Machine learning assists your firm in automating decisions by utilizing algorithms that learn from data.
Machine learning, as opposed to data science, which takes a comprehensive approach to improving your organization, focuses on applied methodologies to address automation challenges with artificial intelligence.
In fact, if you’re reading this, you’re presumably already aware of the general benefits of data science.
Please take your time since our machine learning services are unique. Consider the following advantages of our machine learning consulting services.
It can be difficult to decide which ML project to work on next. We alleviate the strain on your ML strategy by:
Developing good models, while difficult, is simple if you know what’s important. Allow us to go through your model portfolio so that we can:
Do you have trouble maintaining consistency machine-learning learning projects? We can assist you:
Don’t forget that we offer a variety of different ai services in addition to the work that we do. We can make your AI run like clockwork, from data science advice through mlops implementation.
Through domain-specific methodologies and applications, machine learning has penetrated practically every industry.
We have a remarkably wide set of experiences that we can bring to your organization because ARTMAC is a flexible, decentralized, independent machine-learning company.
We provide a variety of data services to assist businesses of all kinds in developing better goods for higher customer satisfaction or market share. By automating crucial choices, you can revitalize unproductive internal procedures. Use intelligent agents to learn from experience in a proactive manner. Create automated and optimized tactics to propel your company forward.
Machine Learning Consulting Process
1. Business Situation
Any issue necessitates context from the business. A solution designed for one industry may not be suitable to another, and no two businesses are alike. Creating a shared context aids in getting the project off to a good start.
2. Involvement of Stakeholders
More than just identification, we find that projects perform best when key stakeholders are involved. Stakeholders are far more likely to collaborate to produce a better overall solution when they have “skin in the game.”
3. Problem Identification
A critical stage in which business problems are defined and prioritized. It is worthwhile to take the time to get this right, as any subsequent effort may be ineffective and wasteful.
4. Interviews
Because data scientists and machine learning engineers are already experts in their fields, interviewing them to learn what they do, how they work, and what the models they’ve created do is an important step in the process.
5. Model Evaluation
In some AI consulting projects, reviewing ML models is the primary duty. Here, we work with you to assess your present models. We consider performance, toil, risk, and a variety of other important factors.
6. Solution Evaluation
Given our understanding of the problem and the processes and models involved, we can now question whether the problem is being solved. If not, we will figure out why and provide actionable advice on how to correct it.
7. Problem Solving
If necessary, we provide specifics on what needs to be done to resolve the issues you are experiencing with this particular problem.
8. Strategic Approach
We frequently encounter larger, strategic-level concerns that must be addressed for optimal ML usage. We provide these solutions to relevant stakeholders throughout this phase. Many projects then proceed to the implementation phase. At times, we take a step back and perform another iteration to address a different issue.
