www.pluralsight.com Open in urlscan Pro
2606:4700::6811:2355  Public Scan

Form analysis
4 forms found in the DOM

<form class="header-search-form">
  <input class="header-search-input" type="text" name="q" placeholder="What do you want to learn?" autocomplete="off">
</form>

<form class="header-search-form -flex-and-center">
  <input class="header-search-input flex-1" type="text" name="q" placeholder="Search" autocomplete="off">
  <button type="submit">
    <svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" role="button">
      <title>A search magnifying glass</title>
      <mask id="mask0_3541_6692" style="mask-type:luminance" maskUnits="userSpaceOnUse" x="2" y="2" width="20" height="20">
        <path fill-rule="evenodd" clip-rule="evenodd"
          d="M21.3534 19.9404L21.3535 19.9404L16.314 14.9C17.403 13.504 18 11.799 18 10C18 7.863 17.167 5.856 15.656 4.344C14.145 2.832 12.137 2 10 2C7.863 2 5.854 2.832 4.344 4.344C2.832 5.856 2 7.863 2 10C2 12.137 2.832 14.146 4.344 15.656C5.854 17.168 7.863 18 10 18C11.799 18 13.504 17.404 14.9 16.315L19.9394 21.3544C20.1347 21.5497 20.4513 21.5497 20.6466 21.3544L21.3534 20.6476L21.3534 20.6475C21.5487 20.4523 21.5487 20.1357 21.3534 19.9404ZM14.242 14.243C13.109 15.376 11.602 16 10 16C8.397 16 6.891 15.376 5.758 14.243C4.624 13.11 4 11.603 4 10C4 8.398 4.624 6.891 5.758 5.758C6.891 4.624 8.397 4 10 4C11.602 4 13.109 4.624 14.242 5.758C15.376 6.891 16 8.398 16 10C16 11.603 15.376 13.11 14.242 14.243Z"
          fill="white"></path>
      </mask>
      <g mask="url(#mask0_3541_6692)">
        <rect width="24" height="24" fill="#A5AACF"></rect>
      </g>
    </svg>
  </button>
</form>

