Any advice on building an end to end/deployment project?

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Hello. I am currently applying to ML Engineering jobs. However, nowadays every company asks for experience in end to end projects and deployment. Any advice and resources for building something portfolio worthy? Thanks, I hope I did not confuse.

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I am also at this point, and for the sake of building a powerful portfolio , powerful Projects are required, Indeed.
We can still use AI (i.e GPT, claude) for such kind of purposes,
But still , those Projects that feel kind of professional will work better in portfolio,

Those Professional like Projects can be in the category of , Recommendation Systems or Recognition Models, and a Personal Powerful Chatbot,

There is a high chance they will stand out and , of course you will also find such task interesting and innovating.

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Have you started any project yet? Just wondering what resources you found most helpful.

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Even I have created several Machine Learning Projects (i.e. Cat recognition, Stock Prediction, Image Classification), I haven’t worked on a complex project even after completion of my learning due to my upcoming college exams, once I am done from those exams I am going to work on Projects and applying for jobs.
But, still I do work on Machine Learning every night and use my free time to study for my exams. It would be great if we learn and develop end-to-end Projects together if you want.

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I am interested for Stock Prediction Project. Manish here :raised_hands:

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Building an end-to-end deployment project in machine learning involves several key steps:

  1. Problem Definition: Clearly define the problem you aim to solve and gather relevant data.
  2. Data Preparation: Clean, preprocess, and explore the dataset to ensure quality and relevance.
  3. Model Development: Select the appropriate algorithms, train the model, and fine-tune it for optimal performance.
  4. Evaluation: Use metrics to validate the model’s performance against test data.
  5. Deployment: Package your model using tools like Flask, FastAPI, or Docker and deploy it on platforms like AWS, Azure, or GCP.
  6. Monitoring: Continuously monitor performance in production and update the model as needed.

Focus on scalability, efficiency, and security during deployment. Start small, and iterate as you go for machine learning development services!

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