Machine Learning is two-sided.

On one side, it helps to reduce uncertainties from processes. But, its development is full of not sure.

While the result of almost every ML  project is an explication that makes businesses more reliable and processes streamlined, the development section of it has an entirely different story to share.

Even though ML has played the central role in changing the profit story and business model of various established mobile app brands, it still works under nascency. This latest makes it all the more challenging for mobile app developers to handle an ML project plan and make it production-ready, keeping the time and cost constraints in mind.

A solution to this difficulty is the black and white Machine Learning app project is the estimation of the time, cost, and deliverables.

But before we head on these sections, let us first look into what makes the complexity and burning off the night candles worth it.

Why does your app need a Machine Learning framework?

For Offering Personalized Experience

The scope of the answer to What is Machine Learning rests on the benefits that technology allows businesses by being a continual learning system. They can help in differentiating the users based on the concern by collecting the users' data and deciding on the app's feel and look.

Businesses can utilize Machine Learning framework integration to learn –

• Who are their clients
• What do the customers need
• What hobbies and preferences do the users have, etc.

Based on the data, machine learning helps clients in classifying and structuring their customers, Searching a unique method for every customer group, and adapting the tone of the content.

For Incorporating Advanced Search

Machine Learning methods make it possible for you to optimize the search functions in a way that you can deliver more contextual output, making search a lot more natural and less burden for the users.

They also provide businesses to collect all the available data 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 behavioral patterns by looking into different kinds of data:

• Age
• Gender
• Frequency of using an app
• Location
• Search details, etc.

Using this information, it gets easier for businesses to plan their marketing strategy according to the individual user type.

For Better Security

In addition to being a useful marketing tool, machine learning can also help with the process of streamlining and securing app authentication. ML-powered facilities like voice, audio, and video recognition make it easier for users to authenticate with the use of biometric data like face or fingerprints. The machine learning can also help in banning and identifying suspicious activities on the app, in addition to preventing users from malware attacks.

For Deep User Engagement

Machine learning capabilities empower businesses to offer fantastic customer support, appealing features, and entertainment value, which gives users the incentive to use the app on an everyday basis. 

• Non-Time-Bound Support
• Advanced Features Set
• Offering Entertainment

Kinds of Machine Learning Models

Machine learning, amidst its various use cases, can be categorized into three model varieties, which play a role in aiming rudimentary apps into intelligent mobile apps – Unsupervised, Supervised,  and Reinforcement. The understanding 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 provides data where the algorithm's inputs and their outputs are labeled correctly. Since input and output information is marked, the network train to identify the patterns in data within the algorithm.

It becomes all the more beneficial for its use to predict the outcome based on future input data. An example of this can see when social media recognizes somebody's face when they tag in a photograph.

Unsupervised Learning

In the case of unsupervised learning, data save in the system, but outputs are labels like a supervised model. It allows the system to identify data and determine patterns from the information. Once the designs are stored, all the future inputs are assigned to the profile for producing a result.

An example of this design can be seen in cases where social media gives friends suggestion based on several known data like demography, education background, etc.

Reinforcement Learning

in the instance of unsupervised learning, the data is given to the system in reinforcement learning is also not specified. Both the machine learning type varies on the ground that when the correct output gets produced, the system is told that the production is right. This learning type allows the system to learn from the experiences and environment.

An example can be seen in Spotify. Spotify app creates a recommendation for a song, which the users then have to gives a thumbs-up or thumbs-down. Based on the selection, the Spotify app learns users' tastes in music.  

The lifecycle of a Machine Learning project

The cycle of a Machine Learning project timeline 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 labeling documentation

Create the data ingestion pipeline

Validation of data quality

Model Exploration

Establish the baseline for model performance

make a simple model with primary data pipeline

Try identical ideas during the initial phase

Find the SoTA model for the issue domain, and reproduce output.

Refinement of Model

Do model-centric optimizations

Debug models as complexity get added

Conduct error analysis for uncovering failure modes.

Test and Evaluate

Evaluate the model on a test distribution

Revisit the model evaluation metric, making sure it drives desirable user behavior

Write tests for – input data pipeline, model inference function, explicit situations expected in the production.

Deployment of Model

Show the model through REST API

Deploy the latest model to a subset of users to ensure that everything is stable before the final rollout.

Have the capacity to roll back the models to its earlier version

Monitor the live data.

Model Maintenance

Retrain the model for stopping model staleness

Teach the team if there is a transfer in the model ownership

How to Determine the Scope of a Machine Learning Project?

The Appinventiv Machine Learning team after analyzing the Machine Learning type and the developmental lifecycle goes on to determine the Machine Learning app project evaluation of the project following these stages:

Phase 1 – Discovery (6 to 14 days)

The ML project design roadmap starts with the description of difficulty. It looks into the operational and issues inefficiencies, which should be discussed.

