Data-Driven ML/AI DevelopmentNashit M
I deliver end-to-end AI solutions, including data collection, exploratory analysis, automated pipelines, and model development. I start with baseline models to identify areas for improvement, then select and develop the most suitable model, whether from scratch or through fine-tuning, tailored to the specific task and field.
What's included
Data collection
Very often the most complicated and time-consuming problem in AI development is related to data finding and collection of it. When you have data, then 70% of job is done, so I can help with picking up the data and with it automated collection using web scrapers.
Exploratory Data Analysis (EDA)
When the data is already collected, then in 100% of cases, it should be preprocessed and analyzed to get a better understanding of what we have right now, which includes how much sparse it is, how well existing features are correlated, how well they are normalized, how they are distributed, getting info about labels if we have any, and providing full statistical analysis of existing data, so we can decide which pipeline is the most suitable for us.
Pipeline creation
After EDA is completed, now we have enough information to choose which pipeline to choose and what type of modifications to our data should be done. Here I also include automated features generation if it is needed.
Model selection
Based on the specifics of tasks and goals that we are trying to achieve I will choose the most competitive model, which will be suitable for you.
Model development
In this section, the model will be developed from scratch using existing ones' architecture or we will use a ready model, on which further actions will be taken
Model training and evaluation
Here we have 2 options: * Model is already pretrained * Model is not pretrained at all In the first case, we will fine-tune it using our dataset via modern validation techniques and make automated hyperparameter selection. In the second case our data will be used to train our model from zero. After it is trained, I will evaluate our model accuracy and get understanding whether expectations about the models were achieved or not.
Draft version deliverable
Client will get a draft version based on client requirements and I will be looking for his feedback on what is needed to be updated / added / deleted.
Final version deliverable
Based on the feedback of a draft version, client will get a final product - dockerized AI model, which he will be instructed of how to run and use.
Model Deployment
Productionize the trained model so it can be used
Nashit's other services
Contact for pricing
Tags
Python
PyTorch
scikit-learn
TensorFlow
Data Analyst
Data Engineer
Data Scientist
Service provided by
Nashit M Canada
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Data-Driven ML/AI DevelopmentNashit M
Contact for pricing
Tags
Python
PyTorch
scikit-learn
TensorFlow
Data Analyst
Data Engineer
Data Scientist
I deliver end-to-end AI solutions, including data collection, exploratory analysis, automated pipelines, and model development. I start with baseline models to identify areas for improvement, then select and develop the most suitable model, whether from scratch or through fine-tuning, tailored to the specific task and field.
What's included
Data collection
Very often the most complicated and time-consuming problem in AI development is related to data finding and collection of it. When you have data, then 70% of job is done, so I can help with picking up the data and with it automated collection using web scrapers.
Exploratory Data Analysis (EDA)
When the data is already collected, then in 100% of cases, it should be preprocessed and analyzed to get a better understanding of what we have right now, which includes how much sparse it is, how well existing features are correlated, how well they are normalized, how they are distributed, getting info about labels if we have any, and providing full statistical analysis of existing data, so we can decide which pipeline is the most suitable for us.
Pipeline creation
After EDA is completed, now we have enough information to choose which pipeline to choose and what type of modifications to our data should be done. Here I also include automated features generation if it is needed.
Model selection
Based on the specifics of tasks and goals that we are trying to achieve I will choose the most competitive model, which will be suitable for you.
Model development
In this section, the model will be developed from scratch using existing ones' architecture or we will use a ready model, on which further actions will be taken
Model training and evaluation
Here we have 2 options: * Model is already pretrained * Model is not pretrained at all In the first case, we will fine-tune it using our dataset via modern validation techniques and make automated hyperparameter selection. In the second case our data will be used to train our model from zero. After it is trained, I will evaluate our model accuracy and get understanding whether expectations about the models were achieved or not.
Draft version deliverable
Client will get a draft version based on client requirements and I will be looking for his feedback on what is needed to be updated / added / deleted.
Final version deliverable
Based on the feedback of a draft version, client will get a final product - dockerized AI model, which he will be instructed of how to run and use.
Model Deployment
Productionize the trained model so it can be used
Nashit's other services
Contact for pricing