The Freelance and Self contractor economy has seen considerable growth recently. Most of this is attributed to individual consumers; however, a significant spike is from companies.
Many companies are feeling substantial pressure to reduce their number of full-time employment commitments for work that's not related to their core mission. This creates a preference for contractors, consultants, and freelancers instead. This pressure is magnified when the task requires in-depth expertise on a unique topic, for instance, Machine Learning and Data Science (MLDS).
Machine Learning and Data Science is a prime example of such an issue faced by most companies. MLDS can provide useful predictions, insights, and automation, giving MLDS the potential to be a dominant factor in differentiating an organization. However, they are still complex tasks that need expert supervision. This field is continually evolving — and many business leaders aren't familiar with the topic of MLDS, or the intricacies of working with a consultant. This can create a reluctance to partner with them. The following article will address some common concerns that might be hindering you from tapping into this valuable resource.
In general, consultants aren't hired for MLDS projects solely for the assumption that machine learning is more intimate than typical software and data projects. Machine Learning is interpreting data to a degree and at a speed that the human mind cannot fathom. Machine learning is evolving, and more significantly, it's learning. The core of the software is continuously evaluating ever-changing data and the multitude of variables that come with it. MLDS studies the interplay between variables, the previous results, and how those affect predictions. The same machine learning equation can produce remarkably different results with every single iteration.
Hiring a consultant is often the most logical option for most companies. However, leadership might not pursue this consulting partnership due to a variety of important concerns. Here are a few misconceptions explained.
Many consider a consultant to be expensive; however considering the alternative of re-training, purchasing specialized software, and the wealth of hardware required to keep MLDS competitive, the situation changes. This partnership revolves around making a decision, buy all the equipment yourself. Or let someone else manage the complexity and intricacies of this advanced technology while only paying for what you need in smaller increments. In most cases, a consultant can offer greater efficiency than an employee without any long-term costs and additionally without the commitment.
Machine Learning models are a trade secret that can differentiate a company in the market. This can lead to a worry that a consultant might learn too much about the business. Intellectual property rights have always been and will remain a concern any time you enter into a partnership with another company. However, what is so unsettling to most companies about MLDS is the unfamiliarity which leads to a secretive and distrustful lockdown on any information. However, this is not an issue as whenever you enter into a partnership you have a non-disclosure agreement (NDA). A typical NDA protects your rights to customer and operational business information shared with the other party in the contract. The vast majority of what's left is tools, techniques, and expertise for MLDS.
To conclude, Machine Learning and Data Science engagements are the same as any other consulting engagement. And an excellent opportunity to create value.
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