<form id="customMarketo_1298" data-mkto-id="1298">
  <div class="marketo-form-field">
    <label for="FirstName" class="mrkto_text_lbl">First Name<span class="requiredAsterix">*</span></label>
    <input type="text" id="1298_FirstName" class="mrkto_text" name="FirstName" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="LastName" class="mrkto_text_lbl">Last Name<span class="requiredAsterix">*</span></label>
    <input type="text" id="1298_LastName" class="mrkto_text" name="LastName" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="Email" class="mrkto_text_lbl">Email Address<span class="requiredAsterix">*</span></label>
    <input type="email" id="1298_Email" class="mrkto_email" name="Email" required="" oninvalid="setCustomValidity('Must be valid email. example@yourdomain.com)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="Company" class="mrkto_text_lbl">Company<span class="requiredAsterix">*</span></label>
    <input type="text" id="1298_Company" class="mrkto_text" name="Company" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="Title" class="mrkto_text_lbl">Job Title<span class="requiredAsterix">*</span></label>
    <input type="text" id="1298_Title" class="mrkto_text" name="Title" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="Phone" class="mrkto_text_lbl">Phone<span class="requiredAsterix">*</span></label>
    <input type="text" id="1298_Phone" class="mrkto_text" name="Phone" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <label for="Country" class="mrkto_select_lbl">Country<span class="requiredAsterix">*</span></label>
    <select id="1298_Country" class="mrkto_select" name="Country" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')">
      <option value="">Select...</option>
      <option value="United States">United States</option>
      <option value="Afghanistan">Afghanistan</option>
      <option value="Aland Islands">Aland Islands</option>
      <option value="Albania">Albania</option>
      <option value="Algeria">Algeria</option>
      <option value="American Samoa">American Samoa</option>
      <option value="Andorra">Andorra</option>
      <option value="Angola">Angola</option>
      <option value="Anguilla">Anguilla</option>
      <option value="Antarctica">Antarctica</option>
      <option value="Antigua and Barbuda">Antigua and Barbuda</option>
      <option value="Argentina">Argentina</option>
      <option value="Armenia">Armenia</option>
      <option value="Aruba">Aruba</option>
      <option value="Australia">Australia</option>
      <option value="Austria">Austria</option>
      <option value="Azerbaijan">Azerbaijan</option>
      <option value="Bahamas">Bahamas</option>
      <option value="Bahrain">Bahrain</option>
      <option value="Bangladesh">Bangladesh</option>
      <option value="Barbados">Barbados</option>
      <option value="Belarus">Belarus</option>
      <option value="Belgium">Belgium</option>
      <option value="Belize">Belize</option>
      <option value="Benin">Benin</option>
      <option value="Bermuda">Bermuda</option>
      <option value="Bhutan">Bhutan</option>
      <option value="Bolivia">Bolivia</option>
      <option value="Bonaire, Saint Eustatius and Saba">Bonaire, Saint Eustatius and Saba</option>
      <option value="Bosnia and Herzegovina">Bosnia and Herzegovina</option>
      <option value="Botswana">Botswana</option>
      <option value="Bouvet Island">Bouvet Island</option>
      <option value="Brazil">Brazil</option>
      <option value="British Indian Ocean Territory">British Indian Ocean Territory</option>
      <option value="Brunei Darussalam">Brunei Darussalam</option>
      <option value="Bulgaria">Bulgaria</option>
      <option value="Burkina Faso">Burkina Faso</option>
      <option value="Burundi">Burundi</option>
      <option value="Cambodia">Cambodia</option>
      <option value="Cameroon">Cameroon</option>
      <option value="Canada">Canada</option>
      <option value="Cape Verde">Cape Verde</option>
      <option value="Cayman Islands">Cayman Islands</option>
      <option value="Central African Republic">Central African Republic</option>
      <option value="Chad">Chad</option>
      <option value="Chile">Chile</option>
      <option value="China">China</option>
      <option value="Christmas Island">Christmas Island</option>
      <option value="Cocos (Keeling) Islands">Cocos (Keeling) Islands</option>
      <option value="Colombia">Colombia</option>
      <option value="Comoros">Comoros</option>
      <option value="Congo">Congo</option>
      <option value="Congo the Democratic Republic of the">Democratic Republic of the Congo</option>
      <option value="Cook Islands">Cook Islands</option>
      <option value="Costa Rica">Costa Rica</option>
      <option value="Cote d'Ivoire">Cote d'Ivoire</option>
      <option value="Croatia">Croatia</option>
      <option value="Cuba">Cuba</option>
      <option value="Curacao">Curacao</option>
      <option value="Cyprus">Cyprus</option>
      <option value="Czech Republic">Czech Republic</option>
      <option value="Denmark">Denmark</option>
      <option value="Djibouti">Djibouti</option>
      <option value="Dominica">Dominica</option>
      <option value="Dominican Republic">Dominican Republic</option>
      <option value="Ecuador">Ecuador</option>
      <option value="Egypt">Egypt</option>
      <option value="El Salvador">El Salvador</option>
      <option value="Equatorial Guinea">Equatorial Guinea</option>
      <option value="Eritrea">Eritrea</option>
      <option value="Estonia">Estonia</option>
      <option value="Ethiopia">Ethiopia</option>
      <option value="Falkland Islands (Malvinas)">Falkland Islands (Malvinas)</option>
      <option value="Faroe Islands">Faroe Islands</option>
      <option value="Fiji">Fiji</option>
      <option value="Finland">Finland</option>
      <option value="France">France</option>
      <option value="French Guiana">French Guiana</option>
      <option value="French Polynesia">French Polynesia</option>
      <option value="French Southern Territories">French Southern Territories</option>
      <option value="Gabon">Gabon</option>
      <option value="Gambia">Gambia</option>
      <option value="Georgia">Georgia</option>
      <option value="Germany">Germany</option>
      <option value="Ghana">Ghana</option>
      <option value="Gibraltar">Gibraltar</option>
      <option value="Greece">Greece</option>
      <option value="Greenland">Greenland</option>
      <option value="Grenada">Grenada</option>
      <option value="Guadeloupe">Guadeloupe</option>
      <option value="Guam">Guam</option>
      <option value="Guatemala">Guatemala</option>
      <option value="Guernsey">Guernsey</option>
      <option value="Guinea">Guinea</option>
      <option value="Guinea-Bissau">Guinea-Bissau</option>
      <option value="Guyana">Guyana</option>
      <option value="Haiti">Haiti</option>
      <option value="Heard Island and McDonald Islands">Heard Island and McDonald Islands</option>
      <option value="Holy See (Vatican City State)">Holy See (Vatican City State)</option>
      <option value="Honduras">Honduras</option>
      <option value="Hong Kong">Hong Kong</option>
      <option value="Hungary">Hungary</option>
      <option value="Iceland">Iceland</option>
      <option value="India">India</option>
      <option value="Indonesia">Indonesia</option>
      <option value="Iran">Iran</option>
      <option value="Iraq">Iraq</option>
      <option value="Ireland">Ireland</option>
      <option value="Isle of Man">Isle of Man</option>
      <option value="Israel">Israel</option>
      <option value="Italy">Italy</option>
      <option value="Jamaica">Jamaica</option>
      <option value="Japan">Japan</option>
      <option value="Jersey">Jersey</option>
      <option value="Jordan">Jordan</option>
      <option value="Kazakhstan">Kazakhstan</option>
      <option value="Kenya">Kenya</option>
      <option value="Kiribati">Kiribati</option>
      <option value="Korea, Republic of">Korea</option>
      <option value="Kosovo">Kosovo</option>
      <option value="Kuwait">Kuwait</option>
      <option value="Kyrgyzstan">Kyrgyzstan</option>
      <option value="Lao People's Democratic Republic">Lao People's Democratic Republic</option>
      <option value="Latvia">Latvia</option>
      <option value="Lebanon">Lebanon</option>
      <option value="Lesotho">Lesotho</option>
      <option value="Liberia">Liberia</option>
      <option value="Libyan Arab Jamahiriya">Libyan Arab Jamahiriya</option>
      <option value="Liechtenstein">Liechtenstein</option>
