Applying Deep Learning Techniques in Enterprise IT: Best Practices
Jun 9, 20237 min read
Senior full stack developer and CTO at Ideamotive.
Remember how OpenAI’s ChatGPT exploded into the mainstream and reached 100 million users in January 2023, barely two months after its launch?
A key piece of OpenAI’s stratospheric success comes from applying deep learning techniques when developing their initial natural language models. And they are not the only ones who have succeeded with deep learning.
With the potential to drive innovation, improve efficiency, and enable intelligent decision-making, deep learning is a critical technique that every enterprise IT company must adopt to stay competitive and meet rising customer expectations.
Read on to discover what deep learning is, some valuable use cases of deep learning within the enterprise IT environment, how to execute deep learning with Python, build a strong team that can successfully execute deep learning projects, and other best practices to drive innovation through deep learning.
An Overview of Deep Learning vs Machine Learning
Deep learning is an Artificial Intelligence (AI) technique that involves processing and analysing vast amounts of data to identify patterns, learn from them, and make intelligent decisions. It is a subset of machine learning, and as the name suggests, it goes deeper than what machine learning can achieve.
While machine learning models rely on a few layers of learning algorithms that process input data and make predictions, deep learning utilises multiple layers of artificial neural networks. The neural architecture is designed to mimic how a human brain would learn and make decisions from patterns found within data.
Each layer of this artificial neural network is then trained to progressively learn hierarchical representations of the input data, enabling the network to extract increasingly complex features and patterns which optimise how it performs various tasks.
Use Cases for Deep Learning in Enterprise IT
Deep learning presents an exhilarating prospect for enterprise IT to expedite tasks traditionally performed by humans. Here are a few use cases of how enterprise IT companies can apply deep learning to stay at the forefront of their niche and achieve sustainable growth.
Detecting fraud is paramount for companies in the financial industry, e-commerce sector, insurance sector, and other domains where transactions and sensitive data are involved.
These companies continuously look for solutions that can swiftly identify patterns and anomalies associated with fraudulent behaviour—and they’re willing to pay top dollar for it—as the financial losses incurred from fraud can be substantial.
To address this critical need, enterprise IT companies play a crucial role in advanced fraud detection software that leverages the power of deep learning techniques to:
Enable real-time identification and prevent fraudulent activities.
Analyse complex data sets, identify hidden patterns, and adapt to evolving fraud techniques.
Empower their customers with insights to take immediate action and mitigate losses.
Real-world application: Amazon Amazon Fraud Detector uses a supervised machine learning model called ‘Online Fraud Insights’. This model uses deep learning algorithms that learn from fraud patterns seen at Amazon and Amazon Web Services (AWS). Amazon reports that their Fraud Detector has helped their clients detect fraud up to 95% faster.
Predictive maintenance Asset maintenance is traditionally conducted by following a fixed schedule or whenever equipment breaks down. This traditional approach is not cost-efficient and can lead to unexpected downtime and unnecessary losses. That's where predictive maintenance comes in.
By applying deep learning algorithms to the data collected from sensors, monitoring systems, and computer vision applications, managers can be alerted of patterns and anomalies that may indicate impending equipment failures.
This proactive approach of predictive maintenance enables managers to:
Prioritise maintenance tasks based on the likelihood of failure.
Schedule routine maintenance activities at the most optimal times.
Furthermore, deep learning models can improve their understanding through time by learning from historical data to accurately predict maintenance needs.
Real-world application: C3.ai In 2017, C3.ai developed C3 AI Readiness, a predictive maintenance solution for the U.S. Air Force to help prevent in-flight failures, improve safety, and increase aircraft readiness.
Their solution uses deep learning to analyse data from aircraft telemetry sensors, maintenance, and operating system. The application can then predict subsystem failures and forecast maintenance. It has since provided the Department of Defence with the ability to predict 40% of unscheduled maintenance.
Inventory management Managing inventory is another way deep learning can be applied in an enterprise IT company. Deep learning models can be used to:
Predict future demand accurately by analysing historical sales data, market trends, seasonality, and other relevant factors.
Optimise inventory levels based on demand predictions and data from computer vision tools to avoid stockouts or overstocking.
Optimise the supply chain by analysing data from various sources, including lead time, supplier performance, production capacity, transportation, production, and production processes.
Reduce downtime by proactively identifying bottlenecks, inefficiencies, and potential disruptions.
Detect anomalies in inventory data such as sudden spikes or drops in demand so managers can take corrective actions promptly.
Real-world application: Walmart Managing inventory is critical for a retail giant at Walmart, which is why they infuse AI and deep learning into almost everything they do, from inventory management through to shopping and search.
AI and deep learning played a pivotal role in helping Walmart leap from managing 700,000 to 60 million items online. By using AI and deep learning algorithms to manage tasks like organising inventory data, pricing items, and fixing problems, they have scaled aggressively and remained competitive.
These applications are just the tip of the iceberg. Other areas in which deep learning can be applied include customer service, quality control, sentiment analysis, natural language text generation, healthcare diagnostics, financial analysis, and more.
Now that we’ve explored how the field of deep learning can help enterprise IT companies gain a competitive edge, let's pivot into how to apply deep learning in an organisation with one of the most popular deep learning frameworks, Keras.
Applying Deep Learning with Python
Keras is a popular open-source deep learning framework written in Python, a popular programming language widely used by companies and developers. As a minimalist Python library, Keras makes developing, evaluating, and implementing deep learning models fast and easy.
