Who should use the Deep Learning workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for deep learning with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
The deep learning model is successfully deployed to production, ready for real-time inference and use.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
The deep learning model is successfully deployed to production, ready for real-time inference and use.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use PyTorch to supporting assets from develop deep learning models are prepared and connected to the main workflow. Then, you pass the output to Dlib to supporting assets from developing deep learning models are prepared and connected to the main workflow. Then, you pass the output to SAS Viya to well-developed and tested machine learning model components are ready for integration into the deep learning workflow. Then, you pass the output to Keras to a trained deep learning model is produced, ready for evaluation or core execution. Then, you pass the output to Kaggle to complementary or benchmark machine learning models are trained and ready for integration or comparison. Then, you pass the output to GluonTS to a first-pass final deliverable is generated and ready for refinement in the next steps. Finally, Seldon Core is used to the deep learning model is successfully deployed to production, ready for real-time inference and use.
Develop deep learning models
Supporting assets from develop deep learning models are prepared and connected to the main workflow.
Developing deep learning models
Supporting assets from developing deep learning models are prepared and connected to the main workflow.
Develop machine learning models
Well-developed and tested machine learning model components are ready for integration into the deep learning workflow.
Train deep learning models
A trained deep learning model is produced, ready for evaluation or core execution.
Train machine learning models
Complementary or benchmark machine learning models are trained and ready for integration or comparison.
Deep Learning
A first-pass final deliverable is generated and ready for refinement in the next steps.
Deploy machine learning models
The deep learning model is successfully deployed to production, ready for real-time inference and use.
Utilize "pytorch" to design, build, and iterate on custom deep learning model architectures and algorithms. Develop and test components that will be integrated into the main deep learning process to enhance its capabilities.
Develop deep learning models strengthens deep learning by feeding better supporting material into the pipeline.
Supporting assets from develop deep learning models are prepared and connected to the main workflow.
Employ "dlib" to create robust machine learning components, focusing on algorithms and tools that complement deep learning tasks. Develop specialized features, data preprocessing modules, or custom network layers.
Developing deep learning models strengthens deep learning by feeding better supporting material into the pipeline.
Supporting assets from developing deep learning models are prepared and connected to the main workflow.
Using "sas-viya", develop and refine additional machine learning models or modules. Focus on creating robust, scalable components that will support or enhance the primary deep learning solution.
Developing strong machine learning models is crucial for building reliable and effective AI systems that complement deep learning solutions.
Well-developed and tested machine learning model components are ready for integration into the deep learning workflow.
Configure and execute the training process for deep learning models using "keras". Define model architecture, prepare datasets, set hyperparameters, and monitor training progress to achieve optimal performance for the subsequent deep learning tasks.
Thoroughly training deep learning models is foundational for ensuring accurate and reliable results in the subsequent core deep learning execution.
A trained deep learning model is produced, ready for evaluation or core execution.
Utilize "kaggle" to train additional or complementary machine learning models using prepared datasets. These models can serve as benchmarks, feature extractors, or as simpler components integrated into the larger deep learning architecture.
Training robust machine learning models provides a strong foundation and valuable benchmarks for the more complex deep learning processes.
Complementary or benchmark machine learning models are trained and ready for integration or comparison.
Run the core deep learning algorithms and models using "gluonts" on the prepared datasets. Monitor the execution, troubleshoot any issues, and generate the primary outputs, such as predictions, classifications, or insights.
This is the core step where deep learning actually happens, so it determines baseline quality for everything after it.
A first-pass final deliverable is generated and ready for refinement in the next steps.
Deploy the trained deep learning models and associated machine learning components into a production environment using "seldon-core". Configure endpoints, ensure scalability, and verify real-time inference capabilities for end-user applications.
Successfully deploying the models makes the deep learning solution accessible and functional for real-world applications and end-users.
The deep learning model is successfully deployed to production, ready for real-time inference and use.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
A streamlined workflow to prepare data, train a neural network model, and evaluate its performance using AI tools.
Streamlined workflow to automatically refactor existing code, debug errors, and finalize the refactored code for deployment.
End-to-end workflow to orchestrate data pipelines: start by performing predictive analytics to inform the pipeline, then orchestrate the data flow, and finally monitor model performance for ongoing reliability.