      <option value="Lithuania">Lithuania</option>
      <option value="Luxembourg">Luxembourg</option>
      <option value="Macao">Macao</option>
      <option value="Macedonia, the Former Yugoslav Republic of">Republic of Macedonia</option>
      <option value="Madagascar">Madagascar</option>
      <option value="Malawi">Malawi</option>
      <option value="Malaysia">Malaysia</option>
      <option value="Maldives">Maldives</option>
      <option value="Mali">Mali</option>
      <option value="Malta">Malta</option>
      <option value="Marshall Islands">Marshall Islands</option>
      <option value="Martinique">Martinique</option>
      <option value="Mauritania">Mauritania</option>
      <option value="Mauritius">Mauritius</option>
      <option value="Mayotte">Mayotte</option>
      <option value="Mexico">Mexico</option>
      <option value="Micronesia, Federated States of">Federated States of Micronesia</option>
      <option value="Moldova, Republic of">Republic of Moldova</option>
      <option value="Monaco">Monaco</option>
      <option value="Mongolia">Mongolia</option>
      <option value="Montenegro">Montenegro</option>
      <option value="Montserrat">Montserrat</option>
      <option value="Morocco">Morocco</option>
      <option value="Mozambique">Mozambique</option>
      <option value="Myanmar">Myanmar</option>
      <option value="Namibia">Namibia</option>
      <option value="Nauru">Nauru</option>
      <option value="Nepal">Nepal</option>
      <option value="Netherlands">Netherlands</option>
      <option value="Netherlands Antilles">Netherlands Antilles</option>
      <option value="New Caledonia">New Caledonia</option>
      <option value="New Zealand">New Zealand</option>
      <option value="Nicaragua">Nicaragua</option>
      <option value="Niger">Niger</option>
      <option value="Nigeria">Nigeria</option>
      <option value="Niue">Niue</option>
      <option value="Norfolk Island">Norfolk Island</option>
      <option value="Northern Mariana Islands">Northern Mariana Islands</option>
      <option value="Norway">Norway</option>
      <option value="Oman">Oman</option>
      <option value="Pakistan">Pakistan</option>
      <option value="Palau">Palau</option>
      <option value="Palestinian Territory, Occupied">Palestinian Territory</option>
      <option value="Panama">Panama</option>
      <option value="Papua New Guinea">Papua New Guinea</option>
      <option value="Paraguay">Paraguay</option>
      <option value="Peru">Peru</option>
      <option value="Philippines">Philippines</option>
      <option value="Pitcairn">Pitcairn</option>
      <option value="Poland">Poland</option>
      <option value="Portugal">Portugal</option>
      <option value="Puerto Rico">Puerto Rico</option>
      <option value="Qatar">Qatar</option>
      <option value="Reunion">Reunion</option>
      <option value="Romania">Romania</option>
      <option value="Russian Federation">Russian Federation</option>
      <option value="Rwanda">Rwanda</option>
      <option value="Saint Barthelemy">Saint Barthelemy</option>
      <option value="Saint Helena">Saint Helena</option>
      <option value="Saint Kitts and Nevis">Saint Kitts and Nevis</option>
      <option value="Saint Lucia">Saint Lucia</option>
      <option value="Saint Martin (French part)">Saint Martin</option>
      <option value="Saint Pierre and Miquelon">Saint Pierre and Miquelon</option>
      <option value="Saint Vincent and the Grenadines">Saint Vincent and the Grenadines</option>
      <option value="Samoa">Samoa</option>
      <option value="San Marino">San Marino</option>
      <option value="Sao Tome and Principe">Sao Tome and Principe</option>
      <option value="Saudi Arabia">Saudi Arabia</option>
      <option value="Senegal">Senegal</option>
      <option value="Serbia">Serbia</option>
      <option value="Seychelles">Seychelles</option>
      <option value="Sierra Leone">Sierra Leone</option>
      <option value="Singapore">Singapore</option>
      <option value="Sint Maarten">Sint Maarten</option>
      <option value="Slovakia">Slovakia</option>
      <option value="Slovenia">Slovenia</option>
      <option value="Solomon Islands">Solomon Islands</option>
      <option value="Somalia">Somalia</option>
      <option value="South Africa">South Africa</option>
      <option value="South Georgia and the South Sandwich Islands">South Georgia and the South Sandwich Islands</option>
      <option value="South Sudan">South Sudan</option>
      <option value="Spain">Spain</option>
      <option value="Sri Lanka">Sri Lanka</option>
      <option value="Sudan">Sudan</option>
      <option value="Suriname">Suriname</option>
      <option value="Svalbard and Jan Mayen">Svalbard and Jan Mayen</option>
      <option value="Swaziland">Swaziland</option>
      <option value="Sweden">Sweden</option>
      <option value="Switzerland">Switzerland</option>
      <option value="Syria">Syria</option>
      <option value="Taiwan">Taiwan</option>
      <option value="Tajikistan">Tajikistan</option>
      <option value="Tanzania, United Republic of">United Republic of Tanzania</option>
      <option value="Thailand">Thailand</option>
      <option value="Timor-Leste">Timor-Leste</option>
      <option value="Togo">Togo</option>
      <option value="Tokelau">Tokelau</option>
      <option value="Tonga">Tonga</option>
      <option value="Trinidad and Tobago">Trinidad and Tobago</option>
      <option value="Tunisia">Tunisia</option>
      <option value="Turkey">Turkey</option>
      <option value="Turkmenistan">Turkmenistan</option>
      <option value="Turks and Caicos Islands">Turks and Caicos Islands</option>
      <option value="Tuvalu">Tuvalu</option>
      <option value="Uganda">Uganda</option>
      <option value="Ukraine">Ukraine</option>
      <option value="United Arab Emirates">United Arab Emirates</option>
      <option value="United Kingdom">United Kingdom</option>
      <option value="United States Minor Outlying Islands">United States Minor Outlying Islands</option>
      <option value="Uruguay">Uruguay</option>
      <option value="Uzbekistan">Uzbekistan</option>
      <option value="Vanuatu">Vanuatu</option>
      <option value="Venezuela">Venezuela</option>
      <option value="Viet Nam">Viet Nam</option>
      <option value="Virgin Islands, British">Virgin Islands, British</option>
      <option value="Virgin Islands, U.S.">Virgin Islands, U.S.</option>
      <option value="Wallis and Futuna">Wallis and Futuna</option>
      <option value="Yemen">Yemen</option>
      <option value="Zambia">Zambia</option>
      <option value="Zimbabwe">Zimbabwe</option>
    </select>
  </div>
  <div class="marketo-form-field">
    <label for="License_Count__c" class="mrkto_select_lbl">How many licenses will you need?<span class="requiredAsterix">*</span></label>
    <select id="1298_License_Count__c" class="mrkto_select" name="License_Count__c" required="" oninvalid="setCustomValidity('This field is required.)" oninput="setCustomValidity('')">
      <option value="">Select...</option>
      <option value="1">1 User</option>
      <option value="2">2 to 10</option>
      <option value="11">11 to 20</option>
      <option value="21">21 to 50</option>
      <option value="51">51+</option>
    </select>
  </div>
  <div class="marketo-form-field">
    <span>By filling out this form and clicking submit, you acknowledge our<span>&nbsp;</span></span><a href="https://www.pluralsight.com/privacy" target="_blank">privacy policy</a><span>.</span>
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_UTM_Source__c" class="mrkto_hidden" name="UTM_Source__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_UTM_Medium__c" class="mrkto_hidden" name="UTM_Medium__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_UTM_Campaign__c" class="mrkto_hidden" name="UTM_Campaign__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_UTM_Content__c" class="mrkto_hidden" name="UTM_Content__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_UTM_Term__c" class="mrkto_hidden" name="UTM_Term__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_mcvisid__c" class="mrkto_hidden" name="mcvisid__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_GCLID__c" class="mrkto_hidden" name="GCLID__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1298_Electronic_Person__c" class="mrkto_hidden" name="Electronic_Person__c">
  </div>
  <div class="marketo-form-field">
    <button type="submit" class="mrkto_submit">Submit</button>
    <input type="hidden" name="formid" value="1298">
  </div>
</form>