Keras creator François Chollet is an artificial intelligence researcher who played a significant role in advancing the field of deep learning. His overarching vision for Keras was to make it “an API for human beings, not machines”.
His book, “Deep Learning with Python”, introduces the field of deep learning using Python and how to use the powerful Keras library with intuitive explanations. The primary reason for this approach was for developers to have user-friendly API to easily build deep learning models without being weighed down by low-level implementation details.
Thus, it is highly recommended that enterprise IT companies choose Keras as their framework of choice to implement deep learning and enjoy benefits such as:
User-friendly and intuitive API.
Seamless integration with TensorFlow and other deep learning frameworks.
Extensive community support and resources.
Versatility for building various types of deep learning models.
Efficient prototyping and experimentation capabilities.
Robust and scalable performance for handling large datasets.
Compatibility with both CPU and GPU acceleration for enhanced training speed.
Easy deployment of deep learning models.
Access to pre-trained models and transfer learning capabilities.
Ongoing development and updates by a dedicated team of contributors.
Considerations for Implementing Deep Learning in Enterprise IT
Deep learning is a powerful tool that can be used to solve complex problems in the enterprise IT space. However, implementing deep learning in enterprise IT requires careful consideration of the various factors involved. Here are a few important factors to consider.
Data privacy Deep learning models often require large amounts of data for training, which can include sensitive information. Enterprise IT companies must ensure appropriate measures are in place to protect the data throughout the deep learning process. This includes anonymising data, implementing robust access controls, and complying with relevant data protection regulations.
Compute resources and cost Deep learning models can be computationally intensive and require substantial computing resources, especially for training large-scale models. Some strategies that can help enterprise IT companies manage the costs associated with deep learning implementation include:
Opting for cloud computing servers to minimise investments in on-premise infrastructure.
Optimising and compressing their deep learning models to reduce computational and memory requirements without sacrificing performance.
Optimising job scheduling, workload balancing, and resource sharing to effectively utilise computational resources, minimise idle time, and maximise cost-effectiveness.
Ethical considerations Enterprise IT companies must be mindful of potential biases in training data or models and work towards building fair and unbiased systems. Before rolling out deep learning models, managers should establish clear guidelines ensuring transparency, accountability, and responsible decision-making throughout development and deployment.
Model interpretability While deep learning models can provide remarkable results, managers must be aware of potential limitations that can affect how data is interpreted. As such, managers ought to use the output of deep learning techniques as insights that can support—not dictate—their decision-making process.
Integration with existing systems Managers should consider deep learning solutions that seamlessly integrate with current systems and workflows to minimise disruption. Integration challenges should be addressed as early as possible to ensure a smooth and efficient deployment of deep learning solutions within the enterprise IT environment.
Building a Team with Deep Learning Skills
The next step is building a team with deep learning skills. Here are some effective strategies to follow:
Finding the right talent and assessing their skills There are various avenues to find talent with the right skills in deep learning, such as networking within the industry, collaborating with universities, searching online, or working with trusted recruitment partners who specialise in hiring qualified Python developers.
These avenues can help managers shortlist the candidates to interview and conduct a hands-on skills assessment test, including coding challenges or practical deep learning assignments.
Attract and retain top talent Finding the right talent isn’t enough if a company doesn’t create the right environment that attracts them to join and stay in the team. Here are some incentives that can help companies attract and retain top talent:
Competitive compensation packages
Options to work remotely or hybrid work arrangements
Opportunities for professional growth and development
Access to training programs, conferences, and research collaborations
A supportive work culture with challenging and meaningful projects
Building a talent pipeline Building a talent pipeline that facilitates scaling or staff augmentation as and when needed is important. Some ways to do this include establishing partnerships and internship opportunities with universities, research institutions, and specialised recruitment partners.
Best Practices to Manage a Team for Deep Learning Projects
The final step to successfully implementing deep learning projects is effectively managing the team. Managers of enterprise IT companies can follow these best practices:
Ensure you have a basic understanding of the concepts and terminologies used in deep learning to facilitate effective communication with the team.
Define clear project goals by breaking down complex deep learning projects into phases and manageable tasks.
Collaborate with the team to identify the most appropriate model architectures and algorithms based on the project requirements.
Facilitate regular meetings and discussions between the deep learning team and other stakeholders for effective cross-department collaboration.
Establish a robust deployment pipeline with considerations for scalability, performance, and integration with existing systems.
Deep learning is a powerful technique that enterprise IT companies should not miss out on. Many companies and organisations across different fields, such as Amazon, Walmart, and the U.S. Air Force, have successfully implemented deep learning projects to drive innovation and grow in leaps and bounds.
A popular way to implement deep learning projects is with the open-source platform Keras, which uses deep learning with Python. Hiring the right talent and qualified Python developers will be pivotal to the success of any deep learning project.
Before diving into or scaling your current deep learning project, it’s important to clarify the project’s objectives and how you will navigate other factors, such as data privacy, compute resources, ethical considerations, model interpretability, and integration with existing systems.
With the right direction, considerations, and talent, enterprise IT companies can use deep learning to improve decision-making, deliver value across various aspects of operations, and create a competitive edge to stand out in the market.
Dawid is a full stack developer experienced in creating Ruby on Rails and React Native apps from naught to implementation. Technological superhero, delivering amazing solutions for our clients and helping them grow.