Machine learning has become an essential part of data science. It is enabling businesses to automate data analysis and make their services more efficient. This article covers some of the best tools for machine learning model training that can help you simplify the process. There are many tools out there that can help you, train models, faster, reduce your code maintenance, and optimize performance at the same time. Here we list some of the best machine learning tools for model training that will make your work easier.

TensorFlow

• TensorFlow is an open-source software library for numerical computation using data flow graphs. It was developed by Google as a general-purpose machine learning library for use in research, end-to-end application development, and production. 
• It can be used for various kinds of computation like image recognition, text analysis, or in other words, any data analysis problem can be solved by means of TensorFlow. The main advantage of TF is that it lets you check the accuracy of the model at each step of its training. 
• It also provides a great insight into the model’s behavior and helps you understand the model better. It is well suited for implementing complex models like neural networks and recurrent neural networks. If you are looking for a tool for model training, TensorFlow should be your first choice.

Hard

• The Hard project is an open-source toolkit for machine learning model training. It is developed by the music recommendation website Pandora with the goal to simplify the process of model training by providing a set of tools out of the box. It supports all the major machine learning frameworks like TensorFlow, sci-kit-learn, and others. 
• It also lets you, train models, in a distributed environment by leveraging the power of Spark. There are two main components of Hard: - The first one is a high-level API where you can simply define your model and train it. 
• This model configuration can be written in Pandas, R, Python, or any other JVM language. - The second one is a low-level API where you can access the underlying model configuration in a highly efficient way. This lets you write your model configuration from scratch and build a model from the ground up.

Spark ML

• Spark is a distributed computing framework that lets you process large datasets in a distributed environment. Spark ML is its machine learning library that lets you implement a variety of machine learning algorithms such as classification, clustering, regression, etc. 
• Hard vs. Soft vs. Spark: The difference between hard and soft boundaries is that the hard boundaries are defined by the requirements of the business whereas soft boundaries are defined by the team. Spark ML is a set of libraries that lets you implement a variety of machine learning algorithms. 
• The main advantage of Spark ML is that it supports distributed training of machine learning models. That is, you can train your model in a distributed environment to scale up your model training process. 
• The distributed implementation of Spark ML also follows a standard machine learning workflow. The only disadvantage of Spark ML is that it is not as efficient as TF when it comes to implementing complex models.

Python Data Science Tooling

• Python is one of the most popular programming languages in data science. There are a bunch of tools built for the Python ecosystem that can help you simplify your model training process and make your life easier. Pandas: Pandas are open source data structures for Python for data manipulation, exploration, and visualization. 
• Pandas can be used for simple data analysis, data wrangling, and building an ML model. Pandas are very useful for exploratory data analysis (EDA) and can be used as a companion tool for your model training process. They provide an in-depth insight into your data and make the model training process more efficient.
• Pandas are often used with NumPy, a library for scientific computing with Python. Together, they are powerful data analysis tools that can be used for machine learning model training. scikit-learn: sci-kit-learn is a machine learning library for Python that helps you build modeling and makes your model training process easier.
•  It is used for exploratory data analysis, data preprocessing, modeling, prediction, and evaluation of machine learning algorithms. It contains variously supervised and unsupervised algorithms that can be used for a wide range of applications.
• Some of the algorithms included in scikit-learn are Decision Trees, k-NN, Logistic Regression, One-Class SVMs, Gradient Boosting, and many more.

Conclusion

• Machine learning is a very powerful tool in data science, and it becomes even more useful when you simplify the model training process. 
• This article covers some of the best tools for machine learning model training that can help you simplify the process and make your work easier. 
• These tools let you implement the process of model training in a more efficient way by simplifying the implementation of the model configuration and providing tools out of the box. If you are looking to simplify the model training process, these tools can help you achieve that goal.