5 Tips for Machine Learning Apps

The following are 5 tips for developing machine learning apps:

1. Understand why and where you can use machine learning (ML) in your organization, and get started with developing machine learning apps

Develop an understanding of why you might need machine learning apps. Take a close look at your business and ask questions, e.g.:

• “Where in my business do I deal with massive data sets without gaining any meaningful insights?”
• “What’s kind of insights do I need?” These could include insights from analyzing facial features. Alternatively, you might need to understand the spending habits of a customer segment. You might need insights on where your customers want to save money.
• “Do I have enough data to provide personalized recommendations to my customers?”
• “Where do I have the maximum business needs for actionable insights?”

Questions like these will help you to understand which departments you should prioritize for machine learning software development. You now know where using machine learning makes sense.

You can also understand whether you need to integrate machine learning with other AI capabilities like natural language processing (NLP), computer vision, and facial recognition. Initiate machine learning software development in a focused manner.

Take up small projects and use the appropriate machine learning algorithms. Deploy machine learning models and derive actionable intelligence. Track the “Return on Investment” (RoI).

2. Educate the decision-makers in your organization about machine learning

By their very nature, developing AI systems and machine learning apps involves higher complexity than developing web or mobile applications. The complexity doesn’t concern only the technology. There are business strategy-related complexities too.

Imagine how the senior leadership team in any business looks at web or mobile applications. They understand the growing penetration of mobile devices. Most of them can have knowledge about prominent mobile app ideas.

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However, only the highly tech-savvy members of the leadership team know sufficiently about machine learning models and algorithms. In this scenario, how do you get the buy-in for the large-scale implementation of machine learning?

You need to educate the decision-makers about machine learning. Neither do they need to become data scientists nor do they need code ML algorithms.

However, they should understand why so many industries prioritize machine learning. They need to understand how a machine learning model can help their business function. You should also showcase the RoI from the earlier machine learning project.

3. Understand the importance of data, and create plans and processes to manage data

You need to understand that success in a machine learning project greatly depends on data. The quality of the machine learning models will improve if you have a higher quality of data. The volume of data is important, however, the quality of it is even more important.

How can you provide high-quality data for your machine learning software? You first need to find an effective way to store data. This might require big data frameworks like Hadoop, furthermore, cloud computing platforms can be useful.

You need to ensure prevent any form of tampering of data, e.g., “data poisoning”. For this, you need to design and build a robust information security solution.

As we explained in our guide to developing machine learning algorithms, creating high-quality input data involves the following tasks:

• Collecting data;
• Exploring data and creating profiles;
• Organizing data sets in the appropriate format for consistency;
• Improving the quality of the data sets;
• Feature engineering and analyzing the input variables;
• Splitting the data sets into testing and training data sets.

4. Build a machine learning and data science project team, and plan to impart machine learning technology training

You need to build a competent machine learning software development team. Hire Python developers with AI/ML development experience. This popular programming language offers excellent libraries for ML development, e.g., Numpy, Scipy, Scikit-learn, PyTorch, etc.

You also need to hire a data scientist. Look for the following skills:

• Excellent knowledge of statistics;
• Knowledge of ML algorithms and techniques;
• Experience in programming languages like Python, R, Julia, etc.;
• Knowledge of deep learning;
• Familiarity with popular SQL and NoSQL databases;
• Experience in Hadoop and Apache Spark;
• Good knowledge of tools like SAS, Tableau, D3.js, etc.

Additionally, you need the following roles:

• Big data architects;
• Data analysts;
• Testers;
• DevOps engineers.

Apart from skills in relevant specialized areas and general software engineering, look for the following competencies:

• Passion for excellence;
• Commitment;
• Collaboration;
• Communication skills;
• Leadership.

You might need to train some of the existing developers in your organization. Establish a training process covering ML, AI, and data science. You can include authoritative books like “Machine Learning for Absolute Beginners” by Oliver Theobald.

5. Use artificial intelligence and machine learning tools/platforms, and take a practical approach about improving the machine learning models

We recommend you use AI/ML development tools and platforms. The reasons are as follows:

• You can use established libraries like Scikit-learn of Python, therefore, you can avoid developing machine learning algorithms. Such libraries contain standard implementations of important ML algorithms.
• You can use well-known AI platforms like Azure AI Platform and Google Cloud AI Platform. They take care of provisioning and management of infrastructure and computing resources, therefore, you can focus on ML development.
• Using ML development tools and platforms helps you allocate expensive human resources for tasks that require their expertise.

Remember that developing and deploying machine learning models is an iterative process. You need to run the relevant algorithms and review the resultant machine learning model. Analyze the exceptions and outliers. You might need to improve the quality of testing and training data, and you might need to revisit the data preparation steps. Prepare to spend enough time on this iterative process.

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