Machine learning helps to eliminate the uncertainties from processes, while on the other hand, it is full of unsureties. While the end result of every Machine learning project is a solution that makes businesses better and processes streamlined. Despite the fact that ML has played an enormous role in changing the profit story and business model of some established mobile application brands, it still works under dawning.
This newness, thus, makes it all the more challenging for mobile application developers to deal with an ML project plan and prepare it for production, considering the time and cost constraints. A solution to this issue is a black and white machine learning app project estimate of the time, cost and deliverables. But, before we head on those areas, let us first investigate what makes the difficulty.
Need of machine learning framework to an app-
1. Personalized Experience-
The extent of the solution to What is Machine Learning lies in the advantages that technology offers to businesses by being a continual learning system. They can help in grouping the users according to the interest by collecting the users’ information and deciding on the application’s look and feel. Businesses can use Machine Learning framework integration to learn-
- Who are their customers
- What do the customers want
- What preferences and hobbies do the users have, etc.
According to the data, machine learning helps users to classify and structure their customers, finding an exceptional approach for every customer group, and adapting the tone of content.
2. For Incorporating Advanced Search-
Machine learning solutions allows you to optimize search functionality in a way that you can deliver more results, making search more automatic and less difficult for the users. It also allows businesses to get the available data about users and rank them according to best match.
3. For Predicting User Behaviour-
ML based applications assist businesses to understand users’ choices and behavioural patterns by analyzing various types of data such as, age, gender, location, frequency of using app, search details etc. Use of such data helps businesses to plan marketing strategy as per individual user type.
4. For Better Security-
Also being a powerful marketing tool, ML can help with the streamlining process and securing the application authentication. ML controlled facilities like voice, audio, and video recognition makes it simpler for users to authenticate with the assistance of biometric data like fingerprints or face. Machine learning algorithms also help to identify and restricting suspicious activities on the application and preventing users from malware attacks.
5. For Deep User Engagement-
Machine learning allows businesses to offer amazing customer support, great features and entertainment value that gives users incentive to use app on a daily basis.
- Non Time-Bound Support
- Advanced Features Set
- Offering Entertainment
Types of Machine Learning Models-
ML, in the midst of its different use cases, can be divided into three model types, which play a role in transforming simple applications into insightful mobile applications – Supervised, Unsupervised, and Reinforcement. The knowledge on what these Machine Learning Models depend on is the thing that help to characterize how to build up an ML-enabled application.
1. Supervised Learning-
It is a process where the system is provided with data where algorithm’s inputs and their outputs are labeled effectively. Since the input and output data are labeled, the system is well trained to distinguish the patterns in data within the algorithm. It turns into even more helpful for it is used to predict the result based on future input data. An example of this can be seen when social media recognizes someone’s face when they are tagged in a photo.
2. Unsupervised Learning-
In unsupervised learning, the data is fed in a system but its outputs are not labeled like in the case of supervised models. It enables the system to identify data and determine patterns from the data. As the patterns are stored, all the future inputs are assigned to the pattern for producing an output. Example of this model is- where social media gives friend suggestions according to several known data such as demography, education background etc.
3. Reinforcement Learning-
Like in case of supervised learning, the information which is given to the system in fortification learning is also not labeled. Both the machine learning types differ on the ground that when the appropriate output gets produced, the system informed that the output is correct. This learning type allows the system to learn from experiences and environments.Example of this is Spotify. Spotify application gives recommendation for song which the users then have to give thumbs up or thumbs down. According to the selection, Spotify app will get to know users’ interest in music.
How to Estimate the Scope of a Machine Learning Project?
Phase 1 – Discovery (7 to 14 days)-
The aim is to gather requirements and evaluate whether Machine Learning suits your business goals. You need to confront your vision with the developer who will inform you what problems can be solved with the use of the current state-of-the-art and what metrics can be used to measure it.
First, metrics and business goals are generally different. Users can give ratings to movies from 1 to 10 stars. Let’s say an algorithm can be trained to predict these outcomes with 90% accuracy. It looks great but, from the business point of view, it may be more helpful to know if a viewer is going to watch the whole movie or switch to do something else. It does not have to correlate with their star ratings.
Secondly, the development team should know the type of data they have and if they would need to fetch it from outside service.
