Whether you’re working on a specific project or require ongoing advice in the field of AI and machine learning, it’s important to choose the right consulting firm to suit your requirements.
From determining strategies to delivering a seamless final product, you have to find an AI partner you can trust.
Why do I need an AI consulting firm?
As an organisation, you may be keen to harness the power of AI, but without the right strategies and skills in place, you will most likely waste precious time and money with little result.
From developing a proof-of-concept for stakeholders through to brainstorming solutions to complex problems and delivering an end product that makes the best use of AI and machine learning, a great consulting firm will make or break your AI venture.
While the power and potential of AI continue to grow, many firms are still hesitant to implement even the low complexity and high impact AI use cases. However, businesses hold high expectations for AI, even though over half are yet to adopt the most essential use cases.
What does an AI consulting firm do?
Many industries are already embracing the power of machine learning to drive predictive analytics and processes, including healthcare and even law enforcement. Whatever your industry, with the right AI consulting firm by your side, you can take your project to new heights.
If your project requires an element of prediction, you will need a data science agency to keep you on track.
AI consultants will then apply their supervised and unsupervised machine learning knowledge to create several model prototypes. These initial prototypes will test whether it is possible for you to make accurate predictions given your existing data.
An AI consulting firm will support your organisation in a number of ways, including:
Understanding how the strategic implementation of AI can help meet your business goals and drive commercial growth is half the battle. AI consultants are the experts, so they can take their skills and knowledge in the fields of AI and machine learning to identify the key areas of improvement within your business.
Typically, the AI strategy development stage starts with a period of research and understanding. The consultants will work with you to discern any pain points or bottlenecks whereby the implementation of the right AI solution could save your business time and money.
This is also the stage in which your AI consultant will work to deliver a proof of concept or MVP to demonstrate the value to stakeholders. Depending on the structure of your organisation and the attitudes towards AI, your consultant will work to deliver a tangible and scalable product that will prove the absolute necessity of AI technology in your business.
Once you have determined the key areas where AI and machine learning could transform your organisation, it’s time to develop a comprehensive implementation plan. Your AI consultant will source the skills and tools required to implement your transformative AI strategy to meet the goals and deadlines outlined in the strategy segment.
It’s up to your machine learning company to ensure the appropriate measures are in place for maintaining & scaling your solution in-house.
This will most likely involve:
- Data science training.
- Access to the data infrastructure.
- A hand over of the login credentials and a visual mind-map of the proposed data architecture. This sill help you to understand how the AI system operates at from a birds eye view.
Signs of a great AI consulting firm
Now you’ve determined whether you do (or don’t) need AI consultants to turn your concept into a reality, let’s explore the characteristics of a data science company:
High Quality Documentation
Your AI consulting firm will need to provide thorough documentation on the following aspects of the project:
- The model + data implementation phase: This will show you what systems, data sources, APIs and cloud resources they will need access to in order to implement your AI solution.
- Code documentation: All of the code, functions and APIs that your data science firm will build need to be thoroughly documented. This ensures that when it is handed over to your in-house team they will be easily able to make changes and to integrate the model’s predictions with their own tools.
Hiring a consulting firm that has a diligent documenting process, ensures you will have a clear view of not only what the project entails but also a clear understanding of how it can fit into your existing IT infrastructure.
Test Driven Development
A good machine learning company will use test-driven development and unit testing. This basically means that all aspects of the data science project will be thoroughly tested and that all of the functions and machine learning models will work on a consistent basis.
For example if you have an API that has 15 unique /routes/, then all of these will be tested daily with Unit Tests.
Implementing unit testing is seen as best practice within the software engineering industry. A data science firm will build your solution to be robust and easily tested.
Object Orientated Programming
An object orientated approach to your data and domain problems allows your code to be:
- Easily re-factored.
- More maintainable.
- Reduces the risk of creating spaghetti code.
A good firm will usually write or have lots of class wrappers that sit between your data and modelling process. This ensures that the code and data structures can be easily changed without heavily affecting the data pipelining.
A Cloud First Strategy
Your machine learning firm needs to be proficient in a cloud IT solution such as:
Additionally, the machine learning company needs to understand when to use serverless technology, when to use virtual machines and also, how to correctly select the right database to serve your specific use-case.
A Well Designed Machine Learning Process
The machine learning process can be broken down into four distinct stages:
- EDA – exploratory data analysis.
- Model training + feature engineering.
- Model development.
- Model maintenance.
A good machine learning company knows how to create a minimum viable prototype whilst still demonstrating to your internal stakeholders that the full solution can be beneficial.
It is not unnatural for your AI firm to trial feature engineering and initial model building to determine whether the final project would be even be feasible.
If you don’t have the relevant data sources that help to predict your variable (in either classification or regression), then throwing additional data or resources at the problem will not help.
A good company will be honest enough and will tell you to:
- Find additional data sources.
- Blend extra data sources in order to create more predictive features.
- Improve the quality of your data.
- Find different ways where machine learning can add value to business.
Highly Selective With Projects
In the same vein as our last point, honesty is key to a good relationship with your AI consulting firm. Choose a machine learning partner who is not afraid to push back. You’re hiring them for their expertise, after all, so let them lead the way in what they do best. If you worry that they’re simply nodding, smiling and awaiting the next paycheck, then you’ll probably not get the best out of that relationship.
Strong Team Players
The difference between hiring an AI consultancy firm and simply farming your project out is collaboration. When you hire consultants to support your AI and machine learning strategy and delivery, you are essentially extending your team.
Whichever machine learning consulting firm you choose to support your AI project, be sure to consider these pointers before sealing the deal.
When choosing an AI consulting firm, pick a partner who doesn’t pull the wool over your eyes. The best consultants will treat your business as if it were their own, and they’ll recognise the importance of plain and simple communication.
Hopefully you’ve gained the necessary insight into how to hire a good data science company. Several questions that you may want to consider asking the company could include:
- How much unit testing will be involved?
- Please could you map out the cloud architecture and model deployment process and share it with us?
- Will hyper-parameter tuning be included?
- Will the API code and deployment be documented and if so in what form?
- What data will you need from us?
- How many hours will it take to create an initial prototype?