These seven AI-based tools empower data scientists

Translator|Bugatti

Reviser|Sun Shujuan

##This article will discussSeven AI-based tools that can help data scientists improve their work efficiency . These tools can helpautomatically handledata cleaning,feature selection, model tuningand so on tasks, directly or indirectly make your work more efficient, more accurate, Andhelps make better decisions. Many of these

tools have user-friendly UI, it is very simple to use. At the same time, some tools allow data scientists to share and collaborate on projects with other members, which can help increase team productivity. 1. DataRobot

DataRobot is a platform based on

Web that can help Automatically build, deploy, and maintain machine learning models. It supports many features and technologies, such as deep learning, ensemble learning and sequential analysis. It uses advanced algorithms and technologies to canhelpyoubuild models quickly and accurately,still Provides functions for maintaining and monitoring deployment models.

It also allows data scientists to share and collaborate with others

Projects, thus making it easier for teams to collaborate on complex projects. 2. H2O.ai

H2O.ai

is a species An open source platform that provides professional tools for data scientists. Its main function is automated machine learning (AutoML) , can automate the process of building and tuning machine learning models. It also includes algorithms like gradient boosting and random forest. Since it is

one#open source platform, data scientists can customize their The source code needs to be customized so that it can be integrated into an existing system .

It uses a version control system to track all changes and modifications that are added to the code. H2O.ai also runs on cloud and edge devices, supporting a large and active base of users and developers who contribute code to the platform 者Community. 3. Big Panda

B

ig Panda is used to automatically handle IT operations Event management and anomaly detection. Simply put, anomaly detection is the identification of patterns, events, or observations in a data set that deviate significantly from expected behavior. It is used to identify data points that may indicate unusual or unusual #s or # problems.

It uses various AI and ML technologies to analyze log data

, and identify potential issues. It can automatically resolve incidents and reduce the need for manual intervention.

Big Panda can monitor the system in real time, which helps to quickly identify and solve problems. In addition, it can help determine the root cause of an incident, making problem

## easier and # prevent the issue from happening again.

4. HuggingFace

HuggingFace is used for natural language processing（NLP ), and provides pre-trained models, allowing data scientists to quickly implement NLP tasks. It performs many functions, such astext classification, named entity recognition, question answering, and language translation. It also provides the ability to fine-tune pre-trained models for specific tasks and datasets , and thus facilitates ImproveImprove performance.

Its pre-trained model has reached the state-of-the-art in multiple benchmark indicators performance, because they are trained using a large amount of data. This allows data scientists to build models quickly without having to train them from scratch, thus saving their time and resources.

The platform also allows data scientists to fine-tune pre-trained models for specific tasks and datasets, which It can improve the performance of the model. This can be done using a simple API, evenNLPexperiencelimited## It is also easy for people to use. 5. CatBoostThe CatBoost library is used for gradient

boost

tasks and is specifically designed for Designed to handle category data. It achieves state-of-the-art performance on many datasets , enabling accelerated model training processes due to parallel GPU computing.

CatBoost

The most stable,

overfitting in the data Most compatible with noise, this can improve the generalization ability of the model. It uses an algorithm called "Ordered Boosting" to before making a prediction. IterationWayFill in missing values. CatBoost provides feature importance, which can help data scientists understand

the contribution of each feature to model predictions . 6. OptunaOptuna is also an open source library, mainly used for hyperparameter

adjustment

and optimization. This helps data scientists find the best parameters for their machine learning models. It uses a called "Bayesian Optimization" technology that can automatically search for a The optimal hyperparameters for a specific model.

Another of its main features is that it

is easy to interact with various A variety of machine learning frameworks and library integrations,

such asTensorFlow, PyTorch and scikit-learn. It can also optimize multiple targets simultaneously, in performance and other metrics provides a good trade-off. 7. AssemblyAIIt is a platform that provides pre-trained models, designed to enable developers to integrate these Model

Easily

integrate into existing applications or services.

It also provides various API, such as speech to textAPI or Natural Language ProcessingAPI. Speech to text API is used to obtain text from audio or video files with high accuracy. In addition, the natural language API can help with tasks such as sentiment analysis, image entity recognition, and text summarization. ##Conclusion

Training a machine learning model includes data collection

&

Preparation, exploratory data analysis, feature engineering, model selection

and training, model evaluation, and model deployment. To perform all tasks, youneedto understand the various tools and commands involved. These seven tools can help you spend the minimum effort to train and Deploy the model. Original title: ##Ranking of universities and colleges specializing in data science and big data technology

By Aryan Garg

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