5 Effective Ways to Accelerate Machine Learning-Based Development In 2022

Machine learning is a very young but rapidly growing domain that focuses on offering computing systems with data and algorithms to mock how humans logically learn things. Machine learning applications are now becoming popular by contributing their benefits to all industries. Many businesses that adopt machine learning and big data, and data analytics are enjoying competitive advantages. It is proven that machine learning based development can significantly reduce cost, ensure better customer satisfaction, and optimize business processes.

With an increasing demand for machine learning applications, it is important to adopt quicker machine learning methods. Studies show that currently, it may take up to 15 or 18 months for deploying a basic ML model, and there is a lot of money and time to be factored in before the machine learning algorithms start to bring in any revenue. In the highly competitive business environment, it is very important to reduce the time to market and also to ensure the quickest turn-around time for the business to recover the development cost by making profits.

However, there is a scope for accelerating the machine learning based development process. You can reduce the work to just a few months, if not weeks, based on the purpose and nature of the model you build. In this article, we will discuss some of the top ways to accelerate machine learning development.

Understanding the business problem

When you plan to outsource machine learning development to a third-party development service or delegate an internal ML team, it is important to discuss the project objectives in clear terms. This has to be done upfront at the planning stage itself. Do not only share the basic idea you have about the project but every detail to be explained.

It is possible to solve business problems in many possible ways with machine learning methods. A good data scientist will understand which modes are more feasible, and the developers can understand which is the easiest and practical way to implement the same from a technology perspective.

When you start to assess the scope of an ML algorithm, one may find that it is more of a research project which may take 15- or 18-months’ time to complete. At this outset, a solid Proof of Concept is needed. With this, you can ask the ML consultants to estimate a comprehensive solution and not just the time and cost projections.

Allocate enough time to do research

By default, machine learning requires in-depth research to build applications. Machine learning projects must allocate enough time to do fundamental research and to explore through all available publications. There are a lot of challenges related to data, which have to be tackled and the solutions to be made available to the public.

Even when you work on a machine learning project with a cutting-edge idea, there is a lot of value for literature research. As a real-time example, you can refer to Cornell University’s arXiv, Papers With Code, etc. The key here is to accomplish the research as quickly as possible and move on to the actual development mode. Doing this efficiently will provide the fundamental building blocks to the system by offering examples and support to envision how the final model with performing. For machine learning data sets, RemoteDBA.com can offer reliable remote DBMS support.

Try to experiment with the ML model

With machine learning development, it is possible to have different hypotheses in hand. You may start to experiment with these hypotheses as early as possible and run them in shorter iterations. Doing this will let you confirm the most promising ideas based on your machine learning algorithms and also reject the compromised ones at the earliest stages. There is no point in starting with the development phase before validating the apt machine learning development concept.

Suppose you do not pay attention to validation as needed. In that case, there is every possibility that you may end up with a compromised machine learning algorithm that will be of no use from the business perspective. You may validate and try to deploy an ML algorithm only to the production model after the prototype is tested sufficiently to save time and money.

Validate the existing data science prototyping

The Internet puts forth many avenues for high-level experimentation and open-source frameworks, allowing data scientists to test different machine learning models before writing the code. Some real-time applications are Hugging Face, Fast.ai, or Ludwig from uber, which will help the developers deliver actionable prototypes quickly to evaluate the initial models. You can find some other useful frameworks available on the web as well.

Ensure effective communication across ML teams

A classic ML development team may include many experts with knowledge in various domains. It may also be needed to collaborate with different departments for the development and implementation of ML projects. In such an environment, a developer may be finding difficulty in fully grasping the overall objectives of data scientists. You have to ensure that all those who are in the team understand the concepts and communicate clearly. Without this being assured, there may be delays and mistakes. By establishing proper communication channels and effective strategic coordination, developers, engineers, and data scientists will be able to focus more on solving the problems instead of getting confused with miscommunication. So, ensure that you follow the communication best practices as in traditional development projects.

Some other important aspects to consider for successful and time-bound ML development project completion are as below.

  • Visualize all machine learning systems at the initial phase itself.
  • Have a proper roadmap for data collection, storage, and ML model upfront.
  • Map the inputs and outputs for the ML algorithms.
  • Ensure that the product owners are also involved actively in the development process to ensure that the business problem is addressed well.
  • Not test the model on the entire data set. Always have a good data split, including 70% of training and 30% of testing data or at a 60-30 ratio, respectively.
  • Also, have a retraining plan for the new machine learning model.

The development teams may also use different open-source tools available for the machine learning projects for validation and also the pre-trained ML models to ensure that they can deploy the algorithms in minimal time to production.