AI and DS Skills

There are many courses available for aspiring data scientists.  But there are few offerings available for practicing data scientists to advance their skills.  At the same time, companies are struggling to successfully embed AI into their operations at the speed they need and with impactful business results.

Part of the struggle is that there is much work to do beyond simply building the most effective machine learning model. In order for AI projects to be successful a practitioner needs to engage in every step of the Enterprise AI Workflow.

The Enterprise AI Workflow is defined as the entire process of development, testing, training, deployment, and maintenance of machine learning models in large enterprises.  The workflow is summarized below:

Data Scientists are well trained in all matters of Probability, Statistics, Linear Algebra, Data Preparation and model building.  But without the practical business experience, it is hard to be successful in the enterprise. 

First, it is important to understand the business opportunity and identify the business metrics that need to be optimized. This isn't something that is typically taught in Data Science programs. Finding the data is the next challenge; data needed to solve a business problem isn't always available, it isn't nicely set up like the data sets used in academic environments. Once the data is ready and the model is built, the next challenge is the deployment. There are many considerations and requirements for a model to be deployed in the business. For example, the IT resource requirements to achieve the perfect model accuracy may be too high and decisions to find the ideal accuracy with the appropriate IT resource usage need to be made. Also, because data is a live entity, it morphs and behaves in ways we can't always predict. Monitoring performance and bias and deciding the parameters that kick of a retraining requirement are critical as well as measuring business success.

Today, the Data and Artificial Intelligence Education and Certification team at IBM announced the launch of the IBM Enterprise AI Workflow certification program aimed at helping companies and data scientists overcome these challenges 

The program consists of a 60 question certification test proctored by Pearson VUE and a 6 course (40 hours) specialization on Coursera  to help prepare ML practitioners and data scientists for the test.

The certification test aims to validate that a practitioner has all the skills required to implement an AI solution: it validates knowledge of the scientific and math foundation, how to apply Data Science and AI in business,  understanding and preparing data,  building models, evaluating models, relating model performance to business metrics, deploying models, and skills to do so using Python and some IBM products.

The IBM Enterprise Workflow specialization launched on Coursera uses a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution. It leverages Design Thinking as a framework to work through the translation of business goals into AI technical implementations. It brings together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality. It Leverages Python as the tool of choice for building AI models, and brings in IBM Watson Studio where it adds value to the process on top of Python and other open source tools.

It consists of 6 courses.

1. Business Priorities and Data Ingestion
2. Data analysis and hypothesis testing
3. Feature engineering and bias detection
4. Machine learning, visual recognition and NLP
5. Enterprise model deployment
6. AI in production

The IBM AI Workflow Certification program offers recognition valued by hiring managers because it is tied to real- world experience within a job role or with a specific product/solution; it provides recognition for the development of deep skills within a solution or product area and unlike any other Data Science skills programs which offer badges or certificates obtained after only going through an educational process, the AI Workflow Certification is obtained after a third-party proctored assessment process.

If you are an ML practitioner or a Data Scientist looking to gain more skills to enter the enterprise workforce, get started today! Join the community and get the 1st month complimentary on any of IBM's Data Science and Artificial Intelligence Specialization and Professional Certificate subscriptions on Coursera.

There are many courses available for aspiring data scientists.  But there are few offerings available for practicing data scientists to advance their skills.  At the same time, companies are struggling to successfully embed AI into their operations at the speed they need and with impactful business results.

Part of the struggle is that there is much work to do beyond simply building the most effective machine learning model. In order for AI projects to be successful a practitioner needs to engage in every step of the Enterprise AI Workflow.

The Enterprise AI Workflow is defined as the entire process of development, testing, training, deployment, and maintenance of machine learning models in large enterprises.  The workflow is summarized below:

Data Scientists are well trained in all matters of Probability, Statistics, Linear Algebra, Data Preparation and model building.  But without the practical business experience, it is hard to be successful in the enterprise. 

