7 ways to leverage AI and ML<\/strong><\/h2>\n\n\nHere are some ways in which startups are uniquely positioned to leverage their flexibility to make AI and ML efforts successful.<\/p>\n\n\n\n
1. Access a lot of data<\/strong><\/p>\n\n\n\n![\"\"\/](\"https:\/\/lh6.googleusercontent.com\/I-Z3uaYpB0nSCtjRyGLTAOQJ847ZO0i5lA-cCJRyiu5CgvcI7KyGMwZToXJ_Y2qiDsLntqcdErjFv8YgsV6v3CJ_Cx-wAhVe_i_JaSGtFJC43zyZpsIjmGpCzSMsVY6Q4OLk-ATh-EJ_SecRrZlW40bnR7n1H6idxnxJ1_L7muDBUKD0cv1fVwtTxmHMJw\")
<\/figure>\n\n\n\nIt is the era of Big Data, and you can\u2019t do AI or ML today without data. Depending on your product\u2019s scale, you need to ensure that you have access to enough data on the problems you are trying to solve. By being small, tech-savvy, and operating at scale, startups use technologies to digitize whatever material they can, automate whatever tasks they can, and often capture large amounts of data for training models and building AI products.<\/p>\n\n\n\n
Machine Learning projects often run into many cold-start problems, not having data resources and whatnot. Manual effort and one-time solutions can get you out of quick pickles. Still, to eventually come up with a scalable and long-term solution, you will need access to large amounts of data. But on not only what works but also what doesn\u2019t by tracking large datasets over long periods. Because startups that realize this consider data as an asset that they actively try to build.<\/p>\n\n\n\n![\"\"\/](\"https:\/\/lh3.googleusercontent.com\/khpWQ13JONSFiNf8668kAWzB3dAxoAXL7ezHR8K4277iSJegwkSsmX92O5AbYmX2GbCdqwM-rGt99kOI1J-dItEPB7804EO7vO4pqSAvOJKbKWjxDB1OZco6FyFbBYEbiBNOEqvLgWr8n5ZDrtEnwxoRpy9Zo9vd4SxfonDWuyeHq1GoRn9J42IUtusHww\")
<\/figure>\n\n\n\nResults of a recent survey of ML Practitioners on their current data requirements (Source<\/a>)<\/p>\n\n\n\n2. Find the Right Fit<\/strong><\/p>\n\n\n\n![\"\"\/](\"https:\/\/lh6.googleusercontent.com\/VzKQWYGNtjQ738_FhOpr8cdlnvVMBZTT286MY-Oc5GOsrj04a-7a_MuNgqLuVLJ8BFoAEQ7TrbyKD0iqS3yLOmKcN9M0O8BA827Tjc9KDvH4II1nsGwhs4SGEdZ0jHZsZqvRZN6bMf-ldIqUoDiQZ1uizPX-6lk16o6jO9WzWfUPVIQ-FiP50TUH3AcHMw\")
<\/figure>\n\n\n\nAccording to VentureBeat<\/a>, 87% of AI projects don\u2019t make it into production.<\/p>\n\n\n\nCompanies often need time to figure out their product\/service concretely, what features they can realistically offer, how it distinguishes themselves in the market, and many things that can only happen through trial and error. Large corporations come under a lot of scrutiny by both the public and investors. <\/p>\n\n\n\n
At the same time, startups generally can spend a lot more time exploring multiple options and finding the right fit for themselves. One of the best examples that come to mind is Slack Technologies. <\/p>\n\n\n\n
Currently one of the world\u2019s most popular chat and productivity tools \u2013 used by 77% of the Fortune 100, Slack was initially a gaming company called TinySpeck. After shutting down their MMORPG Game \u201cGlitch\u201d in 2012, they went on to port the game\u2019s \u201cfamiliar\u201d pet rock into Slackbot, a friendly bot that helps you find things around gives you notifications, and keeps you informed about the workspace. Slack was launched in 2014, got listed on NYSE in 2019, and was acquired by Salesforce for $27.7 billion in cash and stock.<\/p>\n\n\n\n
Related reading: <\/strong>What Does AIOps Mean for Startups?<\/strong><\/a><\/p>\n\n\n\n3. Minimal Bureaucracy<\/strong><\/p>\n\n\n\n![\"\"\/](\"https:\/\/lh5.googleusercontent.com\/UkcyaJt_HgJyg8cfEnecT30Ky4WemfwXzY9O3Vhzl7TMAWV3lAHlTmRcbqu5zUPjBNwR6Ou-G1VHBugdADVz4N8TogMMfF7Ywak-mDnVDcyxxP6tBK7Pb9QytnXGX9_DPTqiveeimrvu_kGBycCDjmo9oY3Pjxb4LVnY09LzIT20arC3dyYONj8Mjixd_Q\")
<\/figure>\n\n\n\nCompared to large enterprises, startups are more flexible and dynamic in setting up the business and cultural procedures, ensuring that they set processes right with a long-term vision right from the get-go. People wear many hats at startups, but there is also a lot more active direct communication and understanding between the business teams, product teams, data science teams, and domain experts.<\/p>\n\n\n\n
