Estimating the Time, Cost, and Deliverables of an ML App Project

Imagine yourself going to buy a customized wallet in a store.

Though you are aware what type of wallet you need, but don’t know the cost or time taken to get the customized version.

Same is the case with machine learning projects. And to help you out with this dilemma, we have provided detailed information for you to have a successful project.

Machine Learning is like a coin that has two-sides. 

On one side, it helps to eliminate uncertainties from processes. But on the other side, its development is full of unsurity. 

While the end result of almost every Machine Learning (ML) project is a solution that makes businesses better and processes streamlined; the development part of it has a completely different story to share. 

Even though ML has played a massive role in changing the profit story and business model of several established mobile app brands, it still operates under nascency. This newness, in turn, makes it all the more challenging for mobile application developers to handle an ML project plan and make it production ready, keeping the time and cost constraints in mind. 

A solution (probably the only solution) to this difficulty is black and white Machine Learning app project estimate of the time, cost, and the deliverables. 

But before we head on those sections, let us first look into what makes the difficulty and burning of the night candles worth it. 

Why Your App Needs A Machine Learning Framework?

You might be thinking how come we are talking about framework in the middle of time, cost, and deliverables estimations.

But the real reason behind the time and cost lies here, that informs us about our motive behind app development. Whether you need machine learning for:

For Offering Personalized Experience

For Incorporating Advanced Search m

For Predicting User Behaviour

For Better Security 

For Deep User Engagement 

Based on these reasons, the time, cost and deliverable will depend accordingly. 

Types of Machine Learning Models

What type of model would you consider to adjust the time and cost? If you don’t know, we have provided information for you to understand and choose models, depending on your requirements and budget.

Machine learning amidst its different use cases can be categorized into three model types, which play a role in turning rudimentary apps into intelligent mobile apps – Supervised, Unsupervised, and Reinforcement. The knowledge of what these Machine Learning Models stand for is what helps define how to develop an ML enabled app. 

Supervised Learning 

It is the process where the system is provided with data where the algorithm’s inputs and their outputs are labeled correctly. Since the input and output information are labeled, the system is trained to identify the patterns in data within the algorithm. 

It becomes all the more beneficial for it is used to predict the outcome on the basis of future input data. An example of this can be seen when social media recognizes somebody’s face when they are tagged in a photograph. 

[Also Read: What is data labeling in machine learning and how does it work?]

Unsupervised Learning 

In the case of unsupervised learning, the data is fed in the system but its outputs are not labeled like in the case of supervised model. It allows the system to identify data and determine patterns from the information. Once the patterns are stored, all the future inputs are assigned to the pattern for producing an output. 

An example of this model can be seen in cases where social media gives friends suggestions on the basis of several known data like demography, education background, etc. 

Reinforcement Learning

 Like in the case of unsupervised learning, the data which is given to the system in reinforcement learning is also not labeled. Both the machine learning type differs on the ground that when correct output gets produced, the system is told that the output is right. This learning type enables the system to learn from the environment and experiences. 

An example of this can be seen in Spotify. Spotify app makes a recommendation for songs which the users then have to either give a thumbs up or thumbs down. On the basis of the selection, Spotify app learns users’ taste in music.  

[Also Read: Machine Learning Recommendation Systems: Types, Applications, Real-Life Examples,  Implementation Techniques & Costs]

Lifecycle Of A Machine Learning Project

The lifecycle of a Machine Learning project deliverables timeline usually appears like this –

ML Project Plan Setup 

• Define the task and requirements
• Identify the project feasibility 
• Discuss the general model tradeoffs
• Create a project codebase

Collection and Labeling of Data 

• Create the labelling documentation 
• Build the data ingestion pipeline 
• Validation of data quality 

Model Exploration 

• Establish the baseline for model performance 
• Create a simple model with initial data pipeline 
• Try parallel ideas during the early stages
• Find the SoTA model for the problem domain, if any, and reproduce results. 

Refinement of Model

• Do model-centric optimizations
• Debug models as complexity gets added
• Conduct error analysis for uncovering failure modes.

