Machine Learning Consulting Services
Business can use machine learning to predict demand, identify abnormal behavior, automate decisions, enhance recommendations and analyze vast quantities of data.
However, choosing where to apply machine learning can be more challenging than selecting a model.
Might have a wealth of data, but not sure whether it‘s service-ready. Have an AI concept, but no concrete business proposition. Construct a functioning model, but find difficulty implementing it.
This is where the machine learning consulting services can be a great assistance.
The role of a ML consultant is to engage with an organization to find real ML solutions, assess the data and technology present, plan an approach, and support development and deployment.
The aim isn‘t just to include AI.
The aim is to address a real business challenge that runs through the system.
Table of Contents
What Are Machine Learning Consulting Services?
Machine learning consulting to advise organizations designing and deploying machine learning applications
Depending on the project, consulting can cover:
- The strategy of AI and ML.
- Use-case discovery
- Data readiness
- Model selection
- ML architecture
- Prototype development
- Production planning
- MLOps
- Integration
- Monitoring
- Team training
Google Cloud, for instance, characterises its AI readiness consulting as being about understanding business goals, evaluating capabilities, benchmarking maturity, and designing an AI roadmap.
The exact scope will vary according to the business and the kind of machine learning problem.
What Does a Machine Learning Consultant Do?
The beginning is different for a machine learning consultant, they tend to start from the business problem instead of the technology.
Suppose a retailer says:
We seek to utilize AI for enhancing inventory planning.
A consultant may ask:
- What are the products that are being forecast?
- Is any historical record of the sales?
- How frequently does demand change?
- Essentially, what other factors will determine sales?
- Are the forecasts available today reliable?
- Where will this prediction be applied?
- What if the model is not right?
They are also useful in narrowing the general idea of an AI to a particular ML project.
Main Services Offered by ML Consultants
1. Machine Learning Strategy
Initial consideration usually involves identifying who gains from machine learning.
A consultant could suggest a list of possible use cases and then rank them by business value, technical feasibility, the data available for a use case, the cost and the risk..
Enabling the company not to choose to implement an ML project just because AI is on the booming.
2. Data Readiness Assessment
There is no substitute for good data in machine learning.
Consultants may review:
- Data quality
- Data availability
- Missing values
- Labels
- Data sources
- Data permissions
- Data pipelines
- Historical coverage
The former may even have to be dealt with prior to model building: so if the data is inconsistent or incomplete, this should be rectified.
3. Model and Architecture Planning
After we understand what the use case is, we could then determine what type of machine learning approach should be used.
Depending on the problem, this could involve:
- Classification
- Regression
- Forecasting
- Recommendation systems
- Anomaly detection
- Natural language processing According to a U.S. Department of Labor report, this branch of linguistics “aims to develop tools that analyze, understand, and generate human language.” These tools include: automatic translation, reliable information retrieval, and speech recognition and synthesis.
- Computer vision
Furthermore, the consultant can also advise on the best location for running the model and how it will connect with the current systems.
4. Machine Learning Development
Briefly, some consulting projects also involve real model development.
Prepare data, create features, train the model, evaluate the performance, compare the methods by the team.
The AWS ML consulting offerings that encompass the entire ML lifecycle from strategy and data engineering to model development, deployment, and MLOps are listed by AWS.
5. MLOps and Deployment
It‘s not just about having a good model. Implementing it in the notebook is a different matter than deploying it a real business.
Production ML often needs:
- Automated pipelines
- Version control
- Model testing
- Deployment processes
- Monitoring
- Drift detection
- Retraining
Description of MLOps tools: One example of MLOps tools,The AWS documentation itself describes the SageMaker MLOps tools as providing automation and standardization across train, test, deployment, govern, and monitor.
Thus, a consulting team can get involved not just in the development of the model but also in helping to establish the needed infrastructure.
Common Machine Learning Consulting Use Cases
Predictive Analytics
Past data can be used by organizations to predict the future.
Examples include:
- Sales forecasting
- Demand forecasting
- Customer Churn Prediction Standard prediction methods fail when applied to highly imbalanced classification problems, such as customer churn prediction, as the characteristics of the tumbling customer population change call volumes decline, and the identification of potential lost customers becomes more difficult.
- Risk scoring
Recommendation Systems
Machine learning is useful for recommending products, content, and services based on customer behavior or other signals.
Fraud Detection
Models can be used to detect abnormal patterns and transactions and escalate activity.
Computer Vision
Companies can use image-based ML for:
- Quality inspection
- Object detection
- Image classification
- Visual monitoring
Natural Language Processing
NLP systems can process text for tasks such as:
- Document classification
- Information extraction
- Sentiment analysis
- Customer message routing. Rather than receiving messages and filter them by their own preferences, customers will subscribe for their preferred interface routings onto the messaging system.
- Search
Predictive Maintenance
Sensor data or equipment data can be used to find the tendency table of suspect failure pattern.
Machine Learning Consulting Firm vs Internal Team
Certain companies develop the systems in-house.
Some also collaborate with a machine learning consulting company.
| Internal ML Team | Consulting Firm |
|---|---|
| The long-term internal capability of the company. | Specialist knowledge from outside the author. |
| Detail knowledge of company systems | Experience on several projects |
| The most sophisticated system in this scope.3 Staff for the implementation of the new system is necessary as it is very complex.3 Moreover a high level of knowledge in the field of machine learning is required. | Can offer specialist advice on request. |
| Internal ownership | Helpful for targeted projects or, in the most part, change work |
A hybrid model is also often used.
A company could have an internal engineers, and use consultancies for architecture, cutom modeling, MLOps or a specific industry problem.
The appropriate decision has to be based on analysis of the company‘s current know-how, the schedule that is to be used, the budget and strategy of the company.
