How to Hire Help For Your NLP & Machine Learning Projects?

While big tech companies have fully staffed Natural Language Processing (NLP) and Machine Learning engineers, your company may be at the cusp of joining the A.I. revolution. At this stage, you may not be ready to hire a brand new team, yet you don’t want to miss out on the opportunity to build some A.I. capabilities around your products and services.

So the question is, what are some ways to hire NLP and Machine Learning help and how can you do this in a cost-effective way? Here are 4 ways you to get your Natural Language Processing and Machine Learning projects going without hiring a whole new team where you’ll have to spend time searching for the right set of candidates, interviewing, and working out a budget for full-time compensation. Not only that, you’ll also have to figure out how to manage the people you employ and how to keep the project on schedule.

Machine learning consulting, artificial intelligence consulting, natural language processing consulting
How to build out your machine learning and NLP capabilities.

Note that while each of these may not be a perfect solution, it is a way for you to actually get projects off the ground. If you happen to work with the right team or person, you can actually stick to the strategy that works until you feel you are ready to grow a team internally and your budget allows for it.

If you think about tapping into external help (e.g. consultants &  freelancers), the two most important things to watch out for are: (1) qualifications – do they have the skills for the type of capabilities that you want to build and (2) reliability – will they do what they say they’ll do. If these two concerns are well addressed, then the cost is just a matter of scoping and negotiation.

In this information age, almost anyone with a software engineering background can read up a couple of tutorials and start implementing machine learning projects with starter code from GitHub. Unfortunately, it’s not as easy as that.  It takes a lot more experimentation to produce results that actually make sense and can be used within an actual product or service workflow. Knowing what experimentation to do and how to get real results requires years of experience and training in the subject matter. So, it’s important to do your due diligence before you engage external help.

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