Top 7 Development Tools for AI and ML

Development tools for AI and ML

Artificial Intelligence a popular technology of computer science is also known as machine intelligence. Machine Learning is a systematic study of algorithms and statistical models.

AI creates intelligent machines that react like humans as it can interpret new data. ML enables computer systems to perform learning-based actions without explicit instructions.

AI global market is predicted to reach $169 billion by 2025. Artificial Intelligence will see increased investments for the implementation of advanced level software. Organizations will strategize technological advancements.

Various platforms and tools for AI and ML empower the developers to design powerful programs.

Tools for AI and ML

Tools for AI and ML:

Google ML Kit for Mobile:

Software development kit for Android and IOS phones enables developers to build robust applications with optimized and personalized features. This kit allows developers to ember the machine learning technologies with cloud-based APIs. This kit is integration with Google’s Firebase mobile development platform.

Features:

  1. On-device or Cloud APIs
  2. Face, text and landmark recognition
  3. Barcode scanning
  4. Image labeling
  5. Detect and track object
  6. Translation services
  7. Smart reply
  8. AutoML Vision Edge

Pros:

  1. AutoML Vision Edge allows developers to train the image labeling models for over 400 categories it capacities to identify.
  2. Smart Reply API suggests response text based on the whole conversation and facilitates quick reply.
  3. Translation API can convert text up to 59 languages and language identification API forms a string of text to identify and translate.
  4. Object detection and tracking API lets the users build a visual search.
  5. Barcode scanning API works without an internet connection. It can find the information hidden in the encoded data.
  6. Face detection API can identify the faces in images and match the facial expressions.
  7. Image labeling recognizes the objects, people, buildings, etc. in the images and with each matched data; ML shares the score as a label to show the confidence of the system.

Cons:

  1. Custom models can grow in huge sizes.
  2. Beta Release mode can hurt cloud-based APIs.
  3. Smart reply is useful for general discussions for short answers like “Yes”, “No”, “Maybe” etc.
  4. AutoML Vision Edge tool can function successfully if plenty of image data is available.

Accord.NET:

This Machine Learning framework is designed for building applications that require pattern recognition, computer vision, machine listening, and signal processing. It combines audio and image processing libraries written in C#. Statistical data processing is possible with Accord. Statistics. It can work efficiently for real-time face detection.

Features:

  1. Algorithms for Artificial Neural networks, Numerical linear algebra, Statistics, and numerical optimization
  2. Problem-solving procedures are available for image, audio and signal processing.
  3. Supports graph plotting & visualization libraries.
  4. Workflow Automation, data ingestion, speech recognition,

Pros:

  1. Accord.NET libraries are available from the source code and through the executable installer or NuGet package manager.
  2. With 35 hypothesis tests including two-way and one-way ANOVA tests, non-parametric tests useful for reasoning based on observations.
  3. It comprises 38 kernel functions e.g. Probabilistic Newton Method.
  4. It contains 40 non-parametric and parametric statistical distributions for the estimation of cost and workforce.
  5. Real-time face detection
  6. Swap learning algorithms and create or test new algorithms.

Cons:

  • Support is available for. Net and its supported languages.
  • Slows down because of heavy workload.

Tensor Flow:

It provides a library for dataflow programming. The JavaScript library helps in machine learning development and the APIs help in building new models and training the systems. Tensorflow developed by Google is an opensource Machine Learning library that aids in developing the ML models and numerical computation using dataflow graphs. Use it by installing, use script tags or through NPM.

Features:

  1. A flexible architecture allows users to deploy computation on one or multiple desktops, servers, or mobile devices using a single API.
  2. Runs on one or more GPUs and CPUs.
  3. It’s yielding scheme of tools, libraries, and resources allow researchers and developers to build and deploy machine-learning applications effortlessly.
  4. High-level APIs accedes to build and train for ML models efficiently.
  5. Runs existing models using TensorFlow.js, which acts as a model converter.
  6. Train and deploy the model on the cloud.
  7. Has a full-cycle deep learning system and helps in the neural network.

Pros:

  1. You can use it in two ways, i.e. by script tags or by installing through NPM.
  2. It can even help for human pose estimation.
  3. It includes the variety of pre-built models and model subblocks can be used together with simple python scripts.
  4. It is easy to structure and train your model depending on data and the models with you are training the system.
  5. Training other models for similar activities is simpler once you have trained a model.

Cons:

  1. The learning curve can be quite steep.
  2. It is often doubtful if your variables need to be tensors or can be just plain python types.
  3. It restricts you from altering algorithms.
  4. It cannot perform all computations on GPU intensive computations.
  5. The API is not that easy to use if you lack knowledge.

Infosys Nia:

This self-learning knowledge-based AI platform accumulates organizational data from people, business processes and legacy systems. It is designed to engage in complicated business tasks to forecast revenues and suggest profitable products the company can introduce.