Test and Evaluate

• Evaluate the model on test distribution 
• Revisit the model evaluation metric, ensuring it drives desirable user behaviour 
• Write tests for – model inference function, input data pipeline, explicit scenarios expected in the production. 

Deployment of Model 

• Expose the model through REST API 
• Deploy the new model to a subset of users to ensure that everything is smooth before the final rollout. 
• Have the ability to roll back the models to its previous version 
• Monitor the live data. 

Model Maintenance

• Retrain the model for preventing model staleness
• Educate the team if there is a transfer in the model ownership

How to Estimate the Scope of a Machine Learning Project?

The Appinventiv Machine Learning team after perusing the Machine Learning type and the developmental lifecycle goes on to define the Machine Learning app project estimate of the project following these phases:

Phase 1 – Discovery (7 to 14 days)

The ML project plan roadmap begins with the definition of a problem. It looks into the issues and operational inefficiencies which should be addressed. 

The goal here is to identify the requirements and see if Machine Learning meets the business goals. The stage requires our engineers to meet with the business people on the client side to understand their vision in terms of what issues they are looking to solve. 

Secondly, the development team should identify which kind of data they have and if they would need to fetch it from outside service. 

Next, developers have to gauge if they are able to supervise algorithms – if it returns the correct response every time a prediction is made. 

Deliverable – A Problem Statement which would define if a project is trivial or or would be complex. 

Phase 2 –  Exploration (6 to 8 weeks)

The goal of this stage is to build upon a Proof of Concept which can then be installed as API. Once a baseline model is trained, our team of ML experts estimate the performance of the production-ready solution. 

This stage gives us the clarity on what performance should be expected with the metrics planned at the discovery stage. 

Deliverable – A Proof of Concept 

Phase 3 – Development (4+ months)

This is the stage where the team works iteratively till they reach a production ready answer. Because there are far less uncertainties by the time the project reaches this stage, the estimation gets very precise.  

But in case the result is not improved, developers would have to apply a different model or rework on the data or even change the method, if needed. 

In this stage, our developers work in sprints and decide what is to be done after every individual iteration. The outcomes of every sprint can be predicted effectively. 

While the sprint outcome can be predicted effectively, planning for sprints in advance can be a mistake in case of Machine Learning, for you will be working on uncharted waters.

Deliverable – A production ready ML solution

Phase 4 – Improvement (continuous)

Once deployed, decision makers are almost always in a hurry to end the project to save costs. While the formula works in 80% of the projects, the same doesn’t apply in Machine Learning apps. 

What happens is that the data changes throughout the Machine Learning project timeline. This is the reason why an AI model has to be monitored and reviewed constantly – to save it from degradation and provide a safe AI enabling mobile app development. 

The Machine Learning centered projects require time for achieving satisfying outcomes. Even when you find your algorithms beating the benchmarks right from the beginning, chances are that they would be one strike and the program might get lost when used on a different dataset.

Factors That Affect The Overall Cost

The way to develop a machine learning system has some distinguishing features such as data related issues and performance related factors which decide the last expense.

Data-related Issues

The development of reliable machine learning depends not just on phenomenal coding, but the quality and quantity of the training information also plays a crucial part.

1. Lack of Suitable Data
2. Complex Extract, Transform, Load Procedures
3. Unstructured Data Processing

Performance-related Issues

The adequate algorithm performance is another important cost factor, as a high-quality algorithm requires several rounds of tuning sessions.

1. Accuracy Rate Varies
2. Performance of Processing Algorithms

How We Estimate the Cost of a Machine Learning Project?

When we talk about the estimation of the cost of a machine learning project, it is important to first identify which project type is talked about. 

There are majorly three types of Machine Learning projects, which hold a role in answering How much does Machine Learning cost:

First – This type already has a solution – both: model architecture and dataset already exists. These types of projects are practically free, so we won’t be talking about them. 

Second – These projects need fundamental research – application of ML in a completely new domain or on different data structures compared to mainstream models. The cost of these project types are usually one which the majority of startups cannot afford. 

Third – These are the ones we are going to focus on in our cost estimation. Here, you take model architecture and algorithms which already exist and then change them to suit the data you are working on. 

