Managing the Machine Learning Projects isn’t an easy piece of cake for every ML enthusiast or a student/developer working on them. Even Gartner has concluded in one of its researches that 85 percent of the ML projects have failed in the current year. And, this trend may continue in the future also if your personal weakness or the weakness of the entire team working on any of the Machine Learning projects isn’t replaced with proper expertise and collaboration when it is required. Is there some fear residing in your hearts whether or not the project you are working with may also result in failure without any prior intimation?
Such a thought is obvious but this mustn’t take you away from being productive and scalable. Rather, you must focus on other aspects like data exploration, monitoring and retraining of the ML models, collaborating effectively amongst the team members, and much more. The reason is that as soon as you start thinking about them, it will enforce you to focus on the point, “Am I using a correct set of tools that can manage well my ML projects?” Then, the question can be answered well with the points mentioned below explaining well their excellence and support so that your overall development is fostered with no compromise on quality and quantity of result-oriented solutions.
1. Google Colab
Google Colab (or Colaboratory) is a Google Research Product comprising of many reasons that assertively tell how and why it can manage well any of your assigned Machine Learning projects? First of all, it could be your EXCELLENT TOOL for any of the project tasks well-relatable with Deep Learning (just another sub-field of ML). Second, the version of Google Colab available over the INTERNET offer you free access to GC or Google Computing Resources like TPUs (Tensor Processing Units) and GPUs (Graphics Processing Units). Both these resources can SUITABLY accelerate the performance of various operations performed by any of your ML projects. And the third one – its COLLABORATION feature helping you at times you prefer to co-code with other experienced developers for learning and educating yourself more from their experiences. What else is required now to convince you more for selecting the Google Colab tool for your UPCOMING MACHINE LEARNING PROJECT?
2. Data Version Control (DVC)
DVC (or Data Version Control) is your OPEN-SOURCE tool or more of a VERSION-CONTROL system handling well datasets and other larger files of your ML projects keeping in mind other metrics of code. Curious to sense remarkably if the tool can help you build Machine Learning models not only reproducible but shareable too!! Yes, DVC knows how to log what you have done in your ML project, share the datasets defined by various rules and protocols, and then reproduce the ML models with no compromise on CONSISTENCY while working in a production environment. All these merits depicted by this Version Control System won’t put you in a dilemma at times you have decided to use its BASELINE for a newer iteration of your ML project somewhere involved with storage types relying upon the cloud infrastructure.
Streamlit after its launch has helped a lot of ML enthusiasts develop and deploy solutions, incredibly solving a lot of Python-related bugs. Whether it is about analyzing the Machine Learning charts or classifying the texts which make many of the ML functions included in your project easy-to-use, all of them can be brought to your table just by writing fewer lines of code through this beautiful tool. Furthermore, Streamlit treats many of the attached WIDGETS AS VARIABLES so you better not think much about the CALLBACKS. What you ought to know now is pip install streamlit – a command that can be used for Streamlit’s installation for simplifying data catching processes and speeding up the COMPUTATIONAL PIPELINES onto which the architecture of your ML project is relying upon.
Kubeflow needs no introduction while pronounced as your Machine Learning toolkit for various data management activities of your ML project either simple or complex. The ultimate mission of this dynamic toolkit is to construct as well as scale easy and portable ML models and then, deploying many of them. What IS GREAT in this toolkit is integration with SELDON CORE which is a considerable open-source platform helping you DEPLOY either ML/DL models on Kubernetes at scalable GPU utilization. Apart from this, the customization offered by this Google-backed toolkit is supportive at times you are stuck at mathematical operations and other dependencies playing a vital role in the end-to-end lifecycle of any of your MACHINE LEARNING projects. Noteworthily, the toolkit can contribute in a lot of ways to many of the use-cases dependent on Machine Learning for helping you get started with execution and monitoring of goals comprehended by the current or futuristic ML project.
5. Amazon SageMaker
Amazon SageMaker is a purposely-built service rather than a tool helping developers and other ML enthusiasts quickly prepare, train, and then deploy ML models of high-quality capabilities. Thinking about whether or not its web-based interface is capable of performing the essential ML Development steps like data collection, parameter tuning, or making predictions from the trained model used by any of your ML projects!! Yes, with this service offered by Amazon working for more than a decade for its customers, you can not only train as well tune the models well but also quickly upload the available data used to compare the results of all the steps of ML development. In short and crisp words, the service Amazon SageMaker understands the importance of keeping you productive at times you are dialing up the available computing resources required to conclude the accomplishments of your Machine Learning projects.
Github is that command-line Git repository whose web-based Graphical interface can potentially offer repository hosting service despite many other features like access control and collaboration. You may now raise a question how is the Git repository hosting service useful for any of your Machine Learning projects? Answer – this service will provide a TRANSPARENT VIEW of all the workflow processes implemented during the ML project chosen by you. Indeed, some of the typical workflow processes like data-preprocessing, evaluating data collection activities, and refined deployment to the production phase, are understood and managed well by this command-line tool. All you must be doing from now is create your profile on this amazing Github tool and get yourself involved with any of the branches of your Machine Learning project somewhere demanding the contributions of this best version control Github platform.
Downloading, Running, and then Analyzing Deepkit for any of your existing Machine Learning projects can reproducibly handle multi-variety data without any human intervention. Moreover, its successful integration with other popular tools like Docker, PyTorch, and TensorFlow will be helpful at times you are doing model debugging or job scheduling which is unavoidable at times you are involved deeply with your project demanding ML algorithms. Also, many of the professional developers call DeepKit an analytical training suite solely capable of steering up high-fidelity collaboration by quickly filtering thousands of experiments in a categorized and labeled manner. So, you need not write project summaries again and again as DeepKit knows really well how to model your ML project’s metrics so that you may track well the ongoing progress in a progressive manner.
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