It’s been ten years since AlexNet’s success in starting the current deep learning revolution. The dust has already settled, the hype abated, and we are now entering the phase of productivity. What does it mean for you?
- Practical use of ML
- Ways of getting your ML solution
- What do you need to succeed with your ML strategy?
- Looking for the right partners for your ML project?
use of ML
Internet behemoths analyzing data from social media and online retail were the first to take use of the potential provided by ML (Machine Learning). Hardware firms soon followed, using it to optimize their CPUs, memory, and storage.
Numerous organizations all across the world were compelled by the recent epidemic to begin their digital transformation journeys or to speed up what they were already doing. As a result, many services are now virtually exclusively provided online using automated or partially automated processes. What is the overall meaning for you? It signals that you should embrace ML and take use of it. Let’s examine the process!
How to acquire your ML solution
You have two options when deciding how to begin your machine learning (ML) journey: either purchase an existing tool or create your own. Both approaches involve some danger and cost.
Finding one on GitHub is preferable because there are plenty of them there. However, you must keep in mind that employing something that already exists may not always accomplish your goals and may not be the greatest choice for your company.
With such a strategy, your solution will be adapted to your requirements, it will address the issues you are experiencing, and it will be unique to your company. Cost is definitely a drawback with this.
The good news is that there is a middle ground that combines the two strategies already mentioned. You mayIt’s a highly practical and cost-effective choice that’s becoming increasingly popular. identify and purchase a product that is only partially complete, then customize it to meet your needs and perform in the manner that you like. It is a highly practical and cost-effective choice that’s becoming increasingly popular.
What is required for your ML approach to be successful?
After determining the methods for obtaining your ML answer, let’s examine what you actually want. help make your ML approach successful. Following is our list:
1 The appropriate issue to address
Although it may seem simple, you must first identify the issue you hope to resolve and ensure that doing so would provide you with the appropriate value. You have to additionally validate that ML can be used to solve the problem.
All organizations are built around data, and this is also true of the IT projects those organizations work on. Its quality, quantity, and applicability to the issue you wish to use machine learning to address are all important. Having access to a lot of data does not guarantee that it will be beneficial to you: only weather-related information is not good enough for issues with industrial operations, even when the weather has a direct impact.
The process of gathering data should begin even before your machine learning project gets underway. In light of the constantly rising data quantities, it is wise to consider your data strategy as soon as possible and evaluate your present data environment before commencing any ML journey.
3 Capable individuals that can accomplish it
If you outsource your ML engineers, they will undoubtedly want assistance from within your company. They will need to communicate with domain experts who are familiar with the issue they are working on, who can clearly describe it, and who will be able to assess the outcomes produced.
Looking for the perfect partners for your machine learning project?
Future Processing can assist you at every level of your ML strategy! See what we can do to help you:
When contemplating a solution, consult our specialists to confirm your options and see whether machine learning can be used to solve the issue you are investigating.
Please present us with whatever data you may already have, and we will review the proof of concept. If you do not currently have your data, we may assist you with organizing the data gathering process (data engineering, cloud, and data pipelines are all areas of competence). A excellent approach is to chat with pragmatic digital transformation experts like embracent, who are ready to help. provide the best results for you and provide solutions to your problems.
Here we go if you’re ready to begin! We have a fantastic team of IT specialists with extensive expertise in machine learning projects. We can either build the machine learning model, reach MVP, and/or continue to develop the solution, or we may provide you with a full solution that includes application development, testing, delivery, and maintenance.
Matt Stuart is embracent’s Data & Insights Practice Lead. He assists his clients in developing the appropriate strategies, solutions, and operational models to maximize the value of their data assets.
Senior software engineer and machine learning practitioner Tomasz Gandor holds an MScEng in engineering. He is now working on Data Solutions projects while completing a PhD at the Polish-Japanese Academy of Information Technology.