<form id="customMarketo_1041" data-mkto-id="1041">
  <div class="marketo-form-field">
    <label for="Email" class="mrkto_text_lbl">Email Address:<span class="requiredAsterix">*</span></label>
    <input type="email" id="1041_Email" class="mrkto_email" name="Email" required="" oninvalid="setCustomValidity('Must be valid email. example@yourdomain.com)" oninput="setCustomValidity('')" maxlength="255">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_UTM_Source__c" class="mrkto_hidden" name="UTM_Source__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_UTM_Medium__c" class="mrkto_hidden" name="UTM_Medium__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_UTM_Campaign__c" class="mrkto_hidden" name="UTM_Campaign__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_UTM_Content__c" class="mrkto_hidden" name="UTM_Content__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_UTM_Term__c" class="mrkto_hidden" name="UTM_Term__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_mcvisid__c" class="mrkto_hidden" name="mcvisid__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_GCLID__c" class="mrkto_hidden" name="GCLID__c">
  </div>
  <div class="marketo-form-field">
    <input type="hidden" id="1041_Electronic_Person__c" class="mrkto_hidden" name="Electronic_Person__c">
  </div>
  <div class="marketo-form-field">
    <input type="checkbox" id="1041_Single_Opt_In__c" class="mrkto_checkbox" name="Single_Opt_In__c" value="yes">
    <label for="1041_Single_Opt_In__c" class="mrkto_checkbox_lbl">I would like to receive emails from Pluralsight</label>
  </div>
  <div class="marketo-form-field">
    <button type="submit" class="mrkto_submit">Submit</button>
    <input type="hidden" name="formid" value="1041">
  </div>
</form>

