Machine Learning and Deep Learning are no longer just hype and buzzwords. The once frontier technology has evolved into a crucial component of the technology stack of enterprises and startups; this has transformed software development. Since Machine Learning (ML) is intertwined with decision-making processes at companies, we want to make the distinction that the ML technology stack is a process and not just a static piece of software.
Machine Learning solutions are driven by availability and quantity of the right data, which changes how we think about building, maintaining and improving infrastructure. Building Machine-Learning solutions is not simple due to these factors. Data scientists, software developers, and DevOps engineers must collaborate in several areas to produce a useful solution. This article outlines 6 best practices that every organization leading a Machine-Learning project should follow.
Defining a concrete goal or objective is not as simple as it sounds. There are a variety of approaches you might use to solve a problem, and it is not always clear which one is best. It can be tempting to spend less time clearly defining goals, but poorly defined goals are how projects end up derailing initiatives as the team building the solution won’t know what to prioritize and may just get lost testing to see what various models can achieve and destroy project momentum, the likelihood of a project launching and Return on Investment (ROI) due to endless development.
Having clearly defined goals and priorities is essential to managing your enterprises’ Machine Learning objectives. You’ll frequently wind up overshooting timelines due to an ever expanding scope and lack of evaluation criteria, both of which may cause you to shift focus from identifying the solutions that have a ROI and meet your enterprises goals. From the beginning of the project, everyone should be working toward the same objective.
You should have a solid concept of how your progress will appear before you even write your first line of code. Think about the following questions before you start your ML project:
What does your ML project want to achieve?
Do you have the right data?
How will the model’s performance be evaluated?
Does the model need to be lightweight and run on a user’s machine or the company’s server?
Can the model process the data in advance or do you need a lightweight model that can run in realtime?
Is the necessary infrastructure in place?
Is the extra performance of larger models and more GPUs actually important to the use case or worth the ROI?
What are the requirements for deployment?
Is explainability necessary?
Even though the initial model is being used in production your work is not yet complete. The key to successful implementation of Machine Learning is to start small, get an MVP up and running with the data you have and benchmark the solution to see if the accuracy of the model is or can be comparative to human level performance. Once you do that you then evaluate if there is ROI in further iterations, investing in getting more and better data, and potentially solving for edge cases that don’t have enough data with non-ML techniques.
Always repeat the procedure for every new solution and make changes before the following iteration. Business objectives almost always vary. As tThe underlying technology, research, methods, and hardware to power computational-intensive solutions evolves. All these can result in the need to fine tune or optimize your model to adapt to changing conditions in the world or industry you operate in, the data you are working with, new capabilities or brand-new systems.
Sometimes the requirements are not very obvious, making it difficult to immediately identify the right goal. When integrating Machine Learning into legacy systems, this is frequently the case. Gather as much information from the current system as possible before getting into the specifics of what your application will perform and the function Machine Learning will play.
You can accomplish the task at hand using historical data in this way. Additionally, this data might immediately point up areas that need optimization and the optimal course of action.
After you have a grasp of the issue, pertinent information is required. It’s worth looking through them because most data sources are accessible for free on websites like Kaggle and UCI datasets. If your problem is distinct, you may need to collect, organise and warehouse your own data. Internet scraping and manually categorizing the data you gather are two frequent options. Getting the right quality and enough quantity of the data you need is often what will allow you to create useful ML solutions that are more likely to get into production after initial testing.
The chosen ML models should be manually run to check for accuracy after selection. For instance, in the case of personalized email marketing, you should adapt your strategy and test more variables if the promotional emails being sent aren’t generating above your baseline conversion rate.
It is necessary to select the best technology after successful manual tests. Data science teams should be free to choose from various technology stacks to enable experimentation and the selection of the technology stack that makes ML simpler. Benchmarking should be done for speed, stability, ROI, ability to solve workforce / customer problems, future use cases, and on device or cloud performance.
Machine Learning and Deep Learning models require extensive domain knowledge, access to high-quality labelled data, and computational resources for continuous model training and improvement. Machine learning model improvement is a skill that evolves from methodically addressing the shortcomings of the existing models with the given constraints. Skim AI provides helpful solutions for people at all levels, from students to CEOs, that assist you in cutting through the noise, discerning better insights, and making better decisions based on data that counts.