/home/leansigm/public_html/components/com_easyblog/services<title>Working with Machine Learning</title>
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	<h1>Working with Machine Learning</h1>
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			<span class="post-date">Jun 29</span>

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				Posted by <a href="/forum/user/2457-mehak-tawakley">Mehak Tawakley</a>				in <a href="/blog/categories/technology">Technology</a>			</span>
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	<p><a href="/our-services/it-services.html" target="_blank" rel="noopener noreferrer">Artificial Intelligence</a>, <a href="/our-services/it-services.html" target="_blank" rel="noopener noreferrer">Machine Learning</a> and <a href="/our-services/it-services.html" target="_blank" rel="noopener noreferrer">Deep Learning</a> are relatively newer technologies invading the fields of information technology, business etc. Though developers are walking towards this era, currently the number of experts is relatively less. The company often makes mistakes by starting up with the technologies instead of focusing on business needs. They often make mistakes by assigning out of domain work to some. For e.g. Hiring data scientists and asking them to build something interested from given database. Rather than a team must be formed of product managers, data engineers, data scientist and DevOps engineers.A team of four will be a kick start to improve our process and giving better results. Now everybody has an opportunity to improve the models, optimise the deployment and scale the business. </p>
<p>Talking about ML, many projects fail due to complex structures. This could occur because of working on wrong problem, to having wrong data, failing to build a model or failing to deploy it correctly. Read more at: <a href="https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382" target="_blank" rel="noopener noreferrer">https://medium.com/@guyernest/the-flywheel-of-machine-learning-systems-50aa6d992382</a></p></div>

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