Text Content

Skip to content
 * Pluralsight
 * Skills
 * A Cloud Guru
 * Flow
 * Blog


An avatar icon Sign in
 * A skills logo
   
   Sign in to Skills
   
   The Skills product logo icon
   
 * A Cloud Guru small logo icon
   
   Sign in to A Cloud Guru
   
   A Cloud Guru logo, color version
   
 * Flow product logo
   
   Sign in to Flow
   
   The Flow product logo icon
   

The Pluralsight logo, color version
 * Explore
 * Software dev
 * Cloud
 * IT Ops
 * Data
 * Security
 * Leadership

 * A search magnifying glass icon
   A search magnifying glass icon
   
 * Contact sales
 * View plans

Close Icon

Sign in Menu
 *  * A skills logo
      
      Sign in to Skills
      
      The Skills product logo icon
      
    * A Cloud Guru small logo icon
      
      Sign in to A Cloud Guru
      
      A Cloud Guru logo, color version
      
    * Flow product logo
      
      Sign in to Flow
      
      The Flow product logo icon
      

 *  * Pluralsight
    * Skills
    * A Cloud Guru
    * Flow
    * Blog

A search magnifying glass
 * Explore
 * Software dev
 * Cloud
 * IT Ops
 * Data
 * Security
 * Leadership