First, it is important to understand the business opportunity and identify the business metrics that need to be optimized. This isn't something that is typically taught in Data Science programs. Finding the data is the next challenge; data needed to solve a business problem isn't always available, it isn't nicely set up like the data sets used in academic environments. Once the data is ready and the model is built, the next challenge is the deployment. There are many considerations and requirements for a model to be deployed in the business. For example, the IT resource requirements to achieve the perfect model accuracy may be too high and decisions to find the ideal accuracy with the appropriate IT resource usage need to be made. Also, because data is a live entity, it morphs and behaves in ways we can't always predict. Monitoring performance and bias and deciding the parameters that kick of a retraining requirement are critical as well as measuring business success.

Today, the Data and Artificial Intelligence Education and Certification team at IBM announced the launch of the IBM Enterprise AI Workflow certification program aimed at helping companies and data scientists overcome these challenges 

The program consists of a 60 question certification test proctored by Pearson VUE and a 6 course (40 hours) specialization on Coursera  to help prepare ML practitioners and data scientists for the test.

The certification test aims to validate that a practitioner has all the skills required to implement an AI solution: it validates knowledge of the scientific and math foundation, how to apply Data Science and AI in business,  understanding and preparing data,  building models, evaluating models, relating model performance to business metrics, deploying models, and skills to do so using Python and some IBM products.

The IBM Enterprise Workflow specialization launched on Coursera uses a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution. It leverages Design Thinking as a framework to work through the translation of business goals into AI technical implementations. It brings together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality. It Leverages Python as the tool of choice for building AI models, and brings in IBM Watson Studio where it adds value to the process on top of Python and other open source tools.

It consists of 6 courses.

1. Business Priorities and Data Ingestion
2. Data analysis and hypothesis testing
3. Feature engineering and bias detection
4. Machine learning, visual recognition and NLP
5. Enterprise model deployment
6. AI in production

The IBM AI Workflow Certification program offers recognition valued by hiring managers because it is tied to real- world experience within a job role or with a specific product/solution; it provides recognition for the development of deep skills within a solution or product area and unlike any other Data Science skills programs which offer badges or certificates obtained after only going through an educational process, the AI Workflow Certification is obtained after a third-party proctored assessment process.

If you are an ML practitioner or a Data Scientist looking to gain more skills to enter the enterprise workforce, get started today! Join the community and get the 1st month complimentary on any of IBM's Data Science and Artificial Intelligence Specialization and Professional Certificate subscriptions on Coursera.

There are many courses available for aspiring data scientists.  But there are few offerings available for practicing data scientists to advance their skills.  At the same time, companies are struggling to successfully embed AI into their operations at the speed they need and with impactful business results.

Part of the struggle is that there is much work to do beyond simply building the most effective machine learning model. In order for AI projects to be successful a practitioner needs to engage in every step of the Enterprise AI Workflow.

The Enterprise AI Workflow is defined as the entire process of development, testing, training, deployment, and maintenance of machine learning models in large enterprises.  The workflow is summarized below:

Data Scientists are well trained in all matters of Probability, Statistics, Linear Algebra, Data Preparation and model building.  But without the practical business experience, it is hard to be successful in the enterprise. 

First, it is important to understand the business opportunity and identify the business metrics that need to be optimized. This isn't something that is typically taught in Data Science programs. Finding the data is the next challenge; data needed to solve a business problem isn't always available, it isn't nicely set up like the data sets used in academic environments. Once the data is ready and the model is built, the next challenge is the deployment. There are many considerations and requirements for a model to be deployed in the business. For example, the IT resource requirements to achieve the perfect model accuracy may be too high and decisions to find the ideal accuracy with the appropriate IT resource usage need to be made. Also, because data is a live entity, it morphs and behaves in ways we can't always predict. Monitoring performance and bias and deciding the parameters that kick of a retraining requirement are critical as well as measuring business success.

Today, the Data and Artificial Intelligence Education and Certification team at IBM announced the launch of the IBM Enterprise AI Workflow certification program aimed at helping companies and data scientists overcome these challenges 

The program consists of a 60 question certification test proctored by Pearson VUE and a 6 course (40 hours) specialization on Coursera  to help prepare ML practitioners and data scientists for the test.

