disabled for Awarded Projects
Gombilla
Jun 6, 2024 | 9:16 AMEdit Comment
Processing...
Please wait a moment!
Hi there. Great initiative with this. My comments would revolve around your model Complexity and Interpretability. The use of stacked ensemble deep learning models introduces complexity, which may affect the interpretability of the forecasting results. Users might struggle to understand the reasoning behind the predictions made by D-SYBIL, especially when combining multiple deep learning architectures. I hope there are strategies to address this. Thanks !
HenriqC
May 14, 2024 | 3:56 PMEdit Comment
Processing...
Please wait a moment!
You are one of pretty few entities who have launched real services on the marketplace. That's highly appreciated and adds credibility quite a bit , thanks! Did you identify any specific challenges/ways to overcome them that might be common and help other teams on their journey ?