Features:

  1. Data Analytics
  2. Business Knowledge Processing
  3. Transform Information
  4. Predictive Automation
  5. Robotic Process Automation
  6. Cognitive Automation

Pros:

  1. Organizational Transformation is possible with enhanced technologies to automate and increase operational efficiency.
  2. It enables organizations to continually use previously gained knowledge as they grow and even modify their systems.
  3. Faster data processing adds to the flexibility of data visualization, analytics, and intelligent decision-making.
  4. Reduces human efforts involved in solving high-value customer problems.
  5. It helps in discovering new business opportunities.

Cons:

  1. It is difficult to understand how it works.
  2. Extra efforts needed to make optimum use of this software.
  3. It has lesser features of Natural Language Processing.

Apache Mahout:

Mainly it aims towards implementing and executing algorithms of statistics and mathematics. It’s mainly based on Scala and supports Python. It is an open-source project of Apache.
Apache Mahout is a mathematically expressive Scala DSL (Domain Specific Language).

Features:

  1. It is a distributed linear algebra framework and includes matrix and vector libraries.
  2. Common maths operations are executed using Java libraries
  3. Build scalable algorithms with an extensible framework.
  4. Implementing machine-learning techniques using this tool includes algorithms for regression, clustering, classification, and recommendation.
  5. Run it on top of Apache Hadoop with the help of the MapReduce paradigm.

Pros:

  1. It is a simple and extensible programming environment and framework to build scalable algorithms.
  2. Best suited for large datasets processing.
  3. It eases the implementation of machine learning techniques.
  4. Run-on the top of Apache Hadoop using the MapReduce paradigm.
  5. It supports multiple backend systems.
  6. It includes matrix and vector libraries.
  7. Deploy large-scale learning algorithms using shortcodes.
  8. Provide fault tolerance if programming fails.

Cons:

  1. Needs better documentation to benefit users.
  2. Several algorithms are missing this limits the developers.
  3. No enterprise support makes it less attractive for users.
  4. At times it shows sporadic performance.

Shogun:

It provides various algorithms and data structures for unified machine learning methods. Shogun is a tool written in C++, for large-scale learning, machine learning libraries are useful in education and research.

Features:

  1. Huge capacity to process samples is the main feature for programs with heavy processing of data.
  2. It provides support to vector machines for regression, dimensionality reduction, clustering, and classification.
  3. It helps in implementing Hidden Markov models.
  4. Provides Linear Discriminant Analysis.
  5. Supports programming languages such as Python, Java, R, Ruby, Octave, Scala, and Lua.

Pros:

  1. It processes enormous data-sets extremely efficiently.
  2. Link to other tools for AI and ML and several libraries like LibSVM, LibLinear, etc.
  3. It provides interfaces for Python, Lua, Octave, Java, C#, C++, Ruby, MatLab, and R.
  4. Cost-effective implementation of all standard ML algorithms.
  5. Easily combine data presentations, algorithm classes, and general-purpose tools.

Cons:

Some may find its API difficult to use.

Scikit:

It is an open-source tool for data mining and data analysis, developed in Python programming language. Scikit-Learn’s important features include clustering, classification, regression, dimensionality reduction, model selection, and pre-processing.

Features:

  1. Consistent and easy to use API is also easily accessible.
  2. Switching models of different contexts are easy if you learn the primary use and syntax of Scikit-Learn for one kind of model.
  3. It helps in data mining and data analysis.
  4. It provides models and algorithms for support vector machines, random forests, gradient boosting, and k-means.
  5. It is built on NumPy, SciPy, and matplotlib.
  6. BSD license lets you use commercially.

Pros:

  1. Easily documentation is available.
  2. Call objects to change the parameters for any specific algorithm and no need to build the ML algorithms from scratch.
  3. Good speed while performing different benchmarks on model datasets.
  4. It easily integrates with other deep learning frameworks.

Cons:

  1. Documentation for some functions is slightly limited hence challenging for beginners.
  2. Not every implemented algorithm is present.
  3. It needs high computation power.
  4. Recent algorithms such as XGBoost, Catboost, and LightGBM are missing.
  5. Scikit learns models take a long time to train, and they require data in specific formats to process accurately.
  6. Customization for the machine learning models is complicated.
AI and ML development

Final Thoughts:

Twitter, Facebook, Amazon, Google, Microsoft, and many other medium and large enterprises continuously use improved development tactics. They extensively use tools for AI and ML technology in their applications.

Various tools for AI and ML can ease software development and make the solutions effective to meet customer requirements. Make user-friendly mobile applications or other software that are potentially unique. Using Artificial Intelligence and Machine Learning create intelligent solutions for improved human life. New algorithm creation, using computer vision and other technology and AI training requires skills and development of innovative solutions that need powerful tools.

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Bridged.co

Bridged is striving to improve the efficiency of clients in the artificial intelligence sector through the use of training data powered by human intel. Since 2018, Bridged has delivered 50M+ datasets by deploying its 15,000+ Bridged-qualified crowd-force.

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