Machine Learning Development
Our team of machine learning engineers has robust, hands-on experience building amazing ML solutions. In fact, every day we assist businesses in solving their complex business problems. Our services facilitate data-based decision making and create new models for addressing difficult issues.
We develop water-tight ML-based applications like:
- deep learning
- video analytics
- predictive analytics
Let us help you automate and optimize the processes in your business by exploring better algorithms in machine learning to achieve exponential growth. You need to tap into our machine learning skills today to increase reliability, strengthen security, enhance spam filtering whilst boosting product insights and future forecasting. Machine learning provides this and more, so take full advantage of our cutting-edge solutions today.
Our Machine Learning Services
Our experts in deep learning algorithms create business technologies by injecting human-like intelligence into your computers. We analyze and interpret complex data. It helps in identifying untapped areas and cost-saving solutions.
Laneways.Agency offer predictive analytics and deep learning. You'll get a clearer review of your progress against predicted outcomes. Our AI-powered solutions help to identify possible future events, both positive and negative. Our solutions here include:
- statistical algorithms
- historical data
- machine learning
Your business decision-making processes need customized software. Enhance your services by creating more reliable models and automatic operations. We change raw data from big data companies into clean datasets. It allows us to classify, cluster, and regress data more quickly. We then deploy the models across systems.
Predictive modeling helps you to avoid risks. It dramatically improves access to business intelligence and maximizes revenues. Optimize your machine learning algorithms models to enhance departmental and company-wide business information accuracy.
Neural networks development
We have highly skilled neural networks and machine learning engineers. They come with robust deep learning systems knowledge and work on very big data sets. Leveraging neural networks solutions that are modeled on the human brain, we can identify hidden patterns that other apps would fail to classify.
Marketing automation solutions
Integrate machine learning programs with CRM applications. We break down market segmentation and carry out precision marketing. Increase demand prediction and quantify leads. You will understand specific segments and clients far better.
Images can have valuable information traditional data analysis methods can't access. Computer vision and technology/algorithms help solve this problem. Our technologies can sense objects and attributes in images. They can understand the hidden information and make it available in a variety of formats
Videos and motion pictures have useful information. For example, you can identify and tag various entities in videos. Use our robust computer vision, machine learning, and video analytics solutions for this process.
Natural language processing
Accelerate your business with a wide range of Natural Language Processing and deep learning. We offer:
- text analysis
- sentiment analysis
- keyphrase extraction
- language detection
We offer a wide range of AI and machine learning algorithms models. Our technologies go through continuous learning. Tweaking makes these models smarter each day.
Our innovative machine learning approach
It is our business to help improve your business's profits and general success. When creating machine learning algorithms/models, we:
Machine platforms we work on
Google Machine Learning
Harness the benefits that Google Machine Learning algorithms offer. You'll predict outcomes better.
Amazon Machine Learning
We help you control Amazon Machine Learning vision tools and wizards. Create and maintain ML models using machine learning algorithms platforms from Amazon.
What can machine learning be used for in the modern world?
Businesses are reaping benefits from Machine learning in various areas. Some of these areas include:
Optimum inventory levels
Predictive analytics and ML are gaining more use as they can predict your clients' buying behaviors. Logistics are utilizing these predictions to plan product inventory. It helps reduce inventory shortages or unneeded surplus.
An AI system analyzes the demand for a specific item on-site. It helps you to decide on the goods that you need to provide in various warehouses at various times.
Let our ML hyper-boost your HRM systems. Using machine learning empowers the human HR team. It speeds up data analysis. Your future HR decision making becomes easier. Some of the areas where ML will help your business in HR department includes:
- Hiring - You can leverage the power of neural networks and machine learning. Use it to analyze recruitment data. Then feed all these data into the machine learning software. Specific patterns will emerge and you can determine, for example—which job ads website brings in more qualified applicants, which interviewer fetches the best talents, or the social media platform that has better employees.
- Employee attrition - Human-driven HR systems alone can fail to analyze every person's feedback fully. Failure to identify people's true intentions can lead to unwanted staff attrition or other HR issues. That's where ML software comes in, interpreting responses on workers' satisfaction surveys in a far more complex form. You can then determine whether the reactions are precursors to certain behaviors, such as their increased likelihood of quitting.
- Employee engagement - Employee engagement is a human-to-human practice. However, you need the smart use of machine learning to reinforce better staff engagement. It allows your business to identify patterns of things that keep them happy. You can ingest data from a common platform into an ML software, then the machine will then dig into the data. After that, it will give you several ways of driving engagement campaigns.
Are you looking to improve margins or mitigate losses? You may need a machine learning system. It can streamline the monitoring of financial flow. You should employ ML software for:
As transactions and users in your business networks increase, you'll naturally experience a spike in security threats. With deep learning and ML integrated, you can worry less about these threats. That's because our machine learning algorithms and the neural network never sleep.
These real-time processes can identify even the most sinister or well hidden fraudulent activity well beyond human capabilities. The machine learning algorithms work tirelessly to smoke out potentially illegal or malicious actions. As these are identified, your systems can then automatically request that the user provide more information.
Machine learning is crucial in financial process automation. Automation can intelligently reduce repetitive, manual work processes. This in turn saves time & money and frees you to allocate resources better, all whilst increasing staff morale. Our machine learning automation services include:
- paperwork automation
- gamification of worker training
- call center automation
Underwriting and credit scoring
Finance and insurance businesses are full of underwriting tasks. Also, vital credit scoring tasks (if you even have the resources to do that at the moment) are another headache. The use of machine learning works wonders here.
Our deep learning scientists collect many data entries and feed them to ML models, training the system to carry out similar tasks. The machine learning happens in real-life situations. Allow these learning engines to take over tiresome manual tasks and your underwriting processes can become quicker, with fewer errors.
