Machine learning competitions are a relatively new phenomenon.
It appeared due to the development of artificial intelligence technologies.
At the moment, it is very actively developing and attracts a lot of interested people.
The advantages that the organizers of the competition receive:
– A large number of qualified people who work on their task and try to solve it better than others
– A relatively small (in comparison with the hiring of professionals) of the financial costs
– The solution of the problem, the most high-quality and suitable for it
And the contestants also benefit:
– Public recognition of high qualifications
– Cash prizes
– And just the pleasure of participating and winning
In this article, I want to look at several tools that can help participants organize the process better and more efficiently, increase the probability of winning, and generally become a more qualified specialist.
Platform for training deep learning models.
– Accelerated model learning, using state-of-the-art distributed learning, without changing the model code
– Automatic search for high-quality models, with advanced hyperparameter settings — from the creators of Hyperband
– Smart planning of your GPU usage and reduce the cost of cloud GPUs by using preemptible instances
– Track and reproduce experiments, including code versions, metrics, checkpoints, and hyperparameters
– Easy integration with popular DL frameworks
– Allows you to spend more time creating models than managing the infrastructure
Machine learning tool for automated forecasting.
– Structuring forecasting tasks and creating tags for supervised learning
– Search for training examples based on the final desired result set by the markup function
– Passing the result to Featurepools for automated feature design
– Passing the result to EvalML for automated machine learning
Framework for automated feature design.
– Converting temporary and relational datasets to feature matrices
– Ability to automatically generate feature descriptions in English
AutoML library for creating, optimizing, and evaluating machine learning pipelines using domain-specific objective functions.
– In combination with Featuretools and Compose, you can create end-to-end ML solutions for supervised learning
Creates profile reports from the Pandas DataFrame.
– Instead of df.describe () — function df.profile_report ()
– Quick data analysis
– Interactive HTML report with columns
– Type inference: Defining types
– Basics: type, unique values, missing values
– Quantile statistics: minimum, Q1, median, Q3, maximum, range, interquartile range
– Descriptive statistics: mean, mode, standard deviation, sum, mean absolute deviation, coefficient of variation, kurtosis, skewness
– Most common meaning
– Histogram
– Correlations of strongly dependent variables: Spearman, Pearson, and Kendall matrices
– Matrix of missing values: quantity, heat map, and dendrogram
– Text analysis: categories (uppercase letters, space), encoding (Latin, Cyrillic), and blocks (ASCII) in text data
– File and image analysis: file sizes, creation dates, truncated images, and images containing EXIF
Machine learning tool that optimizes pipelines using genetic programming.
– Automates the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best of your data
– After the search is complete, provides the Python code for the best found pipeline.
– Made on the basis of Scikit-learn
Game-theoretic approach to explaining the results of any ML model.
– Has an exact algorithm for an ensemble of trees
– Can be used in deep learning models
Library with multiple feature transformers for use in ML models.
– Allows you to select the variables you want to convert
– Transformers for missing data, categorical variables, sampling, variable transformations, outliers, creating and selecting variables
Library for semi-automatic data processing and selecting an algorithm for configuring hyper-parameters.
– Makes better automation, validation, and compatibility
– For automation — high-level interface of pipeline search tools
– To verify correctness — using a JSON schema to detect mismatch errors between hyperparameters and their type or between data and an operator
– For compatibility — growing library of converters and ratings from other popular libraries
Tool for working with unstructured data.
– Automatic classification — short and noisy texts, long texts; tools for monitoring and analyzing classification results; easy-to-use annotation user interface; pre-configured and extensible classifiers
– Data extraction-tabular data, long documents; built-in ready-made objects (date, time, quantity, weight, size, units of measurement), support for multiple file formats (PDF, Word, Excel, HTML, E-mail or plain text); customizable objects, attributes and relationships; relational output of objects, relationships, roles and attributes based on knowledge graphs
– Comparison-customizable semantic similarity services for sentences, paragraphs, and text content in databases; analytical user interfaces for finding the most similar and dissimilar elements
Tool for probabilistic data structures.
– Process and search large amounts of data very quickly
– Very small loss of precision
Tool for working with text.
– Extract the most popular phrases from text documents
– Performing low-cost extracting summation of text documents
– Output of links from unstructured text to structured data
– Support for linking objects
– Graph algorithms (in particular, the centrality of eigenvectors)
– Building a lemma graph to represent links between phrases and the supporting language
– Inclusion of verbs in the graph (but not in the resulting phrases)
– The use of pre-treatment with the division of nouns and recognition of named objects
– Extraction summarization on the basis of the ranked phrases
Set of tools for easy pipeline creation.