 * Contact sales
 * View plans


CONTACT SALES

First Name*
Last Name*
Email Address*
Company*
Job Title*
Phone*
Country* Select... United States Afghanistan Aland Islands Albania Algeria
American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina
Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados
Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bonaire, Saint Eustatius and
Saba Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean
Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon
Canada Cape Verde Cayman Islands Central African Republic Chad Chile China
Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Democratic
Republic of the Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Curacao
Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt
El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands
(Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia
French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece
Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana
Haiti Heard Island and McDonald Islands Holy See (Vatican City State) Honduras
Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Isle of Man Israel
Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Korea Kosovo Kuwait
Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia
Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Republic of
Macedonia Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands
Martinique Mauritania Mauritius Mayotte Mexico Federated States of Micronesia
Republic of Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique
Myanmar Namibia Nauru Nepal Netherlands Netherlands Antilles New Caledonia New
Zealand Nicaragua Niger Nigeria Niue Norfolk Island Northern Mariana Islands
Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea
Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion
Romania Russian Federation Rwanda Saint Barthelemy Saint Helena Saint Kitts and
Nevis Saint Lucia Saint Martin Saint Pierre and Miquelon Saint Vincent and the
Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia
Seychelles Sierra Leone Singapore Sint Maarten Slovakia Slovenia Solomon Islands
Somalia South Africa South Georgia and the South Sandwich Islands South Sudan
Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden
Switzerland Syria Taiwan Tajikistan United Republic of Tanzania Thailand
Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan
Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United
Kingdom United States Minor Outlying Islands Uruguay Uzbekistan Vanuatu
Venezuela Viet Nam Virgin Islands, British Virgin Islands, U.S. Wallis and
Futuna Yemen Zambia Zimbabwe
How many licenses will you need?* Select... 1 User 2 to 10 11 to 20 21 to 50 51+
By filling out this form and clicking submit, you acknowledge our privacy
policy.








Submit

Thank you!

Close button icon

 1. Blog
 2. Blog


WHAT ARE TRANSFORMERS IN GENERATIVE AI?

The Transformer architecture is pivotal in modern natural language processing
(NLP), powering AI tools like ChatGPT. We explain what it is and how it works.

By Kesha Williams

Dec 04, 2023 • 8 Minute Read


 * Data
 * AI & Machine Learning

--------------------------------------------------------------------------------

Subscribe to the newsletter

In the world of artificial intelligence, a force has revolutionized the way we
think about and interact with machines: Transformers. No, not those
shape-shifting toys that morph into trucks or fighter jets! Transformers let AI
models track relationships between chunks of data and derive meaning — much like
you deciphering the words in this sentence. It’s a method that has breathed new
life into natural language models and revolutionized the AI landscape.

In this post, I’ll explain the Transformer architecture, how it powers AI models
like GPT and BERT, and its impact on the future of Generative AI.


HOW DOES GENERATIVE AI WORK?

Generative AI (GenAI) analyzes vast amounts of data, looking for patterns and
relationships, then uses these insights to create fresh, new content that mimics
the original dataset. It does this by leveraging machine learning models,
especially unsupervised and semi-supervised algorithms.

So, what actually does the heavy lifting behind this capability? Neural
networks. These networks, inspired by the human brain, ingest vast amounts of
data through layers of interconnected nodes (neurons), which then process and
decipher patterns in it. These insights can then be used to make predictions or
decisions. With neural networks, we can create diverse content, from graphics
and multimedia to text and even music.

These neural networks adapt and improve over time with experience, forming the
backbone of modern artificial intelligence. Looping back to Transformers, it’s
like the Matrix of Leadership, which allows Optimus Prime to leverage the
knowledge of his ancestors to inform his decisions.

There are three popular techniques for implementing Generative AI: 

 * Generative Adversarial Networks (GANs)

 * Variational Autoencoders (VAEs)

 * Transformers

Examining how the first two work helps provide insight into how transformers
operate, so let’s delve a little deeper into GANs and VAEs.


WHAT ARE GENERATIVE ADVERSARIAL NETWORKS? (GANS)



Generative Adversarial Networks (GANs) are a type of generative model that has
two main components: a generator and a discriminator. The generator tries to
produce data while the discriminator evaluates it.

Let’s use the analogy of the Autobots and Decepticons in the Transformers
franchise. Think of the Autobots as "Generators," trying to mimic and transform
into any vehicle or animal on Earth. On the opposite side, the Decepticons play
the role of "Discriminators," trying to identify which vehicles and animals are
truly Autobots. As they engage, the Autobots fine-tune their outputs, motivated
by the discerning eyes of the Decepticons. Their continuous struggle improves
the generator's ability to create data so convincing that the discriminator
can't tell the real from the fake. 

