Machine Learning Development Services

Develop machine learning models to perform complex calculations and computing, from predicting human behavior to fraud detection.

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Machine Learning Development Services We Provide

We create machine learning solutions for businesses across multiple industries, from healthcare to manufacturing. Our ML solutions enhance decision-making capabilities and improve operations.

Custom Machine Learning Model Development

Uncover business insights. Personalize the user experience. Achieve higher prediction accuracy. From data preprocessing to model training and optimization, we build custom machine-learning solutions to help you make data-driven decisions.

Our data scientists and machine learning engineers design and develop custom models specific to your niche. We use programming languages like Python, R, and Java, along with machine learning technologies like TensorFlow and PyTorch to build and deploy your ML model.

Natural Language Processing Services

NLP is the driving force behind chatbots, virtual assistants, and spam detection tools. It allows systems to communicate more effectively with users.

With tools like the Python Library Natural Language Toolkit (NLTK), we integrate NLP capabilities into software to accommodate users with different needs and abilities.

Predictive and Real-Time Analytics

Predictive analytics harness machine learning capabilities to identify patterns, insights, and relationships within data.

With tools like SPSS and Hadoop, we collect and process your data to build predictive models. Make informed forecasts and business decisions.

ML Integration

Incorporate machine learning models into existing software and systems to enhance applications.

Using APIs and SDKs, we integrate pre-trained models into applications to add functionalities like image recognition and speech-to-text. We can also train custom ML models and incorporate them directly into your software.

Computer Vision Services

Many industries such as healthcare, entertainment, and agriculture have begun to rely on object detection, scene recognition, and image classification. These processes are used in security, activity monitoring, and other operations. All of them rely on computer vision.

This field allows computers to derive insights from visual inputs and automate processes associated with human sight. Using techniques like Convolutional Neural Networks (CNNs), we incorporate computer vision systems into applications and devices. For example, we can use them in automated checkout systems, which recognize the items being purchased and enable fast checkout without manual intervention.

Deep Learning Services

Ever wondered how Netflix and Amazon became so good at recommending products? It’s because of deep learning. A subset of machine learning, deep learning leverages artificial neural networks to perform complex tasks and solve problems.

As a deep learning development company, we design neural networks, configure the learning process, train the model, and deliver deep learning solutions using tools like TensorFlow, PyTorch, and Keras. They can be applied to everything from shopping recommendation systems to medical imaging in hospitals.

Key Facts About Machine Learning Development

5 Main Reasons to Consider Outsourcing

Why Is Machine Learning Important for Businesses?

Machine learning is emerging as a critical field for enabling businesses to perform tasks that were once thought to be impossible. Across industries, organizations are tapping into its enormous potential. But it’s also highly complex. When you outsource machine learning services to specialized providers, you’ll take advantage of top talent with expertise and knowledge of ML techniques and technologies.

Here are 5 benefits of outsourcing:

Access Niche Expertise: Your in-house team may lack important ML-specific skills. When you look beyond your immediate vicinity, you’ll find data scientists and ML engineers with specialized knowledge of machine learning techniques.
Reduce Time to Market: Thanks to established workflows and resources, outsourcing providers can often complete intricate machine learning projects more quickly than in-house teams.
Mitigate Risks: Outsourcing companies share the risk with you. They’re also typically familiar with regulations governing AI and ML and have experience dealing with challenges in the field, such as data quality and bias.
Scale Easily: Quickly adapt as your demands change. With a flexible partner, you can scale as needed.
Work with Global Talent: Access a global talent pool and diverse skills and perspectives.

In today’s data-driven landscape, adopting machine learning technologies is often important for many businesses’ survival. It can transform business operations, facilitate stronger decision-making, and automate key processes.

Best Practices for Machine Learning Development

Machine learning is constantly evolving, so it’s important to stay up to date with modern techniques and tools. Here are the best practices we follow.

Part 1: Investigate the Problem Domain

The ML development process begins with outlining your requirements and the procedures for building a model that will meet them.