The certification test aims to validate that a practitioner has all the skills required to implement an AI solution: it validates knowledge of the scientific and math foundation, how to apply Data Science and AI in business,  understanding and preparing data,  building models, evaluating models, relating model performance to business metrics, deploying models, and skills to do so using Python and some IBM products.

The IBM Enterprise Workflow specialization launched on Coursera uses a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution. It leverages Design Thinking as a framework to work through the translation of business goals into AI technical implementations. It brings together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality. It Leverages Python as the tool of choice for building AI models, and brings in IBM Watson Studio where it adds value to the process on top of Python and other open source tools.

It consists of 6 courses.

1. Business Priorities and Data Ingestion
2. Data analysis and hypothesis testing
3. Feature engineering and bias detection
4. Machine learning, visual recognition and NLP
5. Enterprise model deployment
6. AI in production

The IBM AI Workflow Certification program offers recognition valued by hiring managers because it is tied to real- world experience within a job role or with a specific product/solution; it provides recognition for the development of deep skills within a solution or product area and unlike any other Data Science skills programs which offer badges or certificates obtained after only going through an educational process, the AI Workflow Certification is obtained after a third-party proctored assessment process.

If you are an ML practitioner or a Data Scientist looking to gain more skills to enter the enterprise workforce, get started today! Join the community and get the 1st month complimentary on any of IBM's Data Science and Artificial Intelligence Specialization and Professional Certificate subscriptions on Coursera.

There are many courses available for aspiring data scientists.  But there are few offerings available for practicing data scientists to advance their skills.  At the same time, companies are struggling to successfully embed AI into their operations at the speed they need and with impactful business results.

Part of the struggle is that there is much work to do beyond simply building the most effective machine learning model. In order for AI projects to be successful a practitioner needs to engage in every step of the Enterprise AI Workflow.

The Enterprise AI Workflow is defined as the entire process of development, testing, training, deployment, and maintenance of machine learning models in large enterprises.  The workflow is summarized below:

Data Scientists are well trained in all matters of Probability, Statistics, Linear Algebra, Data Preparation and model building.  But without the practical business experience, it is hard to be successful in the enterprise. 

First, it is important to understand the business opportunity and identify the business metrics that need to be optimized. This isn't something that is typically taught in Data Science programs. Finding the data is the next challenge; data needed to solve a business problem isn't always available, it isn't nicely set up like the data sets used in academic environments. Once the data is ready and the model is built, the next challenge is the deployment. There are many considerations and requirements for a model to be deployed in the business. For example, the IT resource requirements to achieve the perfect model accuracy may be too high and decisions to find the ideal accuracy with the appropriate IT resource usage need to be made. Also, because data is a live entity, it morphs and behaves in ways we can't always predict. Monitoring performance and bias and deciding the parameters that kick of a retraining requirement are critical as well as measuring business success.

Today, the Data and Artificial Intelligence Education and Certification team at IBM announced the launch of the IBM Enterprise AI Workflow certification program aimed at helping companies and data scientists overcome these challenges 

The program consists of a 60 question certification test proctored by Pearson VUE and a 6 course (40 hours) specialization on Coursera  to help prepare ML practitioners and data scientists for the test.

The certification test aims to validate that a practitioner has all the skills required to implement an AI solution: it validates knowledge of the scientific and math foundation, how to apply Data Science and AI in business,  understanding and preparing data,  building models, evaluating models, relating model performance to business metrics, deploying models, and skills to do so using Python and some IBM products.

The IBM Enterprise Workflow specialization launched on Coursera uses a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution. It leverages Design Thinking as a framework to work through the translation of business goals into AI technical implementations. It brings together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality. It Leverages Python as the tool of choice for building AI models, and brings in IBM Watson Studio where it adds value to the process on top of Python and other open source tools.

It consists of 6 courses.