GANs have many limitations and challenges. For instance, they can be difficult
to train—because of problems such as model collapse, where the generator
produces limited varieties of samples or even the same sample, regardless of the
input. For example, it might repeatedly generate the same type of image rather
than a diversity of outputs.


WHAT ARE VARIATIONAL AUTOENCODERS? (VAES)

Variational Autoencoders (VAEs) are a generative model used mainly in
unsupervised machine learning. They can produce new data that looks like your
input data. The main components of VAEs are the encoder, the decoder, and a loss
function.

Within deep learning, consider VAEs as Cybertron's advanced transformation
chambers. First, the encoder acts like a detailed scanner, capturing a
Transformer's essence into latent variables. Then, the decoder aims to rebuild
that form, often creating subtle variations. This reconstruction, governed by a
loss function, ensures the result mirrors the original while allowing unique
differences. Think of it as reconstructing Optimus Prime's truck form but with
occasional custom modifications.



VAEs have many limitations and challenges. For instance, the loss function in
VAEs can be complex, where striking the right balance between making generated
content look real (reconstruction) and ensuring it's structured correctly
(regularization) can be challenging.


HOW TRANSFORMERS ARE DIFFERENT FROM GANS AND VAES

The Transformer architecture introduced several groundbreaking innovations that
set it apart from Generative AI techniques like GANs and VAEs. Transformer
models understand the interplay of words in a sentence, capturing context.
Unlike traditional models that handle sequences step by step, Transformers
process all parts simultaneously, making them efficient and GPU-friendly.

Imagine the first time you watched Optimus Prime transform from a truck into a
formidable Autobot leader. That’s the leap AI made when transitioning from
traditional models to the Transformer architecture. Multiple projects like
Google’s BERT and OpenAI’s GPT-3 and GPT-4, two of the most powerful generative
AI models, are based on the Transformer architecture. These models can be used
to generate human-like text, help with coding tasks, translate from one language
to the next, and even answer questions on almost any topic.

Additionally, the Transformer architecture's versatility extends beyond text,
showing promise in areas like vision. Transformers' ability to learn from vast
data sources and then be fine-tuned for specific tasks like chat has ushered in
a new era of NLP that includes ground-breaking tools like ChatGPT. In short,
with Transformers, there’s more than meets the eye!


HOW DOES THE TRANSFORMER ARCHITECTURE WORK?

Transformer is an architecture of neural networks that takes a text sequence as
input and produces another text sequence as output. For example, translating
from English (“Good Morning”) to Portuguese (“Bom Dia”). Many popular language
models are trained using this architectural approach. 




THE INPUT

The input is a sequence of tokens, which can be words or subwords, extracted
from the text provided. In our example, that’s “Good Morning.” Tokens are just
chunks of text that hold meaning. In this case, “Good” and “Morning” are both
tokens, and if you added an “!”, that would be a token too.


THE EMBEDDINGS

Once the input is received, the sequence is converted into numerical vectors,
known as embeddings, which capture the context of each token. These embeddings
allow models to process textual data mathematically and understand the intricate
details and relationships of language. Similar words or tokens will have similar
embeddings. 

For example, the word “Good” might be represented by a set of numbers that
capture its positive sentiment and common use as an adjective. That means it
would be positioned closely to other positive or similar-meaning words like
“great” or “pleasant”, allowing the model to understand how these words are
related.

Positional embeddings are also included to help the model understand the
position of a token within a sequence, ensuring the order and relative positions
of tokens are understood and considered during processing. After all, “hot dog”
means something entirely different from “dog hot” - position matters!


THE ENCODER

Now that our tokens have been appropriately marked, they pass through the
encoder. The encoder helps process and prepare the input data — words, in our
case — by understanding its structure and nuances. The encoder contains two
mechanisms: the self-attention and feed-forward mechanisms.

The self-attention mechanism relates every word in the input sequence to every
other word, allowing the process to focus on the most important words. It's like
giving each word a score that represents how much attention it should pay to
every other word in the sentence.

The feed-forward mechanism is like your fine-tuner. It takes the scores from the
self-attention process and further refines the understanding of each word,
ensuring the subtle nuances are captured accurately. This helps optimize the
learning process.