Communication in startups is more comfortable, and analyzing the problem from multiple angles and understanding a lot of different perspectives is simpler. Because of the closer working relationship among team members, it is easier for them to get access to any data point that they may need, and get resources to test out different technologies and operation processes until the right fit for the team(s) has been discovered. This system is also present in more prominent companies, but in startups, the system is cheaper, quicker, and easier to manage.<\/p>\n\n\n\n
<\/figure>\n\n\n\nIt is the era of Big Data, and you can\u2019t do AI or ML today without data. Depending on your product\u2019s scale, you need to ensure that you have access to enough data on the problems you are trying to solve. By being small, tech-savvy, and operating at scale, startups use technologies to digitize whatever material they can, automate whatever tasks they can, and often capture large amounts of data for training models and building AI products.<\/p>\n\n\n\n
Machine Learning projects often run into many cold-start problems, not having data resources and whatnot. Manual effort and one-time solutions can get you out of quick pickles. Still, to eventually come up with a scalable and long-term solution, you will need access to large amounts of data. But on not only what works but also what doesn\u2019t by tracking large datasets over long periods. Because startups that realize this consider data as an asset that they actively try to build.<\/p>\n\n\n\n Results of a recent survey of ML Practitioners on their current data requirements (Source<\/a>)<\/p>\n\n\n\n 2. Find the Right Fit<\/strong><\/p>\n\n\n\n According to VentureBeat<\/a>, 87% of AI projects don\u2019t make it into production.<\/p>\n\n\n\n Companies often need time to figure out their product\/service concretely, what features they can realistically offer, how it distinguishes themselves in the market, and many things that can only happen through trial and error. Large corporations come under a lot of scrutiny by both the public and investors. <\/p>\n\n\n\n At the same time, startups generally can spend a lot more time exploring multiple options and finding the right fit for themselves. One of the best examples that come to mind is Slack Technologies. <\/p>\n\n\n\n Currently one of the world\u2019s most popular chat and productivity tools \u2013 used by 77% of the Fortune 100, Slack was initially a gaming company called TinySpeck. After shutting down their MMORPG Game \u201cGlitch\u201d in 2012, they went on to port the game\u2019s \u201cfamiliar\u201d pet rock into Slackbot, a friendly bot that helps you find things around gives you notifications, and keeps you informed about the workspace. Slack was launched in 2014, got listed on NYSE in 2019, and was acquired by Salesforce for $27.7 billion in cash and stock.<\/p>\n\n\n\n Related reading: <\/strong>What Does AIOps Mean for Startups?<\/strong><\/a><\/p>\n\n\n\n 3. Minimal Bureaucracy<\/strong><\/p>\n\n\n\n Compared to large enterprises, startups are more flexible and dynamic in setting up the business and cultural procedures, ensuring that they set processes right with a long-term vision right from the get-go. People wear many hats at startups, but there is also a lot more active direct communication and understanding between the business teams, product teams, data science teams, and domain experts.<\/p>\n\n\n\n Communication in startups is more comfortable, and analyzing the problem from multiple angles and understanding a lot of different perspectives is simpler. Because of the closer working relationship among team members, it is easier for them to get access to any data point that they may need, and get resources to test out different technologies and operation processes until the right fit for the team(s) has been discovered. This system is also present in more prominent companies, but in startups, the system is cheaper, quicker, and easier to manage.<\/p>\n\n\n\n
![\"\"\/](\"https:\/\/lh4.googleusercontent.com\/XCU9GCOxn-Wv_ij8dtm1j81fZQvcG4Vk1nLPTFJXAHfWTE92N6XUjdABhlh2snlxaIjVEAs64Pt_5kfUw8I06tgghIeFX6ndYDLtZ8RSS_LE_U23In-J7sXx1kYnw3BkA1Ilz5Mu6uQc1XpR5Y2nvZ_xo0DNkFzQwjvix8mFGnXztIFdQNXlNDWsDQXm_g\")
<\/figure>\n\n\n\n