Building an AI team is hard. Many times, it’s best to hire an outside team to build your AI solution.
Still, the downsides to consulting firms are also well-known:
- They’re famous for making things more complex than they need to be.
- They can struggle to meet deadlines and stay on budget, or even
- Fail to deliver on their promise of a working ML solution altogether.
It’s hard to separate a top consulting firm from an upstart when you yourself are just getting started with machine learning. This guide will help you find a consultancy that is worth working with.
In this article, you’ll see
- 9 things to look for in an ML consultancy,
- The questions you need to ask yourself before reaching out to firms, and
- Tips to avoid the common mistakes that lead to failed or overly expensive projects.
9 things to look for in a machine learning consultancy
1. They make machine learning simple
Your machine learning consultancy should be able to explain how machine learning works in language that you and your team can understand.
What is machine learning? If you use AI today then it’s almost always about building a system that can take data and turn it into some prediction.
To be able to make these predictions, Machine learning algorithms find hidden patterns that connect the data to the correct prediction, by looking at lots of training examples (that’s why you need lots of data to make AI work).
For an excellent visual illustration of how a model trains, finds patterns and then makes predictions check R2D3’s a visual introduction to machine learning.
2. They’re quick on their feet
An AI consulting firm that’s worked on enough projects can tell you whether your idea is promising in minutes, not days or weeks.
In 90% of cases, an experienced agency can give you a “Yes, this could work,” or a confident “No” within ~15 minutes of you explaining your problem, goal and available data.
They can’t guarantee you’ll get meaningful results at that point, but they should be able to tell you which ideas are worth pursuing. You’ll still need to build out a proof of concept to validate that what you want to predict can in fact be predicted accurately with your data – and that should take no more than 8 weeks.
3. They’re not afraid to tell you “no”
Is a machine learning consulting firm ready to work on every idea you give them?
Or do they guide you to the best solution, even if it’s not the one you expected?
Many machine learning ideas are not a good idea upon closer look. If you don’t have the right data, or there is no clear path to a solution – even for experts – then they should tell you.
Likewise, not every project will require AI.
We regularly have clients come to us for machine learning consulting when a simpler system is likely to get them 90% of the results. In these and other cases we often recommend against using machine learning and point them in the right direction to do it themselves or with a standard software development consultancy.
Not every project is possible. Even fewer are worth pursuing.
Seek out an ML agency that will tell you which ideas to toss out early and focus on the use cases that will bring you results quickly.
4. They’re transparent about their experience
Don’t let an AI consultancy hide behind NDAs. Privacy is a top priority in the world of data science, but a respected AI firm should still have permission to point you to some specific projects and talk about some real names and numbers.
We have NDAs with all of our clients, but we can still tell you who we worked with, what problem we solved and what the impact was.
These cases should come with names and contact details of the project managers of the companies they worked with.
Choose a trusted AI agency that has strong references and gives you insights into specific projects they’ve built, how they approached those projects, and what results they accomplished. Be sure to ask if those projects are research or POC, or if they are actually deployed for use in their clients’ everyday business.
5. They ask the right questions
Did you just leave a meeting feeling like they asked all the right questions? Did they ask follow-up questions and dig and probe until they got right to the heart of your problem?
To get your project right, your AI consulting firm needs to learn as much as possible about your business and your domain—what you care about, your vocabulary, what your data looks like, and why what you want to predict is meaningful to you.
They need to be able to repeat your goals back to you in a way that sounds right to you.
Over the course of a machine learning project, the consulting team will make critical decisions that will determine the outcome of your project. They’ll also bring you ideas or make suggestions for improvement. They can only do these things effectively if they truly understand your business and your goals.
Your consultancy should be more focused on understanding your problem than selling you their services.
Small miscommunications in the beginning can lead to expensive, hard to fix, mistakes later on.
6. They’re generous with their knowledge
Your machine learning consulting agency should be upfront about everything—like how much effort it will take, which technologies they’ll employ, and what techniques they’ll use to get there.
In our first calls with potential clients we give specific feedback. We assess whether they have the right data and sketch a plan for how they can go from idea to finished solution – including the algorithms, setup, and the budget they will need to get there.
There are very few “secrets” in machine learning. It’s not a secret that you’ll use convolutional neural nets for automated radiology exams, or that hybrid recommenders work best in recommending content from a frequently updated library (like Netflix or news sites).
The real value of a consultancy is not in their ideas, but in elegantly solving the hundreds of small problems they are sure to run in – on the way to building a solution that solves the right problem.
7. They’re serious about “process”
“We’ll do whatever it takes” isn’t good enough. Ambition should always be backed by experience.
An experienced ML or AI consultancy will have a proven process – fine-tuned over many projects. Otherwise the chances are very slim that they will finish the project within the time and budget you originally planned.
8. They have a technical project manager
Machine learning projects are different from standard software projects and it takes a project manager (PM) with both communication skills and technical know-how to keep your ML project on schedule.
