The Machine Learning Transformation

The Machine Learning Transformation

At Textron Systems, digital transformation is core to our strategy, not only from the attitude of how we execute our business but also how our products and services are differentiated. A key enabler to digital transformation is leveraging an ecosystem of ’as-a-service’ offerings that bring the required capabilities and operational performance to our solutions. Beyond the elasticity that these architectures provide, they also enable access to a marketplace of services like machine learning, advanced analytics, and proven algorithms. Machine learning represents a chance for commercial, civil and government organizations alike to drive powerful economies like cost reduction, productivity optimization, and merchandise differentiation. to realize these benefits, organizations must implement machine learning with an eye fixed toward avoiding their commonest failure modes.

First and foremost, data is vital when it involves machine learning. The more data you've got for the advanced algorithms to find out from, the upper the probability of driving desired and meaningful insights. There are inherent challenges to perfecting machine learning insights, starting with the cleanliness, or quality, and availability of excellent data sets to mine, or “learn” from. There are several applied examples where biases in data have skewed the results and accuracy of knowledge insights through machine learning concepts. Training an algorithm is like training a body; the healthier the inputs, the higher the probabilities of improved performance.

While there is an art and a science to picking the right algorithm, the services available across modern SaaS architectures provide access to run models and validate confidence levels without having to have a PhD in Data Science

Equally important to developing successful machine learning insights in choosing the proper algorithm for the proper problem statement. By that specialize in outcomes and developing the talents to understand which model works best that problem, organizations can apply the proper algorithm to satisfy the business need.

As we glance at increased machine learning model performance in applied areas like sensor and imagery analysis, we are leveraging large catalogs of knowledge to coach algorithms. Automating workflows or improving confidence in insights requires A level of accuracy and dependability in data labeling. Where the size may be a challenge, and availability to quality labeled data is required to further mature models, we will leverage the ecosystem for synthetic models. Synthetic models are information objects manufactured, or engineered, by computers instead of real-world events. Synthetic models provide access to large amounts of accurately labeled data inputs that would otherwise take years to catalog. the utilization of synthetics also can assist in improved machine learning where data varies in interactive conditions, like within the case of a spread of pixel resolutions or in-motion changes.

For our organization, the advantages gained from leveraging machine learning concepts and improved insights will include building automated workflows to help in shifting data/human interactive tasks from discerning data inputs to more value-added action or decision-oriented steps. Beyond improved automation, additional benefits include the power to find out at scale for predictive insight.

At Textron Systems, we are “All certain Autonomy”, which holds true for not only the tremendous products and services we deliver to our customers but also for the solutions Information Technology brings to allow our users and program teams; solutions that are original to enable our strategy.

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