Machine learning is an important part of data science. It uses the data that organizations gather combined with programmed algorithms to try and imitate the way that the human brain learns. Machine learning helps organizations implement artificial intelligence (AI) and get the most value out of their available data.
Machine learning algorithms can be trained to carry out important tasks like making classifications and predictions and uncovering data insights. In theory, machine learning could eventually make it possible for computers to continually learn on their own, without programming or human intervention.
Artificial intelligence vs. machine learning vs. deep learning
Artificial intelligence uses machine learning, and other techniques like deep learning, to solve problems and complete tasks quickly while avoiding human error. It is a broad term for the data science goal of mimicking human learning and abilities.
Machine learning is actually a subset of artificial intelligence that teaches machines how to learn. It focuses on data structure and creating iterative approaches that can easily be automated. A machine learning algorithm doesn’t test theories against existing data. It learns from that data and tests its findings on new data sets to determine whether or not the rules still apply correctly.
While we won’t be diving deep into deep learning here, you may see the term in a discussion about machine learning and artificial intelligence. Deep learning is a sub-field of machine learning that uses neural networks to tackle complex patterns and high volume data sets. Machine learning requires humans to establish hierarchies, while deep learning detects which features in the data set are most important on their own.
Why is machine learning important for your business?
Machine learning can be a solid foundation for businesses that want to create more value from their data. Its insights improve business intelligence by helping organizations identify patterns and make accurate predictions for data-driven decisions. They can forecast and take advantage of opportunities for growth and profit that have been overlooked. They can also identify unnoticed risks. Machine learning fuels artificial intelligence’s automation, helping teams improve processes and complete tasks faster and with less error than before.
How does machine learning power AI and help organizations get value from their data?
The goal of artificial intelligence is to create “smarter” machines, machines that can process, think, and learn as humans do — or better than humans do. Machine learning is one of the many ways data science works to make this goal a reality.
Machine learning needs data, lots of data. It wasn’t so long ago that the amount of data readily available was fairly small and difficult to process and store. As computer processing and data storage advanced, the number of devices and machines that connect to the internet and stream large amounts of data grew, too. More data and better tools paved the way for machine learning. There is more information for machine learning to learn from than ever before.
Machine learning powers artificial intelligence systems like automation and speech and image recognition. These tools in turn make it possible for organizations to learn about their customers’ behaviors and desires and then create simplified and personalized processes. This builds brand loyalty and helps close sales. Internally, AI can help employees improve operations by automating mundane tasks and eliminating human error.
While there are many machine learning tools on the market, there are still many more organizations that don’t have extensive data science knowledge or personnel to create machine learning models. So, an ideal machine learning tool should fill in the gaps.
Find a tool that can:
Connect data from any cloud, on-premises, or proprietary system
Prepare and cleanse data sets
Give you model options to choose from
Build machine learning models into your data pipelines
Write predictive data back to your source systems
Share data insights visually throughout your organization
Help with data governance by using data permissions
Discover automated machine learning (AutoML).
Domo’s automated machine learning (AutoML) helps businesses like yours augment analytics with machine learning insights. With AutoML, AI and machine learning can be easy for everyone no matter their level of data science expertise. Its deep integration with Amazon SageMaker Autopilot means teams can determine the best machine learning models for their data and share insights at lightning speed–hours instead of weeks or months.
How do businesses use machine learning and AI?
Businesses in every industry are finding new ways to use machine learning and AI every day. They are putting their data to work and reaping the rewards. Here are a few examples:
Recommendations. From your favorite streaming sites to online retail, machine learning and AI make it possible for businesses to use past behavior and trends to predict which types of products you may want to engage with in the future.
Image recognition. Image recognition can be used to auto-generate tags for photos on social media, help categorize images in large batches, and unlock your phone and favorite apps.
Fraud detection. Financial services use machine learning to detect and prevent fraudulent transactions.
Speech recognition. Anytime you speak to Siri, Alexa, or Google Voice Assistant, you benefit from machine learning and AI.
Natural language interfaces. Those helpful chatbots that manage your return, help you find the resources you need, and even book appointments are all made possible with machine learning.
Supply chain optimization. Machine learning and AI models can be put to work building systems that identify risks to on-time fulfillment in the supply chain. They can also be used to test prescribed optimizations to determine the best course of action for improvements across the product life cycle.
Lead qualification. With AI and machine learning, businesses can better identify which leads are most likely to convert into a sale. Models can be created to help score leads based on how well they fit an organization’s successful conversion patterns. This helps sales teams prioritize leads and know where to focus their efforts for the most ROI.
How will machine learning and AI evolve in the future?
Machine learning and AI are the future of business. They open up new ways to increase business intelligence, productivity, and customer satisfaction. In the coming years, the majority of all companies across industries will incorporate machine learning and AI into their everyday processes to remain competitive and get the most value possible out of their data.
Check out some related resources:
3 Reasons Why BI & Analytics Is the Top CFO Initiative
Putting Data in the Hands of Online Retailers with Domo
Domo Named a Leader in The Forrester Wave™: Augmented BI Platforms, Q3 2021
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