Third, developers need to examine if they can supervise algorithms – if it returns the right response each time a prediction is made.
Deliverable – A Problem Statement which would characterize if a project is simple or would it be complicated.
Phase 2 – Exploration (6 to 8 weeks)-
The goal of this stage is to build upon a Proof of Concept which can then be able to be installed as API. When a standard model is prepared, our team of ML specialists examines the performance of the production-ready solution. This stage gives us the accuracy of what performance should be expected with the metrics planned at the discovery phase.
Deliverable – A Proof of Concept
Phase 3 – Development (4+ months)-
In this phase, team works iteratively till they reach a production ready solution. Since there are less vulnerabilities when the project arrives at this stage, the estimation you get will be extremely accurate. But ,if the result isn’t improved, developers would have to apply different models or rework on the data or even change the strategy, if necessary. In this stage, developers work in sprints and decide what they should do after every single iteration. The results of each sprint can be predicted successfully.
Deliverable – A production ready ML solution
Phase 4 – Improvement (continuous)-
Once deployed, decision-makers are quite often in a rush to end the project to save expenses. While the formula works in 80% of the projects, the same doesn’t apply in Machine Learning applications. What happens is that the data changes all through the Machine Learning project timeline. Hence, an AI model must be monitored and reviewed continually – to save it from degradation. The Machine Learning focused projects need time for accomplishing satisfying outcomes. Even when you discover your algorithms beating the benchmarks directly from the beginning, chances are that they would be one strike and the program may get lost when used on an alternate dataset.
How to estimate the cost of a machine learning project?
The cost of a machine learning project depends on the type of project. There are significantly three kinds of Machine Learning projects, which play a role in determining machines Learning cost:
- First – This type already has a solution – both: model architecture and dataset already exists. These types of projects are practically free, hence no need to talk 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. Generally. the cost of these projects are not affordable to startups
- Third – These are the ones we are going to concentrate on in our cost estimation. Here, you take model architecture and algorithms which already exist and afterward change them to suit the data you are working on.
Let us see where we estimate the cost of the ML project.
1. The data cost-
Data is the primary factor of a Machine Learning project. Most of the solutions and research focuses on the variations of the supervised learning model. It is a popular fact that the more profound the supervised learning goes, the greater are the requirement for annotated data, and thus, the higher is the Machine Learning application development cost. Presently while services like Scale and Amazon’s Mechanical Turk can assist you with gathering and annotation of data, what about quality? It tends to be amazingly tedious to check and afterward correct the data samples. The solution to this issue is two-faced – either outsource the data collection or refine it in-house.
2. The research cost-
The research part of the project, manages the entry-level feasibility study, algorithm search and the experimentation phase. The data which generally surfaces from a Product Delivery Workshop. Basically, the exploratory stage is the one each project experiences before its production. Completing the phase with its most extreme perfection is a process that accompanies an attached number in the cost of executing ML discussion.
3. The production cost-
The production part of Machine Learning project cost includes infrastructure cost, integration cost, and maintenance cost. Out of these costs, you should make minimal costs with cloud computation. But, that also will fluctuate from the complexity of one algorithm to another. Integration cost changes with one use case to another. Generally, it is sufficient to place an API endpoint in the cloud and document it to then be used by the rest of the system.
One key factor that most of the people will generally ignore when developing a machine learning project is the need to pass consistent support during the whole lifecycle of the project. The data which comes in from APIs must be cleaned and annotated appropriately. At that point, the models must be trained on new data and tested, deployed.
Estimating the cost of a software project is easy when is developed by an agile approach. However, it becomes more difficult when you work on creating a time and effort-wise machine learning app project estimate. Even Though the objectives may be more characterized, the assurance of whether a model would accomplish the ideal result isn’t there. It isn’t generally conceivable to lower the scope and then run the project in a time-boxed setting through a predefined delivery date. It is of prime significance that you distinguish that there will be vulnerabilities. A methodology that can help moderate delays is ensuring that input data is in the right format for Machine Learning.
These are some key points to consider while estimating the time, cost of the ML app project. If you are still facing any difficulty with estimation, consult with solace experts. We have dedicated Machine learning developers to help you with consultation and development through the best knowledge. Get a free quote for developing an effective ML project. We will be happy to help you.