The process of building machine learning models, as any developer or data scientist would attest, is fraught with hurdles.
From the taxing necessity of writing repetitive code to managing intricate steps in the data pipeline, the challenges are numerous.
Yet, a groundbreaking research paper from the International Journal for Research In Applied Science & Engineering Technology presents an innovative solution. One that promises to make this journey a lot smoother.
The Common Roadblocks in ML Development
“The process of building ML models is tedious and time consuming. There are some common problems which people face while developing ML-based projects. That includes lack of resources and infrastructure, lack of uniformity in codebase and writing repetitive code for every project. These problems are very serious as building ML models needs a lot of effort and time,” the paper says.
Before delving into the solution, it’s crucial to understand the biggest problems faced in ML model development:
Lack of resources and infrastructure:
The infrastructure necessary to handle vast amounts of data and intensive computation often becomes a bottleneck.
According to Censius.ai, a primary hurdle in ML model creation is a lack of resources and infrastructure. Model training can be time-intensive and demands specific infrastructure, with certain applications consuming over 50,000 GPU hours. And you can imagine the costs.
This makes it essential that organisations allocate resources for the appropriate infrastructure to back their ML initiatives.
Uniformity concerns in the codebase:
Disparities in coding practices can result in inconsistencies, hampering the efficient development and scalability of models.
An additional obstacle highlighted by Neptune.ai is a clear lack of consistency in the codebase.
Varied teams might adopt distinct tools and frameworks, resulting in integration issues due to these disparities, particularly during deployment. For example, while some data scientists might prefer TensorFlow, others could lean towards scikit-learn, complicating the deployment of ML models.
Reinventing the wheel for every project is neither efficient nor sustainable.
Repetitive code is a common issue in ML development. As The Enterprisers Project points out, this often leads to inconsistencies, portability issues, and dependency management problems.
It’s essential to ensure everyone is using the same tooling and hardware across different training environments to reliably share code and datasets.
data management, model learning, verification, and deployment:
Each of these steps has its inherent problems, further explained in the referenced papers, which can bog down the entire development process.
In data management, data preparation consumes much of a data scientist’s efforts. On top of that, the need for vast datasets during training presents challenges like data movement difficulty, high transfer costs, and extended durations.
Model training often presents frustrations like variations in data sources, model settings, and feature adjustments – leading to potential errors. Then, validating ML models presents challenges like shifting data inputs, which degrade a model’s performance, necessitating hyperparameter adjustments, as noted by Neptune.ai.
Lastly, ML model deployment is intricate; uniform workflows are hard to maintain, and the resource-intensive nature of some projects demands collaborative decision-making on deployment priorities.
Enter the Web-Based Portal for Complete Data Engineering
The proposed portal is a one-stop solution designed to streamline the machine learning pipeline.
- Automation: One of the portal’s most significant advantages is its ability to conduct standard data preprocessing steps without requiring a single line of code. Imagine uploading a dataset and, within a few clicks, having it ready for ML model deployment!
- Uniformity and Efficiency: By offering a uniform codebase, developers can avoid the pitfalls of inconsistencies. What’s more, the redundancy of writing repetitive code for every project becomes a thing of the past.
- Key ML Pipeline Steps: While the paper doesn’t delve deep into the exhaustive features of the portal, it underscores its capacity to manage crucial steps like Exploratory Data Analysis, Data Preprocessing, and Feature Engineering swiftly.
Implications and Benefits
Developers, researchers, and data scientists can look forward to a future where ML project development is more efficient and less resource-intensive.
Organisations, in turn, can expect improved quality in their projects, giving them a competitive edge.
The proposed web-based portal for complete data engineering signifies a paradigm shift in how we approach ML development. It not only addresses the existing challenges head-on but promises a future where these problems become a footnote in the annals of ML history.
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