We develop water-tight ML-based applications like:
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 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 offers 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:
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.
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.
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.
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.
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.
First we have to understand the challenges in your business. We will collect and examine data from various sources. We only deal with relevant data that can enhance your business performance.
Use learning algorithms to transform and clean the raw data. This step helps in improving data quality. It ensures complete and formatted data in the future.
We will create and train models to solve your business issues. After that, our team checks the model's efficiency. They then repeat these steps until you achieve the required accuracy.
We will sit down with you and go through your feedback. We strive to iron out any issue in the data. Once we finish checking, we will proceed with the model deployment process.
Businesses are reaping benefits from Machine learning in various areas. Some of these areas include:
Using ML algorithms, offer sales leaders new insights from past data. It helps them identify the most effective actions for their team. Save your sales team valuable time and only rely on data-based insights and alarms. Ultimately, you should enjoy more closed sales from better-targeted sales campaigns.
Our ML evaluates past price data like discount campaigns. It can also determine the relative success of sales history and promotion measures. The machine learning algorithms then use this data to calculate the best price to set, so you can maximize profits.
ML can analyze the changes that caused some long-term clients to change their buying habits or leave. Better still, we can use ML to identify early warning signs. That way, you can adjust how you deal with your existing customers before it becomes an issue.
Great AI and Machine learning models monitor specific patterns. These patterns form data profiles that consist of attributes, values, and traits. You can then compare the profiles of current clients and predict their value to your business.
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:
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.
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 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:
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.
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.
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.
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.
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.
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.
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:
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:
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.