Machine Learning, a subset of Artificial Intelligence, is a two-sided coin. On one hand it helps to eliminate uncertainties from processes and on the other side, its development is full of unsureties.
Though the end result of every Machine Learning (ML) project makes businesses better and processes streamlined, the development part of the solution is completely different.
Machine Learning plays a massive role in earning profit and changing the business model of several mobile app brands. 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.
Probably the only solution to this difficulty is a clear ML app project estimate of the time, cost, and the deliverables.
But before we look upto those sections, let us first look into what makes the difficulty and burning of the night candles worth it.
Why does your app need a Machine Learning framework?
For Offering Personalized Experience
The extent of the answer to What is Machine Learning lies in the benefits that the technology offers to businesses by being a continual learning system. They can help in classifying the users based on the interest by collecting the users’ information and deciding on the app’s look and feel.
Businesses can make use of Machine Learning framework integration to learn –
- Who are their customers
- What do the customers want
- What preferences and hobbies do the users have, etc.
Based on the information, machine learning helps users in classifying and structuring their customers, finding a unique approach for every customer group, and adapting the tone of content.
For Incorporating Advanced Search
Machine Learning solutions make it possible for you to optimize the search functionality in a way that you are able to deliver more contextual results, making search a lot more intuitive and less burdensome for the users.
They also allow businesses to collect all the available information about the customers and rank them according to the best match.
For Predicting User Behaviour
Machine Learning based applications help businesses understand users’ choices and behavioural patterns by looking into different kinds of data:
- Frequency of using app
- Search details, etc.
Using this data, it gets easier for businesses to plan their marketing strategy according to the individual user type.
For Better Security
In addition to being the effective marketing tool, machine learning can also help with the process of streamlining and securing the app authentication. ML powered facilities like voice, audio, and video recognition makes it easier for customers to authenticate with the help of biometric data like fingerprints or face. The machine learning algorithms can also help in identifying and banning suspicious activities on the app, in addition to preventing users from malware attacks.
For Deep User Engagement
Machine learning capabilities empower businesses to offer amazing customer support, endearing features, and entertainment value which gives users the incentive to use apps on an everyday basis.
- Non Time-Bound Support
- Advanced Features Set
- Offering Entertainment
Types of Machine Learning Models
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.
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.
In the case of unsupervised learning, the data is fed into the system but its outputs are not labeled like in case of supervised models. 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.
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 types differ 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.
Snapchat is one of the few successful apps that utilised the full-length capabilities of Machine Learning technology. Filters like 3D Paint in Snapchat are great examples of how augmented reality and ML can be used side by side for enhanced computer vision.
The lifecycle of a Machine Learning project in simple steps:
A. ML Project Plan Setup
- Define the task and requirements
- Identify the project feasibility
- Discuss the general model tradeoffs
- Create a project codebase
B. Collection and Labeling of Data
- Create the labelling documentation
- Build the data ingestion pipeline
- Validation of data quality
C. 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.
D. Refinement of Model
- Do model-centric optimizations
- Debug models as complexity gets added
- Conduct error analysis for uncovering failure modes.
E. Test and Evaluate
- Evaluate the model on test distribution
- Revise 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.
F. 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.
G. 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 RevInfotech Machine Learning team after perusing the Machine Learning type and the developmental life cycle 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 starts 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 that would define if a project is trivial or would it be complex.
Phase 2 – Exploration (6 to 8 weeks)
The goal of this phase of the project is to build upon a Proof of Concept which can then be installed as API. Once a baseline model is trained, our ML experts estimate the performance of the production-ready solution.
This phase gives us a clear picture 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 of the project where the team works iteratively till they reach a production ready answer. 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 different models 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.
Deliverable – A production ready ML solution
Phase 4 – Improvement (continuous)
Once deployed, decision makers most of the time are in a hurry to end the project to save costs. Though the formula works in 80% of the projects, it 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.
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.
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 a different data structure compared to mainstream models. The cost of these projects 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 are 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.
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 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.
There are a number of frameworks that can be used to define an ML app techstack. The ones that we most commonly use in the development of ML app in house are:
TensorFlow – It is a general purpose Machine Learning framework that covers a wide array of use cases. Parented by Google, it is one of the most used frameworks for development and deployment of ML apps.
Firebase ML Kit – It provides an easy to use cloud API for the purpose of image processing tasks such as – barcode scan, text detection, face detection, and logo detection.
OpenCV – the framework is by far one of the most popular machine learning and computer vision libraries. Operating in an open source model, it is available on multiple platforms for developers to create computer vision centric 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.
Challenges in developing Machine learning apps
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
Estimating the manpower and time needed to finish a software project is comparatively 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 harder once you work on creating the time and efforts wise Machine Learning app project estimate.
Even Though the goals could be well-defined, the guarantee of whether or not a model would achieve the specified outcome isn’t there. It’s not usually possible to lower the scope then run the project during a time boxed setting through a predefined delivery date.
It is of prime importance that you simply identify that there’ll be uncertainties. An approach which will help mitigate delays is ensuring that the input file is within the right format for Machine Learning.
But ultimately, regardless of which approach you propose to follow, it’ll only be deemed successful once you partner with a Machine Learning app development agency that knows the way to develop and deploy the complexities in their simplest form.
Frequently Asked Questions
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To ensure the quality and reliability of your software solutions, we employ a comprehensive range of testing methodologies and cutting-edge tools. Our testing approach includes:
- Manual Testing: We use structured test cases to identify usability, functionality, and design issues.
- Automated Testing: Automated testing streamlines processes, improves efficiency, and ensures consistency.
- Unit Testing: JUnit, NUnit, and PyUnit are unit testing frameworks we use to validate the functionality of code units.
- Integration Testing: We verify the seamless interaction of various system components using tools such as Selenium, Appium, and Postman.
- Performance Testing: To assess system responsiveness and scalability, we employ tools like JMeter, LoadRunner, and Gatling.
- Security Testing: Our security testing includes vulnerability assessments and penetration testing using tools like OWASP ZAP and Nessus.
- User Acceptance Testing (UAT): We collaborate closely with your team to ensure that the software aligns with your end-users’ expectations.
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I'm founder and CEO of Revinfotech Inc. I traits in leadership and brilliant practitioner in the Financial Services and FinTech. I helped ban in connecting to the FinTech ecosystem through payment acceptance in blockchain as a service and even help i... read more