When to Consider Machine Learning Development Services
Artificial intelligence (AI) and machine learning (ML)-based software enables your solution to learn from data and adapt to new circumstances, providing a richer experience for users. AI enables your software to process data and make decisions. This can be as simple as recommending products to customers or as complex as self-driving cars.
The applications of artificial intelligence and machine learning in software development are endless. According to Forbes, 76% of enterprises prioritized AI and machine learning at the top of their list of initiatives in 2021.
Scenarios to Consider Machine Learning Development Services
If you are ready to implement ML, don’t go it alone. A machine learning development company with AI expertise can help you reap the benefits of ML faster than you can internally while implementing the technology seamlessly into your solution.
1.) When Evaluating Machine Learning Opportunities
There are tons of applications for ML, so it’s important to understand how it best fits into your software product and if it’s the right solution in the first place.
Here are some examples of good use cases for machine learning development services:
- Identifying high-value customers
- Advanced data analysis and insights generation
- Creating forecasts and predictions
- Automating routine manual processes to increase efficiency
- Image recognition, such as an app that tags your photos or detects objects in them
- Natural language processing, like a chatbot or voice assistant
- Recommendation engines, such as a movie recommendation site
- Clustering and grouping data, such as a site that groups products based on what users like
A Machine Learning Development Service Guides the Evaluation Process
A software development partner can help guide you through this process, steering you toward a better solution if ML is not best suited to your use case. Where there are opportunities to innovate with ML, a service provider can assist in building your roadmap and allocating resources for smooth, efficient development. Specifically, your partner can help you:
Identify Business Goals and the Problem You Want to Solve:
Before getting into the weeds of algorithms and data sets, it’s important to get clear on what you want the AI to do. Though this may seem simple on the surface, it can be challenging to narrow down a specific use case with a controlled scope.
Sometimes advanced statistics can accomplish the goal better than AI; sometimes the goal is out of scope for what’s possible in AI’s current state. A partner can help reify your vision into the precise particulars that help you suss out which solution is the right one.
Ask the Right Questions:
What type of data do you have? Whether your data is structured, unstructured, or in combination will determine the kind of ML applications you can leverage. Additionally, how the data is housed will impact the ease of implementing AI, whether it’s in a warehouse or lake.
What kind of modeling are you trying to do? Predicting future data trends or recommending products that customers are likely to buy require different ML models of varying complexities.
What is the output of the ML algorithm? Once the algorithm analyzes data, additional emerging technologies may be necessary to create a usable output. For example, natural language generation can produce insights or recommendations in plain English, providing the user with actionable next steps.
Do you have the right infrastructure for AI? ML can require significant computing power, data connectors, cloud, and more. Understanding the compound needs of an AI project is essential for generating ROI.
2.) When You Have Gaps in Your Data
ML algorithms are dependent on quality data. Data must be accurate, complete, and relevant to the problem. These data sets must also be large enough to support the algorithm’s needs.
For example, if you’re working on a recommendation engine, you’ll want to make sure that the data set represents a balanced variety of products, including items that users may not have purchased before. Without relevant data, algorithms will be unable to develop accurate results.
A Machine Learning Development Services Provider Helps Fill Data Gaps
A lack of the right data in place can be a huge roadblock to adopting ML, so a development partner can help you jump this hurdle quickly. An ML development partner can assist in identifying staging data gaps and then cleaning and preparing the data for a training and testing set. Where gaps exist, they can also assist in mining new data or creating accurate synthetic data.
Where data has been collected but not formatted correctly, they can also assist in cleaning and normalizing data. Partners may also be engaged to assist in the transfer of data into a data warehouse and loading data into a data lake, depending on each company’s data needs.
Additionally, a partner can help navigate training the algorithms if needed. As the machine learns, human knowledge and experience is required to ensure the machine is drawing the right conclusions. An experienced partner can help you guard against data bias.
3.) When You Don’t Have the Right ML Team in Place
Lack of In-house AI/ML Expertise
Many companies may not have developers with AI/ML expertise on their team, or their existing ML resources may be focused on core business projects. ML engineers and developers are in high demand. The shortage of skilled developers has been widely reported, and the situation is only expected to worsen as the demand for data skills grows.
As data grows across industries, there will be an increased demand for developers and data engineers. Finding good candidates is hard enough, and hiring them is even harder. Remote work has exacerbated this struggle as strong candidates can now work anywhere, increasing hiring competition significantly.
Hiring and onboarding a new team member(s) is an expensive and time-consuming investment. A LinkedIn study reports that it takes a median time of 49 days to hire an engineer and 44 days to hire an IT professional. And during that process, the company loses valuable productivity as the new hires try to adjust to their new environment.
Machine Learning Development Services Get You Up and Running Quickly
A machine learning development company can ramp up immediately, filling gaps in your existing team and scaling to meet the demands of your project. It’s more cost-effective, faster, and flexible than internal hiring.
Working with an experienced vendor lets you focus on the most important aspects of your product or service instead of hiring and training new employees. And you’ll get the added benefit of a team with domain expertise and experience working with a variety of technologies. Plus, it’s easier to find a developer with experience in the language and framework you need than to train an engineer on new technology.
4.) When You Need Support From Data Acquisition to Deployment
Navigating a full ML project can be daunting, especially if it’s your first venture. Deploying ML into your product requires expertise and skill in several key areas:
Data Acquisition: Getting the needed data ingested into the system. There are different ways to get data into the system, such as extracting data from a data warehouse, loading data from files, or streaming data from devices.
Data Preprocessing: Preprocessing involves cleaning up data to make it useful for ML algorithms. Common data preprocessing steps include data normalization, transformation, filtering, and sampling. Data normalization involves adjusting data to have a similar range. Transformation is changing data from its original format to another format that is more useful for ML algorithms. Filtering involves removing any data that is not useful to the ML algorithm. Sampling involves selecting a subset of data to train the ML algorithm.
ML Modeling: ML modeling is the process of building models to solve specific problems. This is the main step in machine learning, as it’s where data scientists actually put algorithms to work. There are many different types of ML models, from simple logistic regression to cutting-edge deep neural networks.
The process of building an ML model is the same, no matter what type of model it is. You’ll need to gather and prepare your data, decide on the model you want to use, and then set up the model’s parameters. You’ll also need to validate your model to ensure that it’s actually solving the problem you set out to solve.
Deploy: Push the data into data warehouses, business intelligence tools, or other systems where it can be analyzed and used to improve business processes. Data has to be stored and put into a format where it can be reused if needed.
An ML Development Services Provider Supports You Through the Entire Project Lifecycle
A machine learning development company can assist at every stage to bring your ML deployment to fruition.
Choosing the right machine learning development services partner to work with can help you avoid common pitfalls and get the most out of your investment in machine learning. It’s important to select a partner that understands your business goals and can recommend the right tools and technology to achieve them.
Kickoff Your Machine Learning Development Project With KMS Technology
Our teams help you get the most value out of your machine learning project. We work to understand your goals and are committed to your success. Schedule a free consultation to discuss your machine learning development needs.