The aim here is to recognize the requirements and see if Machine Learning matches the business objects. The stage requires engineers to comply with business people on the client-side to understand their idea in terms of what problems they are looking to resolve.

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

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

Phase 2 –  Exploration (5 to 8 weeks)

The purpose of this phase is to make a Proof of Concept, which can be installed as API. Once a baseline design is trained, our team of ML experts estimates the performance of the production-ready solution.

This stage gives us 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)

It is the stage where the team works until they reach a production-ready answer. Because there are far fewer uncertainties by the time the project enters this phase, the estimation gets very specific.

But in case the output is not improved, developers would have to apply different models or rework on the data or may change the way if required.

In this stage, our developers work in races and decide what is to be done after every particular iteration. The results of every race can be foretold efficiently.

Phase 4 – Improvement (continuous)

Once deployed, decision-makers are almost always in a hurry to end the project to reduce costs. While the method works in 80% of the projects, the same doesn't practice in Machine Learning apps.

What happens is that the information changes throughout the Machine Learning project timeline. It is the reason why an AI model has to be monitored and continuously reviewed – to save it from degradation.

Machine Learning centered projects need time for achieving satisfying results. Even when you find your algorithms hitting the benchmarks right from the beginning, the chances are that they would be one stroke, and the program might get failed when used on a diverse dataset. 

How We calculate the budget of a Machine Learning Project?

When we talk about the calculation of the price of a machine learning project, it is essential first to identify which project type is spoken.

There are three types of Machine Learning projects, which hold a part in acknowledging How much does Machine Learning cost:

First – This type now has a solution – both: model architecture and dataset already exist. These types of projects are almost free, so that we won't be discussing them.

Second – These projects need basic research – application of ML in a completely new domain or on different data structures compared to mainstream models. The cost of these projects type is usually one which majority of startups cannot stand. 

Third – In this, we are going to aim at our cost calculation. Here, you take model algorithms and architecture which already exist and then change them to suit the data.

Let us now get to the portion where we determine the cost of the ML project.

The data cost

Data is the primal money of a Machine Learning project. The maximum of the solutions and research focuses on the variations of the supervised learning model. It is a well-known fact that the more profound the supervised learning goes, the higher are the need for interpreted data, and in turn, the more necessary is the Machine Learning app development cost.

Now while services like Amazon's and Scale  Mechanical Turk can help you with the collection and annotation of data, what about Property?

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 element 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. The exploratory phase is the one every project goes through ere its production.  

Completing the stage with its ultimate perfection is a process that comes with an assigned number in the cost of executing ML discussion.

The production cost

The production section of Machine Learning project cost is made up of integration cost, infrastructure cost,  and maintenance cost. In these costs, you will have to make the lease payments with cloud computation. But that too will diversify from the complexity of the particular algorithm to another.

Integration costs differentiate from one use case to another. Regularly, 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 main factor that people tend to overlook when acquiring a machine learning project is the need to pass continuous support during the complete lifecycle of the project. The information which comes in from APIs has to be washed and correctly interpreted. Then, the models have to be designed on the latest data and tested, deployed.

In point mentioned, two more factors carry essence on the calculation of the cost to make an ML project.

Frameworks Used to make a Machine Learning System

As you can see from the image, several frameworks can be used to define an ML app tech stack. The ones that we most usually used in the development of ML app in the house are:

TensorFlow – It is a general-purpose Machine Learning framework that covers a vast array of use cases. Govern by Google; it is one of the most used structures for the deployment and development of ML apps.  

Firebase ML Kit – It allows an easy to use cloud API for image processing tasks such as – barcode scan, face detection, text detection,  and logo detection.

OpenCV – the framework is by considerably one of the most famous machine learning and computer vision archives. Operating in an open-source model, it is available on multiple platforms for developers to create computer vision centric apps.

Challenges in developing Machine learning apps

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

The challenges for Machine Learning projects can extend from:

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

Conclusion

Estimating the workforce and time required to finish a software project is almost effortless when it is designed on the grounds of modular designs and is managed by an expert team following an Active approach. The same, however, becomes all the more complicated when you work on designing the effort and time-wise Machine Learning app project to evaluate.  

Even though the aim might be well-defined, the guarantee of whether or not a model would accomplish the desired result is not there. It is not usually possible to reduce the range and then run the project in a time-boxed setting through a predefined performance date.  

It is of prime significance that you recognize that there will be difficulties. An offer that can help mitigate delays is assuring that input information is in the right format for Machine Learning.

But eventually, no matter which method you plan to follow, it will only be considered victorious when you consult with a Machine Learning app development company that knows how to build and deploy the complexities in their most natural form.