– Simple parallel computing
– Transparent function caching and lazy re-evaluation
– Optimized for fast and reliable processing of large data and arrays
– Convenient re-restart of experiments
– Separation of flow execution logic from domain logic and code
– Parallel helper — makes it easier to write readable parallel code and debug it
– Replacement of Pickle for working with objects containing big data
Tre-processing algorithm based on the structure.
– Faster performance than other optimizers
– Supports a set of pre-prepared matrices that operate in one dimension while shrinking in the others
– Has guarantees of convergence in a stochastic convex situation
Uber’s machine learning platform.
– Ensuring a continuous workflow
– Centralized feature storage
– Distributed learning infrastructure
– Evaluation and visualization of models with decision trees
– Model deployment tools
– Prediction and routing
– API for connecting pipelines
Tool for creating image labels.
– Fast data markup
– Automating the markup process
– Training of the helping model right during the markup
– Search for possible errors
Tool for large-scale workloads.
– Deploy models as a real-time or batch API
– High availability with availability zones and automatic instance restarts
– Logical output of on-demand instances or spot instances with on-demand backups
– Autoscaling for processing production workloads with support for redundant query allocation
Set of tools for machine learning.
– Tracking experiments
– Hyperparameter optimization
– Versioning of models and datasets
– Toolbar — view the experiment in real time
– Optimize models with a scalable hyper-parameter search tool
– Artifact tracking — save all the details of a continuous pipeline
– Joint documents — research of results and exchange of conclusions
Set of tools for deploying and managing ML experiments.
– Read configuration files and manage experiment directories
– Logging in Weights & Biases
– Setting up and running the hyperparameters using Weights & Biases
– Write text or images to a file, progress indicators
– Convert matplotlib shapes to images
– Visualization of multidimensional images
– Waiting for running processes to finish and resources to be released
Working with data — testing, documenting, and profiling.
– Automatic data documentation
– Generating documentation from tests
– Automatic data profiling
Platform for optimizing hyperparameters.
– Defining the search space
– Search for the best values
– Built-in Bayesian optimization algorithms
Desktop application for AI libraries, designed for developers of embedded applications and MCU C code.
– Search for the best libraries for embedded projects
– Enabling machine learning capabilities in MCU C code
– Run libraries on any Arm Cortex-M microcontrollers and optimized for them
– Very small model memory size (1–20kB RAM / Flash)
– Ultra fast models (1–20ms output on M4 80MHz)
– Automatic data quality check
– Automatic search for the best AI model
– Real-time data collection and import via serial port
– Emulator for testing the library before embedding
– Easy deployment of C libraries
– Models can be trained directly, without using the MCU
– No ML experience or expertise is required to create and deploy models.
End-to-end platform for creating and managing high-quality data.
– Automated markup
– Shared workspace for working with data and collective interaction between internal and external teams
– Track activity and work progress
– Access and role management
– API (Python, GraphQL) and SDK
– Working with images: classification, recognition and segmentation
– Working with video: powerful video editor, tags on video up to 30 FPS with frame level, tag feature analysis
– Working with text: classification, named entity recognition, support for complex ontologies with built-in classifications
– Pre-marking based on models and active learning
– Prioritizing the queue for marking the most important data using the API
Organization of ML experiments and monitoring of the learning process from a mobile device.
– Easy integration (2 lines of code)
– Storing the experiment log, including git-commits, settings and hyperparameters
– Storing the Tensor board log
– Control panel in the local browser
– Storage of checkpoints
– API for custom visualization
Low-code ML library.
– Fast process — from data preparation to model deployment
– Focus on business tasks instead of coding
– Easy to use and build a complete experiment process
– Model performance analysis (more than 60 graphs)
– Data preparation (missing values, transforming categorical data, creating features, configuring hyperparameters of the model)
– Support for the Boruta algorithm
Tool for quickly creating models.
– Track, compare, explain, and optimize experiments and models
– Fast integration
– Comparison of experiments — code, hyperparameters, metrics, predictions, dependencies, system metrics
– Debugging models — view, analyze, get information and visualize data
– Workspace for team interaction
Solution for combining ML tools (MLOps).
– One set of tools for automating the preparation, execution, and analysis of experiments
– Experiment management — parameters, tasks, artifacts, metrics, debugging data, metadata, and logs
– Management and orchestration of GPU / CPU resources, automatic scaling on cloud and on-premises machines
– Data storage — versioning analysis; creating and automating data pipelines; rebalancing, mixing, and combining datasets
Creates comfort, convenience, pleasantness, warmth and promotes creative inspiration
– Room with a pleasant atmosphere
– Classical music
– Great mood
Conclusion
Of course, just description of the tools is not enough to always win.
Success depends on many other factors — knowing where and when to use or not to use a particular tool, what restrictions exist, how to combine tools, etc., etc.
I hope that nevertheless this article will be useful for you and your participation in the competition will become more fruitful and effective.