ML in Government
Public safety and other government agencies need machine learning. They have multiple data sources that they need to mine for useful insights.
For example, deep learning can help them analyze complex census data. It enables them to understand how to boost efficiency and save funds. These processes can also minimize identity theft and detect fraud.
ML in Healthcare
Big Healthcare business is embracing machine learning algorithms. Wearable devices can determine patients' health in real-time. Medical experts can also use deep learning to analyze and spot data patterns. Alarms and warnings are enhancing a better diagnosis and treatment.
We have extensive experience in the aged care sector, where ML will revolutionize the way elderly residents' needs are met.
ML in Retail
Using machine learning algorithms among retailers is increasing. Retail owners can recommend items on their websites intelligently and automatically. Past purchases and website visits are raw data for the machine. Retailers can collect and analyze data and use that in customizing shopping experiences.
Furthermore, retailers can use it to plan for supply and to gain deep customer insights, potentially giving them an advantage over competitors.
ML in Oil and Gas
Machine learning is vital in the oil and gas industry. For example, Engineers use it to explore new sources of energy. They can also analyze ground mineral sources. It becomes easier to predict the failure of refinery sensors.
Further, there are often vast amounts of data being generated in this industry, and machine learning is vital to ensuring that that data is mined efficiently.
ML in Transport Industry
Is your business in the transport industry? Then, you understand the importance of identifying trends. For example, you should constantly identify more efficient routes. You can also use ML to actively mitigate possible issues that human intervention may miss.
Further, machine learning can identify cost-saving patterns as it uses past data to predict future performance, allowing you to identify potential expenses and opportunities.
Machine Learning FAQ
So, what is machine learning? At its core is the process of feeding raw data to a computer system. You then train the computer system using Artificial Intelligence to make accurate predictions. For example, the predictions may involve:
- determining whether a piece of fruit in an image is mango or orange
- detecting human beings in front of a self-driving vehicle
- determining whether the word book in a text relates to a hotel reservation
- spotting spam emails
- recognizing speeches and generate captions for a video
Traditional computer software lacks ML software algorithms. They cannot direct the system to spot an orange or a mango.
However, the ML algorithms model has intelligent code. It can identify a mango from an orange. How? We just train it on a large number of images. Such images are labeled as having an orange or a mango. Then the magic begins.
Developing a Machine learning algorithm for business applications is currently enjoying a huge amount of success. It's helping businesses dramatically reduce the time and complexity of processes. However, it is just one way of achieving 'Artificial Intelligence'.
Mostly, ML achieves this by doing what people could do, only much much faster and more accurately. For example, an ML algorithm took a complex insurance process at Aon insurance from a 10 day process to a 10 hour process. What machine learning did for Aon it can do for you.
Interestingly, using an algorithm in machine learning has been around since the 1950s. The big difference now is the power of cloud computing. Cloud computing has effectively put the machine learning algorithm on steroids.
On the other hand, true AI systems display at least some of these attributes:
- creativity and social intelligence
In other words, a human like robot for example.
Data scientists can use other approaches to create AI systems though. These approaches include:
Evolutionary computation - This involves random mutations between generations of algorithms. The purpose is to attempt to evolve optimal solutions.
Expert systems - programming of computers with specific rules. These rules enable the machines to mimic a human expert's behaviour. For example, when flying a plane using an autopilot system.
The supervised machine learning approach utilizes a lot of examples. Algorithms achieve this by exposing systems to vast amounts of labelled data.
Consider annotated images of handwritten figures as an example. Here, the images correspond with the numbers. Because of this, supervised learning algorithm/software can determine the written numbers.The key takeaway is that supervised learning requires a large number of labelled data sets. In fact, you may have to expose the systems' algorithms to millions of examples to achieve consistency and reliability.
Unsupervised learning task attempts to identify resemblances that divide the data. For example, each day Google News clusters stories on similar topics. Airbnb also uses unsupervised learning in clustering available houses.
The engineers don't design such algorithms to single out specific data. The learning attempts to identify data that can be clustered. It uses similar attributes in this process. It can also recognize anomalies that separate the data sets.
The semi-supervised learning models combines both processes. It uses limited labelled data. The amount of unlabeled data in this learning is vast.
In semi-learning, the engineers perform a procedure called pseudo-labelling. They apply the labelled data in partial training of the ML model and use the model to label the unlabeled data. They train the algorithm model on the labeled and pseudo-labeled data.
Vast sets of labeled data may become less important over time. Many industries are now using semi-supervised machine learning.
Reinforced machine learning is easier to understand. Think about how you learned a particular skill, for example playing a computer game well.
At first, you were a complete novice that didn't understand the rules of the game. However, by playing the game frequently your performance got better with time. That's because you continuously looked at how the buttons related to each other, each moment. The continuous happenings on the screen and your in-game experiences reinforced your skills.
Reinforcement learning works in the same way. For example, Google Deep Q-network uses a reinforcement machine learning model. Its algorithm has now surpassed the human mind when playing vintage video games.
Just as in the Google deep Q-network example, data researchers feed the system with pixels from each game. They then determine different information concerning the game's status—for instance, the distance from one object to another on the screen. The system then learns how its actions relate to its score. Repeated cycles of playing enhance the creation of the machine learning models. Such models can determine which actions will reduce the score.
Consider the video game of Breakout. The machine learning software will recognize where the paddle should move. That way, it will intercept the ball.
In more real-world applications, reinforcement learning can be used for anything from teaching robots in a manufacturing plant how to take one of many identical objects out of a box, to managing electricity grids more safely and efficiently, or even improving investment decisions for large hedge funds.