Hari Thapliyaal

Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master’s degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set. With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management. In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights. Hari’s professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.

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ML Model Development Framework

ML Model Development Framework & Model Repositories

Introduction

There are hundreds of machine learning tasks. To do these tasks there are thousands of datasets created by individuals, governments, and corporations. We need to develop AI models using these datasets. There are thousands of models 1, 2, 3 and many model development frameworks. It is practically mind-blowing to track this whole body of work and understand all this work in its entirety. But if you dive deeper into the following frameworks you will get a fair idea about the overall direction of the work. These frameworks are used to maintain pre-trained model repositories and download pre-trained models. You can develop your own finetuned model using those pre-trained models.

List of Frameworks

  1. Model Repo - tfhub.dev : AI Model repository from google.
  2. tensorflow.org Website : an open-source software library for machine learning from Google
    github.com/tensorflow/models
  3. Microsoft AI on github
  4. Microsoft Cognitive Toolkit Website (previously known as CNTK), an open source toolkit for building artificial neural networks.
    github : github repo of CNTK
  5. Model Repo - pytorch.org/hub : An open-source Tensor and Dynamic neural network in Python from facebook
    Framework - pytorch.org
  6. Keras, a high level open-source software library for machine learning (works on top of other libraries).
  7. huggingface.co/models : AI Model Repository from huggingface.
  8. modelzoo.co : Discover open source deep learning code and pretrained models. Repository is created by Jing Yu Koh
  9. www.alibabacloud.com
  10. paperswithcode.com
  11. github.com/openvinotoolkit/open_model_zoo
  12. developers.google.com/mediapipe
  13. github.com/likedan/Awesome-CoreML-Models
  14. www.elinux.org/Jetson_Zoo
  15. Pinto_model_zoo
  16. github.com/onnx/models
  17. modelplace.ai : Ready-to-Integrate Computer Vision Neural Networks
  18. Apache Mahout, a library of scalable machine learning algorithms.
  19. Deeplearning4j, an open-source, distributed deep learning framework written for the JVM.
  20. OpenNN Website, a comprehensive C++ library implementing neural networks. OpenNN github repo
  21. Theano, a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones.