Know The Four Steps To Accelerate Machine Learning Projects

Machine Learning technologies, also called automatic learning, are growing. Once considered a peripheral technology, machine learning has now become an essential tool for a large number of industries, from health to agriculture, including the financial sector. Brights software development agency has extensive experience in machine learning services to develop your business applications.

Whether a business is just starting or is already deploying machine learning technologies, here are four steps to accelerate that deployment.

Organise Your Data

Data is often presented as the main challenge when adopting machine learning. For example, when building its machine learning model, a company should plan that more than half of the time will be spent analysing, preparing, and cleaning the data. This preparation step is essential because it allows the teams to subsequently focus on creating new algorithms.

Organisations should ask themselves three critical questions before adopting machine learning: What data is available today? What data can be made public? A year from now, what data would we like to have started collecting today?

First, we must overcome the difficulties associated with data hugging, that is, the tendency to silo data between teams within the same organization. Addressing this is crucial to gaining a broader view of the managed dataset and ensuring long-term model success. Then, it will be necessary to determine which data must be chosen to store them and invest in tools allowing them to be processed and anonymized if necessary.

Know How To Identify Real Business Problems

To identify potential pain points, think about data availability, business impact, and the ability to apply machine learning based on the team’s skills. In addition, it is essential to balance speed and business value. Rather than trying to embark on a three-year machine learning project, it’s often more beneficial to focus on a few use cases that can be solved in 6-10 months.

Formula 1 is an excellent example, as the organization aimed to offer teams and spectators a complete experience using data collected during races. To do this, Formula 1 analysts have reviewed more than 65 years of data, most of it untapped, to provide a more rewarding experience for spectators and racers.

Using Amazon SageMaker, a fully managed service from AWS, they were then able to train deep learning models on this data, extract performance statistics, race predictions and thus transmit relevant information in a Fraction of a second.

Promoting The Culture Of Machine Learning

To increase the use of machine learning technologies at scale, it is essential to create a culture within the organization and promote them. Leaders and developers need to think together about how to apply machine learning to various business problems.

A mistake many companies make is to isolate technical experts. Working alone, they will build machine learning models that will not scale to business issues. Forming teams, including technical profiles, makes it possible to involve each layer of the organization in the project, making everyday machine learning and its benefits accessible to all.

Similarly, leaders must facilitate the application of machine learning continuously. For example, building the infrastructure needed for large-scale machine learning is a process that slows down innovation. By using tools that span the entire process to build, train, and deploy machine learning models, companies can get to production faster, with much less effort, and at a lower cost.

Machine learning tools are already making a huge difference for organizations, enabling meaningful transformation. Even though companies are struggling, this technology can improve over time. It is, therefore, necessary to accept failures to succeed. By following these steps, the machine learning culture created will play a vital role in long-term success.

Grow Your Teams Through Training

While recruiting highly experienced talent in an already limited field is very competitive and expensive, developing team skills internally through training programs is possible. These help future engineers speed with machine learning, regardless of skill level or industry.

To that end, Amazon has created several training programs to help future engineers get up to speed with machine learning, regardless of skill level or industry.

Implementing machine learning is essential for companies in many sectors, and the challenges associated with this technology can make it seem difficult to access. Yet, implementing simple strategic actions and encouraging machine learning throughout the organization will gain a significant competitive advantage.

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