Let us now get to the part where we estimate the cost of the ML project. 

The data cost 

Data is the primal currency of a Machine Learning project. Maximum of the solutions and research focuses on the variations of the supervised learning model. It is a well known fact that the deeper the supervised learning goes, the greater the need for annotated data, and in turn, the higher is the Machine Learning app development cost.

Now while services like Scale and Amazon’s Mechanical Turk can help you with gathering and annotation of data, what about Quality? 

It can be extremely time consuming to check and then correct the data samples. The solution to the issue is two faced – either outsource the data collection or refine it in-house.

You should outsource the bulk of the data validation and refinement work and then appoint one or two people in-house for cleaning the data samples and labeling it.

The research cost 

The research part of the project, as we shared above, deals with the entry level feasibility study, algorithm search and the experimentation phase. The information which usually surfaces from a Product Delivery Workshop. Basically, the exploratory stage is the one every project goes through before its production. 

Completing the stage with its utmost perfection is a process that comes with an attached number in the cost of implementing ML discussion. 

The production cost

The production part of Machine Learning project cost is made up of infrastructure cost, integration cost, and maintenance cost. Out of these costs, you will have to make the least expenses with the cloud computation. But that too will vary from the complexity of one algorithm to another. 

Integration cost varies from one use case to another. Usually, it is enough to put an API endpoint in the cloud and document it to then be used by the rest of the system. 

One key factor that people tend to overlook when developing a machine learning project is the need to pass continuous support during the entire lifecycle of the project. The data which comes in from APIs have to be cleaned and annotated properly. Then, the models have to be trained on new data and tested, deployed. 

In addition to the points mentioned above, there are two more factors that carry an importance on the estimation of the cost to develop an AI app/ML app. 

Challenges in Developing Machine Learning Apps

Usually, when a Machine Learning app project estimate is drawn, the developmental challenges associated with it are also kept into consideration. But there can be instances where the challenges are found mid-way of the ML powered app development process. In cases like these, the overall time and cost estimation automatically increases. 

The challenges for Machine Learning projects can range from:

• Deciding what set of features would become machine learning features
• Talent deficit in AI and Machine Learning domain 
• Acquiring data sets is expensive 
• It takes time to achieve satisfying results

Conclusion 

Estimating the manpower and time needed to finish a software project is relatively easy when it is developed on the grounds of modular designs and is handled by an experienced team following an Agile approach. The same, however, becomes all the more difficult when you work on creating the time and efforts wise Machine Learning app project estimate. 

Even though the goals might be well-defined, the guarantee of whether or not a model would achieve the desired outcome is not there. It is not usually possible to lower the scope and then run the project in time boxed setting through a predefined delivery date. 

It is of prime importance that you identify that there will be uncertainties. An approach that can help mitigate delays is ensuring that input data is in the right format for Machine Learning. 

But ultimately, no matter which approach you plan to follow, it will only be deemed successful when you partner with a Machine Learning development company that knows how to develop and deploy the complexities in their simplest form.

FAQs about Machine Learning app project estimate 

Q. Why use Machine Learning in developing an app? 

There are a number of benefits that businesses are able to avail with the incorporation of Machine Learning into their mobile apps. Some of the most prevalent ones are on the app marketing front – 

• Offering personalized experience
• Advanced search 
• Predicting user behaviour 
• Deeper user engagement 

Q. How machine learning can help your business?

The benefits of Machine Learning for businesses goes beyond marking them as a disruptive brand. It ripples down to their offerings becoming more personalized and real-time. 

Machine Learning can be the secret formula that brings businesses closer to their customers, just how they want to be approached. 

Q. How to estimate ROI on developing a Machine learning project?

While the article would have helped you in establishing the Machine Learning app project estimate, calculating ROI is a different game. You will have to keep into consideration the opportunity cost in the mix as well. Additionally, you’ll have to look into the expectation that your business has from the project. 

Q. Which platform is better for an ML project?

Your choice of whether to connect with an Android app development company or with iOS developers will depend entirely on your user base and the intent – whether it is profit making or value centric. 

THE AUTHOR

chirag

Blockchain Evangelist