How can you find machine learning platforms that provide automated model selection and hyperparameter tuning?

ML is more than 50 years old. If you want to know what the various models and algorithms are doing behind the math. Just google 'AI reference books'. Then buy them on amazon and read them. You will be astonished how a book written 20 years ago illuminates much of what can be done today. But to be honest, platforms that have automated model selection and hyperparameter tuning allow you to skip the understanding ML. AI is not regulated so accuracy and speed are end all be all. Verification and explainability are lagging behind performance. So whichever platform you end up with, you can get away with training all the models and tuning it to get the one that satisfies your metric.

The specific problem you are trying to solve is critical in determining the most ML model. Different types of ML problems (e.g., classification, regression, clustering) and domains (e.g., finance, healthcare, e-commerce) require different approaches and models

To find machine learning platforms with automated model selection and hyperparameter tuning, start by researching online reviews and articles about AutoML tools. Look into popular options like Google Cloud AutoML, Azure Machine Learning, and H2O.ai. Join data science communities on LinkedIn or Reddit for recommendations. Additionally, use free trials to test and see which platform suits your needs best.

In a real-life scenario, consider an e-commerce platform aiming to improve its product recommendation system. Automated model selection would enable the platform to identify the most suitable algorithm among various options, such as decision trees, neural networks, or support vector machines, based on the dataset's characteristics and desired outcomes. Once the model is selected, hyperparameter tuning comes into play in a neural network model, adjusting hyperparameters like learning rate, batch size, and number of layers can significantly impact its performance in accurately predicting. hyperparameter tuning, the e-commerce platform can fine-tune its recommendation system to achieve results, improving user satisfaction and boosting sales.

There are several ways to find it. Online Search: Look for platforms like Google Cloud AI Platform, Amazon SageMaker, Microsoft Azure Machine Learning. Engage with the Community: Join machine learning forums, participate in discussions on LinkedIn, attend conferences, webinars, and workshops, or join online communities like Kaggle to connect with experienced practitioners.

Best way I found is look for AI papers in a field that is not traditionally AI like physics or biology or non-electrical engineering. These types of papers are often written by domain experts who wanted to use AI but are not trained in it. Look through methodologies as often they will mention platforms they use and the library they used.

You start by researching platforms with a strong reputation among ML practitioners. Through reading reviews and participating in online communities like Reddit's r/MachineLearning or Stack Overflow, you discover a platform highly praised for its automation features. fellow data scientist shares their experience on a forum, highlighting how the platform's automated model selection and hyperparameter tuning significantly boosted their productivity and model accuracy. how easy it was to deploy models and integrate them into their existing workflows. Encouraged by such positive feedback, you decide to give the platform a try. As you explore its features, you find that its popularity indeed correlates with its reliability and user-friendliness

Look into reputable machine learning platforms like Google Cloud AI Platform, Amazon SageMaker, and Microsoft Azure Machine Learning. These platforms are known for their advanced automation features. For instance, Google's AutoML offers sophisticated model selection and hyperparameter tuning that can handle complex datasets with ease.

Choosing the right machine learning platform involves several key considerations: 1. Open Source vs. Proprietary: Open-source platforms offer flexibility and community support, while proprietary ones may provide polished tools but at a higher cost. 2. Supported Algorithms and Frameworks: Ensure the platform supports the algorithms you need (e.g., Bayesian Optimization) and integrates with ML frameworks like PyTorch or TensorFlow. 3. Scalability: Choose platforms with cloud integration and scalability features to handle large datasets and computing demands. 4. Cost: Balance between free offerings and paid platforms. 5. Automated Features: Look for automation in hyperparameter tuning and model training to save time and improve model quality.

Amazon SageMaker provides comprehensive documentation on its automatic model tuning feature, which optimizes hyperparameters using Bayesian optimization or random search algorithms. It supports a wide range of built-in algorithms, including XGBoost, TensorFlow, and Apache MXNet, making it versatile for different use cases. Microsoft Azure Machine Learning offers a balance between automation and user control. While it provides automated machine learning capabilities for model selection and hyperparameter tuning, it also allows for manual tuning and customization through its user-friendly interface and integration with popular frameworks like scikit-learn and TensorFlow.

Dive into the features of each platform. Google Cloud AI Platform's AutoML Tables, for instance, automates the process of building and optimizing machine learning models for structured data. Amazon SageMaker includes automatic model tuning that continuously optimizes model performance based on specified metrics.

NeptuneAI and ObviouslyAI are one those platforms that I've tried for myself and they are surely a must try option to everyone too

Data scientist at a startup exploring different platforms to improve their machine learning capabilities. Your team is considering using a platform to streamline your image recognition tasks for a new mobile app feature. After researching various platforms, you decide to sign up for free trials. during the trial period, you upload a sample dataset of images representative of the app's usage. You then explore each platform's automated features, particularly focusing on automated model selection and hyperparameter tuning. you will select best products and services according to need.

Take advantage of free trials or demos to see these tools in action. Google Cloud AI Platform and Amazon SageMaker both offer trial periods where you can experiment with their automated capabilities and see how they benefit your specific projects.

I love to learn from people and their programs. most of the time data analyst and data scientist learn from kaggle, leet code and neet code. one help to improve coding and one help to improve analytical skill.

Join online communities like Kaggle forums or Reddit's machine learning subreddit. Many practitioners share their experiences with different platforms. For example, you might hear success stories about how someone used AutoML on Google Cloud AI Platform to rapidly develop accurate predictive models.

Consider the level of ongoing support provided by each platform. Google, Amazon, and Microsoft have extensive documentation, tutorials, and support forums. They also offer training courses and certifications to help you master their automation tools and get the most out of your machine learning projects.