How the Machine Learning Consulting Process Works
A normal engagement can be broken down into a series of phases as follows:
Step 1: Discover the Problem
First, state clearly what business problem you are solving and what you are trying to achieve.
Step 2: Assess the Data
Assess the sufficiency of what the data can provide.
Step 3: Prioritize the Use Case
Select a problem with a sound business case and a measurable result.
Step 4: Build a Proof of Concept
Having a system in a working state allows you to test the approach before investing a large sum.
Step 5: Develop the Solution
Train, test, optimize and integrate the model.
Step 6: Deploy
Deployed the solution into the live environment.
Step 7: Monitor
Monitor model performance and usage over time.
En CompTIA 2017 AWS Consultation offerings describe thesekindsoffering (defining the use-case, preparing the data, developing the model, deployingthedigit,providingmonitoring) in the same way.
Benefits of Machine Learning Consulting
Access to Specialist Skills
A consultant can also provide they have the knowledge that might not be inside the company!
Faster Project Planning
Experienced consultants can identify technical requirements and potential problems earlier.
Better Use-Case Selection
The consultants can also differentiate ideas that are realistic ML opportunities from outlandish ideas that would probably offer little value.
Production Guidance
An excellent consulting engagement should deploy, monitor, secure and maintain and not just halt at a prototype.
Team Enablement
Certain consulting models provide training and knowledge transfer so that internal employees will be able to maintain the solution. Services offered through AWS Marketplace consulting include team training and support.
How Much Do Machine Learning Consulting Services Cost?
No standard price.
The cost depends on how extensive the project is
Factors can include:
Project Complexity
It‘s important to realize that a simple forecasting project is very different from a big computer vision system.
Data Requirements
Cleaning, labeling, aggregation, and formatting of data may involve a lot of engineering effort
Integration
The integration of the ML system with existing applications, API‘s, databases, or internal systems adds development effort.
Infrastructure
Cloud computing, model serving, storage, monitoring and data pipelines can also result in ongoing costs.
Support
Particularly for some companies a pre-bid consultation is sufficient. For other companies monitoring, and constantly updating the model would be required.
A good idea is to start with an well-scoped discovery or pilot phase in front of a larger scale production estimate.
How to Choose a Machine Learning Consulting Firm
Selecting the right consulting partner is not just as simple as verifying if he implemented AI.
Look at Relevant Experience
Is this a problem you‘ve solved before? Ask this questions.
Understand Its Process
The team should be able to communicate its approach to data, modeling, testing, deployment, and monitoring.
Ask About Production
Verify whether the company not only builds prototypes but also provides in-production ML systems support.
Check Integration Skills
The model must be compatible with your current technology.
Clarify Ownership
Be aware of the ownership of all artifacts such as code, model, documentation, pipeline, etc.
Discuss Support
Inquire about the next step after deployment.
Depending on the project, machine learning models may require sustained monitoring as observed data evolves.
Questions to Ask an ML Consultant
Before starting a project, ask:
What business problem are we addressing?
Define success. How will success be measured?
Is the data we currently have enough?
Which model do you suggest and for what reason?
How will the model be evaluated?
How will it fit in with the rest of our systems?
Who will support and update the model post-launch?
How will you measure/monitor your performance?
These questions are meant to uncover whether the proposed project is viable.
Common Mistakes
Starting With AI Instead of the Business Problem
Do not begin with:
We require a machine learning algorithm.
Begin with:
This business process requires improvement.
Ignoring Data Quality
Data that is of high quality may be wasted on a simple model, whereas low quality data could corrupt a complex model.
Stopping at the Prototype
A successful demo doesn‘t necessarily mean the system is ready for production.
Forgetting Monitoring
The increase in the performance of a model when the way that people behave in the world changes.
Choosing Based Only on Price
The client can choose the cheapest consulting answer, though it might not deliver the architecture, integration, or long term support needed.
FAQs
What are machine learning consulting services?
It refers to the professional services that assist businesses in identifying, planning, constructing, deploying, integrating and managing machine learning solutions.
What does a machine learning consultant do?
A consultant is able to: evaluate a business use case, review data, come up with an architecture for the ML, guide model development, plan deployment, design MLOps and Monitoring.
Why hire a machine learning consulting firm?
When businesses decide to bring in a consulting firm, it is because they lack the specialized ML expertise, they need assistance with a particular project, they seek external help, or they require support in moving from experimentation to production.
Are machine learning consultants only for large companies?
No. The scope of work is flexible to the organization. With a smaller, perhaps one employee business, there may only be a need for one forecasting or recommendation project. A larger organization may need an enterprise ML approach.
How long does an ML consulting project take?
It all comes down to the scope. A discovery engagement or proof of concept can be fairly narrow, but a production system where you have data engineering, integration, deployment, monitoring etc could take a lot longer.
Does machine learning consulting include model development?
Yes,. Some consulting projects are limited to strategy and planning. Some others work through the entire lifecycle, from data extraction and model building through to deployment and monitoring.
Final Thoughts
Here are the services that consulting in machine learning can offer to give an enterprise an AI project direct implementation to implement a real machine learning system.
The work can contain strategy, data assessment, selection of use-cases, model development, architecture, deployment, MLOps, integration, and monitoring.
Great consulting projects always stem from the business problem.
They wonder if there are enough data to support the goal, define where success lies, experiment with a small project, and what happens after deployment.
What to look for in an ML consulting company,: relevant experience, development process defined, production experience, integration expertise, clear ownership, post launch support.
Machine learning is more than just constructing a model.
Building something that will work efficiently within a real business.
This guide is part of our Machine Learning Development Services cluster. You can also explore custom machine learning development services, machine learning model development, machine learning application development, machine learning integration services, and how to choose a machine learning development company.