Understand the Problem
Define clear objectives, KPIs, and success criteria to inform your understanding of the problem domain.

Obtain and Clean the Data
Source data from credible sources, assess it for consistency and accuracy, and address any missing information or inconsistencies.

Choose the Appropriate Model
Experiment with different ML algorithms or architectures to find the best fit to address your problem.

Establish Relevant Features
Feature engineering affects model performance. Choose features that are relevant to the model.

Evaluate the Model
Select evaluation metrics based on the type of problem.

Part 2: Hone the Machine Learning Model

Here’s how we build your ML solution and account for its nuances.

Conduct Data Preprocessing
Data preprocessing involves assessing the quality of your raw data. This process includes encoding categorical variables and handling missing values.

Perform Exploratory Data Analysis (EDA)
EDA—including visualizing distributions, correlations, and anomalies—helps inform decisions about feature engineering and model selection.

Standardize the Data
Improve the stability of ML algorithms by normalizing or standardizing the data.

Account for Scalability
You’ll need to accommodate a growing volume of data, so it’s important to build the model with scalability in mind. Cloud-based solutions can help with scalability.

Evaluate and Improve the Model
Continuously assess and iteratively improve your ML model, testing it against new data to monitor its performance and adjust as necessary.

Part 3: Test the Model

QA testing is critical for ensuring the ML model’s efficacy, security, performance, and functionality.

Perform Bias and Fairness Testing
Assess your model for biases. We use fairness metrics and testing techniques to detect issues in predictions related to factors like gender, race, and age.

Conduct Security Testing
Identify potential vulnerabilities and implement measures to protect your data.

Test for Robustness
Evaluate how well the model handles unexpected outputs. Assess stability and perform exploratory testing to understand how the model makes predictions.

Why Choose Techbly for Machine Learning Development

Custom App Development

We use a thorough vetting process to ensure that we hire only the top 1% of machine learning talent. Our engineers have expertise in the tools and technologies in the field, as well as knowledge of the latest news and trends.

Customized ML Solutions

What do virtual assistants, facial recognition tools, and health trackers have in common? They’re all powered by machine learning. We have experience working on custom ML projects like these and many others. We’ll collaborate with you to create a unique machine learning solution.

Rapid Development

Machine learning is constantly evolving, and you need to reach market quickly to keep up. Our engineers will accelerate your timeline, working speedily to build and refine your ML solution. This helps you stay competitive in the rapidly growing AI space.

Our process. Simple, seamless, streamlined.

STEP 1

Discuss Your Requirements

We begin by discussing the problem you're looking to solve, determining the ML task, and identifying metrics to evaluate the performance of the model. We'll also discuss how these objectives align with your business goals.

STEP 2

Create a Plan and Assemble a Team

We'll create a plan to gather and transform data to inform your solution. We will work together to determine which engagement model is most appropriate for your business: staff augmentation, dedicated teams, or end-to-end software outsourcing. Then, we'll select the best-fit ML engineers.

STEP 3

Get to Work

Our engineers start working on your ML solution. No AI development process is linear, but typically, we'll conduct an Exploratory Data Analysis, choose the model, train the data, evaluate, and deploy the solution. We'll keep you informed of our progress at every turn.

Frequently Asked Questions (FAQ)

How does outsourcing to a machine learning development company work?

Outsourcing machine learning development involves contracting an external company to complete ML projects collaboratively with your in-house team or autonomously. We offer three different engagement models: staff augmentation, dedicated teams, and end-to-end software outsourcing.

What kind of applications can you build using machine learning?

There are many kinds of machine learning apps you can build across industries and niches. Examples include:

Spam detection tools
Recommendation engines
Virtual assistants
Chatbots
Natural language processing (NLP) software such as speech recognition tools
Fraud detection tools

What is the difference between ML and AI?

Machine learning is a subfield of artificial intelligence. While AI is a larger umbrella term encompassing multiple branches, ML specifically concerns the use of data science and algorithms to mirror how humans learn, adapt, and grow as the model accesses more data.

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