Evaluate the problem - Deepstash

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Evaluate the problem

Before you start thinking about how to solve a problem with ML, take some time to think about the problem you are trying to solve. Ask yourself the following questions:

  • Do you have a well-defined problem to solve? Many different approaches are possible when using ML to recognize patterns in data.
  • Is ML the best solution for the problem? Supervised ML (the style of ML described in this documentation) is well suited to certain kinds of problems.
  • How can you measure the model's success? One of the biggest challenges of creating an ML model is knowing when the model development phase is complete.




AI Platform provides various interfaces for managing your model and versions, including a REST API, the gcloud ai-platform command-line tool, and...

To develop and manage a production-ready model, you must work through the following stages:

Monitor the predictions on an ongoing basis. AI Platform provides APIs to examine running jobs. In addition, various Google Cloud tools support the operation of your deployed model, such as Cloud Logging and Cloud Monitoring.

  • Vertex AI Workbench user-managed notebooks are Deep Learning VM Images instances pre-packaged with JupyterLab notebooks and optimized for deep learning data science tasks, from data preparation and exploration to quick prototype development.
  • BigQuery is a fully managed data warehouse...

You must have access to a large set of training data that includes the attribute (called a feature in ML) that you want to be able to infer (predict) based on the other features.

Develop your model using established ML techniques or by defining new operations and approaches.

AI Platform provides the services you need to train and evaluate your model in the cloud. In addition, AI Platform offers hyperparameter tuning functionality to optimize the training process.

Having sourced your data, you must analyze and understand the data and prepare it to be the input to the training process. For example, you may need to perform the following steps:

AI Platform provides tools to upload your trained ML model to the cloud, so that you can send prediction requests to the model.

During training, you apply the model to known data to adjust the settings to improve the results. When your results are good enough for the needs of your application, you should deploy the model to whatever system your application uses and test it.

TensorFlow has several preprocessing libraries that you can use with AI Platform. For example, tf.transform.

In the preprocessing step, you transform valid, clean data into the format that best suits the needs of your model. Here are some examples of data preprocessing:

Machine learning (ML) is a subfield of artificial intelligence (AI). The goal of ML is to make computers learn from the data that you give them. Instead of writing code that describes the action the computer should take, your code provides an algorithm that adapts based on examples of intended be...

AI Platform provides the services you need to request predictions from your model in the cloud.

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