Machine Learning Model Development

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About this service

Summary

Offering machine learning and deep learning engineering models and classifiers for all sorts of business use cases. Datasets can be built and curated if not provided before model training. Clients may supply desired metrics and performance that should be achieved by the model.

Process

Hop on a discovery call to learn more about your business and use case

Proposed solutions to client's use are laid down to make a decision on the approach to use

Agent-building process commences, client is updated throughout the process to allow for complete transparency

Final solution is presented to the client, any revisions may be suggested until the desired solution is reached!

FAQs

  • What is the difference between machine learning and deep learning?

    These are simply two different ways to build a model to make some sort of predictions for you. Machine Learning is a more traditional route where computers learn patterns within the data while Deep Learning is a more complex approach that tries to emulate the way the brain thinks in order to extract meaningful information from the data and make predictions.

  • Do you build deep learning models, or just conventional machine learning models?

    I can build both machine learning and deep learning models. It will typically depend on the use case and jumping on a call to determine which approach is better a fit for your specific situation.

  • How long does it take to build the model?

    This time it takes depends hugely on the situation. For example, a project working with a huge proprietary dataset may require weeks of cleaning, preprocessing, training, and evaluation while with a more simpler project can have all of those phases done within days.

  • Do I have to provide a dataset, or can you build one yourself?

    For more niche and specific projects, it is better to provide your own dataset. However, in almost any case, a dataset can be harvested and built for a client. Datasets provided by clients will typically be better though, in most cases since they know their problem the best.

What's included

  • Machine Learning or Deep Learning Model

    At the end of the project can receive one of several deliverables, depending on the project: the model deployed on an accompanying web app for easy access and prediction, an API deployed on the cloud for flexible integration into existing software and programs, or the raw model file. The dataset built (if not provided by the client) can also be delivered along with the machine learning or deep learning model.


Skills and tools

ML Engineer
AI Model Developer
AI Developer
Python
PyTorch
scikit-learn
TensorFlow
Variational Autoencoders (VAEs)

Industries

Machine Learning
Artificial Intelligence (AI)

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