1. Business Priorities and Data Ingestion
2. Data analysis and hypothesis testing
3. Feature engineering and bias detection
4. Machine learning, visual recognition and NLP
5. Enterprise model deployment
6. AI in production

The IBM AI Workflow Certification program offers recognition valued by hiring managers because it is tied to real- world experience within a job role or with a specific product/solution; it provides recognition for the development of deep skills within a solution or product area and unlike any other Data Science skills programs which offer badges or certificates obtained after only going through an educational process, the AI Workflow Certification is obtained after a third-party proctored assessment process.

If you are an ML practitioner or a Data Scientist looking to gain more skills to enter the enterprise workforce, get started today! Join the community and get the 1st month complimentary on any of IBM's Data Science and Artificial Intelligence Specialization and Professional Certificate subscriptions on Coursera.

There are many courses available for aspiring data scientists.  But there are few offerings available for practicing data scientists to advance their skills.  At the same time, companies are struggling to successfully embed AI into their operations at the speed they need and with impactful business results.

Part of the struggle is that there is much work to do beyond simply building the most effective machine learning model. In order for AI projects to be successful a practitioner needs to engage in every step of the Enterprise AI Workflow.

The Enterprise AI Workflow is defined as the entire process of development, testing, training, deployment, and maintenance of machine learning models in large enterprises.  The workflow is summarized below:

Data Scientists are well trained in all matters of Probability, Statistics, Linear Algebra, Data Preparation and model building.  But without the practical business experience, it is hard to be successful in the enterprise. 

First, it is important to understand the business opportunity and identify the business metrics that need to be optimized. This isn't something that is typically taught in Data Science programs. Finding the data is the next challenge; data needed to solve a business problem isn't always available, it isn't nicely set up like the data sets used in academic environments. Once the data is ready and the model is built, the next challenge is the deployment. There are many considerations and requirements for a model to be deployed in the business. For example, the IT resource requirements to achieve the perfect model accuracy may be too high and decisions to find the ideal accuracy with the appropriate IT resource usage need to be made. Also, because data is a live entity, it morphs and behaves in ways we can't always predict. Monitoring performance and bias and deciding the parameters that kick of a retraining requirement are critical as well as measuring business success.

Today, the Data and Artificial Intelligence Education and Certification team at IBM announced the launch of the IBM Enterprise AI Workflow certification program aimed at helping companies and data scientists overcome these challenges 

The program consists of a 60 question certification test proctored by Pearson VUE and a 6 course (40 hours) specialization on Coursera  to help prepare ML practitioners and data scientists for the test.

The certification test aims to validate that a practitioner has all the skills required to implement an AI solution: it validates knowledge of the scientific and math foundation, how to apply Data Science and AI in business,  understanding and preparing data,  building models, evaluating models, relating model performance to business metrics, deploying models, and skills to do so using Python and some IBM products.

The IBM Enterprise Workflow specialization launched on Coursera uses a single use case/real world scenario as the foundation to work through what it takes to build an end to end AI solution. It leverages Design Thinking as a framework to work through the translation of business goals into AI technical implementations. It brings together different capabilities such as Machine Learning, Optimization, and specific narrow-AI functionality. It Leverages Python as the tool of choice for building AI models, and brings in IBM Watson Studio where it adds value to the process on top of Python and other open source tools.

It consists of 6 courses.

1. Business Priorities and Data Ingestion
2. Data analysis and hypothesis testing
3. Feature engineering and bias detection
4. Machine learning, visual recognition and NLP
5. Enterprise model deployment
6. AI in production

The IBM AI Workflow Certification program offers recognition valued by hiring managers because it is tied to real- world experience within a job role or with a specific product/solution; it provides recognition for the development of deep skills within a solution or product area and unlike any other Data Science skills programs which offer badges or certificates obtained after only going through an educational process, the AI Workflow Certification is obtained after a third-party proctored assessment process.

If you are an ML practitioner or a Data Scientist looking to gain more skills to enter the enterprise workforce, get started today! Join the community and get the 1st month complimentary on any of IBM's Data Science and Artificial Intelligence Specialization and Professional Certificate subscriptions on Coursera.