In a machine learning project, it’s impossible to separate the project management role from the role of the engineering manager, because how your data is selected and combined directly impacts whether your solution produces the results you want.
That’s why one of the people you talk to daily should be the same person who is writing the tasks for the engineering team. And that person must also have the communication skills to give you progress reports that you can understand.
We found that a senior machine learning engineer can take that role. In addition our CEO and CTO is overseeing every project to constantly ensure we are keeping true to our clients' actual goals.
9. They help make your team better
A good consulting firm keeps you in the loop and helps your team grow.
Most likely your project managers and engineers want to learn as much as possible from the project. So pick a consultancy that will work openly with your team rather than building a solution in isolation.
Your consulting firm should clearly document the requirements before the project, regularly push code to a code repository that you can access and give you frequent updates on so that your team can learn, ask questions and – which is crucial – course correct if necessary.
3 questions to ask yourself before choosing a machine learning consultancy
Now that you have an idea of what to look for, here are a few questions to ask yourself about what YOU want from your machine learning consulting firm:
1. Do you need an AI strategist, or a builder?
You’ll find two main types of consultancies working in AI: strategists and builders.
Almost all consultancies do both of these things at some level, but very few firms do both well.
Machine learning strategy consulting:
Teams that work on projects that revolve around developing an AI strategy that fits into your corporate vision. Their employees are more likely to have experience as corporate consultants rather than engineers. They tend to focus on developing a comprehensive strategy rather than a working solution. They most likely don’t have the engineering power to execute AI products end-to-end, and they often outsource the development that they offer or work on PoCs with students.
Machine learning building consulting:
A consulting firm that focuses on building means more engineers and fewer career consultants. That’s not to say they don’t value strategy, but their approach is to identify the areas where they can make the biggest difference, and then to do the work.
In our case we are focused on building, and our staff is 90% engineers. So we can produce a proof of concept in weeks and fully-functional AI products in months—much more quickly than a strategy-focused firm.
Nothing builds confidence in the long-term viability of AI like putting a functional AI system in place quickly. Even skeptical board members become convinced once you start tapping lost revenue from your customer data or trimming 80% of analysis time out of a critical workflow.
We’ve seen that the momentum from just one early win with AI can kick-start the adoption of AI-driven strategies company-wide.
2. What do you want to pay for? (results vs. hours)
Most consulting firms want to work by the hour or day because it limits their risk and shifts the risk to you, the client.
If a project takes twice as long as estimated or never reaches a successful conclusion, it’s not their problem. The consultancy gets paid more despite your frustration.
Only consultancies that are confident in their assessment of both your problem and their own skills can offer you the alternative: fixed project pricing.
Project pricing moves your risk to the consultancy. If they don’t deliver the results you agreed upon, then they don’t get paid. And if it takes longer or requires more effort than expected, then the extra cost falls on them, instead of you.
3. Can you get by with SaaS, or do you need a custom machine learning solution?
You have 3 options for building a solution with AI or machine learning:
1. AI SaaS services
Example: Google’s Speech-to-Text API.
These services are good building blocks that you can build into an application. If hundreds or thousands of other businesses have exactly the same AI challenge as you (like turning English audio to text) then it’s likely that there is an API out there that solves this problem better than you can with reasonable effort.
2. Machine Learning as a Service Platforms
Examples: Cloud platforms like Amazon SageMaker, IBM Watson or Microsoft Azure
These platforms promise to make any machine learning project easier to build but our experience is that they only work for straightforward use cases. And surprisingly few ML applications turn out to be straightforward enough to fit into that model.
3. Build your own
If you want to use data sets that are unique to you to solve a complex problem, then in most cases you need to build your own custom ML or AI solution.
Fortunately, best practices for planning, building, and scaling applications are readily available. That, combined with open source machine learning libraries and accessible cloud compute power (like AWS EC2) are making building custom ML products easier every day.
Beware of “superior algorithms”
A final note of caution: People selling AI or machine learning consulting services based on their “proprietary algorithms” either don’t know what they’re doing, or they’re not being honest about what matters to succeed in a project.
After all, if algorithms are such a big secret, then why would Google and Amazon make so many of their best algorithms public?
Your data, your domain knowledge, and how your project is executed is what makes the real difference.
Here are the 3 most important elements that actually determine machine learning success:
- Understanding: internalizing your data, problems, and goals;
- Engineering: writing well-documented, easy-to-scale code;
- Infrastructure: building a rock-solid infrastructure that can handle your volume of requests.
Pay for expertise, not algorithms.
Find a machine learning consulting firm that is 100% transparent about their experience, their skill set, and their process. They should speak your language, ask you a ton of questions, and tell you what they’ll do in a way that makes sense to you.
Don’t let them hide behind AI terminology. If you don’t understand how something works, keep asking questions until you do. And if they can’t make it clear for you, find another consulting firm who can.
If you don’t find a machine learning consulting firm that fits your criteria – we are builders who focus on taking complex and large scale AI projects from idea to production – if that’s what you are looking for, let’s talk.