THE DECODER

At the culmination of every epic Transformers battle, there's usually a
transformation, a change that turns the tide. The Transformation architecture is
no different! After the encoder has done its part, the decoder takes the stage.
It uses its own previous outputs — the output embeddings from the previous time
step of the decoder — and the processed input from the encoder.

This dual input strategy ensures that the decoder takes into account both the
original data and what it has produced thus far. The goal is to create a
coherent and contextually appropriate final output sequence. 


THE OUTPUT

At this stage, we’ve got the “Bom Dia” — a new sequence of tokens representing
the translated text. It's just like the final roar of victory from Optimus Prime
after a hard-fought battle! Hopefully, you’ve now got a bit more of an idea of
how a Transformer architecture works.


TRANSFORMER ARCHITECTURE: IT’S CHATGPT’S ALLSPARK

In the Transformer series, the shape-shifting robots were animated by an ancient
artifact called the AllSpark. Much in the same way, the Transformer architecture
is ChatGPT’s AllSpark — the core technology that “brings it to life” (at least
in the sense of allowing it to process and coherently generate language). 

The Generative Pre-trained Transformer (GPT) is a model built using the
Transformer architecture, and ChatGPT is a specialized version of GPT,
fine-tuned for conversational engagement. Thus, the Transformer architecture is
to GPT what the AllSpark is to Transformers: the source that imbues them with
their capabilities.


WHAT’S NEXT FOR TRANSFORMERS AND TOOLS LIKE CHATGPT?

The Transformer architecture has already brought about significant changes in
the AI field, particularly in NLP. There could be even more innovation in the
Generative AI field thanks to the Transformer architecture. 

 * Interactive Content Creation: Generative AI models based on Transformers
   could be used in real-time content creation settings, such as video games,
   where environments, narratives, or characters are generated on the fly based
   on player actions.

 * Real-world Simulations: Generative models can be used for simulations. These
   simulations could become highly realistic, aiding in scientific research,
   architecture, and even medical training.

 * Personalized Generations: Given the adaptability of Transformers, generative
   models might produce content personalized to individual tastes, preferences,
   or past experiences. Think of music playlists, stories, or artworks generated
   based on personal moods or past interactions.

 * Ethical and Societal Implications: With increased generative capabilities
   come challenges. Deepfakes, misinformation, and intellectual property
   concerns are just a few. The evolution of generative AI will require
   mechanisms to detect generated content and ensure ethical use.


CONCLUSION: AN ARCHITECTURE CHANGING AI AS WE KNOW IT

The Transformers architecture is poised to significantly advance the
capabilities and applications of Generative AI, pushing the boundaries of what
machines can create and how they assist in the creative process. And now, having
read this article, you should now have a better grasp on how it works.


WANT TO TRANSFORM YOUR GENAI SKILLS?

If you’re just beginning with Generative AI and ChatGPT, learning prompt
engineering is a great starting point.  Take a look at my course "Prompt
Engineering for Improved Performance" to master advanced techniques in prompt
engineering!



Kesha W.

Kesha Williams is an Atlanta-based AWS Machine Learning Hero, Alexa Champion,
and Director of Cloud Engineering who leads engineering teams building
cloud-native solutions with a focus on growing early career technologists into
cloud professionals and engineering leaders. She holds multiple AWS
certifications and has leadership training from Harvard Business School. Find
her on Topmate at https://topmate.io/kesha_williams.

More about this author



 * SUPPORT
   
   * Contact
   * Help Center
   * IP Allowlist
   * Sitemap
   * Download Pluralsight
   * Skills Plans
   * A Cloud Guru Plans
   * Flow Plans


 * COMMUNITY
   
   * Guides
   * Teach
   * Partner with Pluralsight
   * Affiliate Partners
   * Pluralsight One
   * Authors


 * COMPANY
   
   * About Us
   * Careers
   * Newsroom
   * Resources


 * INDUSTRIES
   
   * Education
   * Financial Services (FSBI)
   * Healthcare
   * Insurance
   * Non-Profit
   * Public Sector


 * NEWSLETTER
   
   Email Address:*
   
   
   
   
   
   
   
   
   I would like to receive emails from Pluralsight
   Submit
   
   Thank you!
   
   * A facebook icon
   * 
   * 
   * 
   * 

--------------------------------------------------------------------------------

Pluralsight logo Copyright © 2004 - 2024 Pluralsight LLC. All rights reserved
 * Terms of Use
 * Privacy Notice
 * Modern Slavery Statement